Uczenie maszynowe stanowi jeden z najbardziej fascynujących i dynamicznie rozwijających się obszarów technologii informatycznej. W księgarni internetowej helion.pl oferujemy szeroki zakres książek oraz kursów video, które pomogą Ci zgłębić tajniki tej dziedziny.
Uczenie maszynowe
Książki, ebooki, kursy video z kategorii: Uczenie maszynowe dostępne w księgarni Helion
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This book will help you successfully implement deep learning in Python to create smart web applications from scratch. You will learn how deep learning can transform a simple web app into a smart, business-friendly product. You will also develop neural networks using open-source libraries and also integrate them with different web stack front-ends.
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Hands-On Python Deep Learning for the Web. Integrating neural network architectures to build smart web apps with Flask, Django, and TensorFlow Hands-On Python Deep Learning for the Web. Integrating neural network architectures to build smart web apps with Flask, Django, and TensorFlow
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This book will help you learn how to build a scalable end-to-end machine learning pipeline in Azure from experimentation and training to optimization and deployment. By the end of this book, you will learn to build complex distributed systems and scalable cloud infrastructure using powerful machine learning algorithms to compute insights.
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Mastering Azure Machine Learning. Perform large-scale end-to-end advanced machine learning in the cloud with Microsoft Azure Machine Learning Mastering Azure Machine Learning. Perform large-scale end-to-end advanced machine learning in the cloud with Microsoft Azure Machine Learning
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Deep learning enables efficient and accurate learning from data. Developers working with R will be able to put their knowledge to work with this practical guide to deep learning. The book provides a hands-on approach to implementation and associated methodologies that will have you up-and-running, and productive in no time.
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Hands-On Deep Learning with R. A practical guide to designing, building, and improving neural network models using R Hands-On Deep Learning with R. A practical guide to designing, building, and improving neural network models using R
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This book is a step by step guide to one-shot learning using Python-based libraries. It is designed to help you understand and design models that can learn information about your data from one, or only a few, training examples. You will also learn to apply these techniques with real-world examples and datasets for classification and regression.
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Hands-On One-shot Learning with Python. Learn to implement fast and accurate deep learning models with fewer training samples using PyTorch Hands-On One-shot Learning with Python. Learn to implement fast and accurate deep learning models with fewer training samples using PyTorch
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Deep learning is rapidly becoming the most popular topic in the industry. This book introduces trending deep learning concepts and their use cases with an industrial and application-focused approach. You will cover a range of projects covering tasks such as mobile vision, facial recognition, smart AI assistant, augmented reality, and more.
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Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter. Build scalable real-world projects to implement end-to-end neural networks on Android and iOS Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter. Build scalable real-world projects to implement end-to-end neural networks on Android and iOS
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Promocja
Komputerowe systemy wizyjne znajdują liczne zastosowania w zakresie: rozpoznawania zdjęć lotniczych i satelitarnych terenu dla celów rejestracji zmian w kartografii i meteorologii, badań przesiewowych zdjęć rentgenowskich i innych w medycynie, analizy obrazów mikroskopowych dla wykrycia obecności pewnych faz, systemy bioidentyfikacji na po- ePub + Mobi pkt
Przetwarzanie i analiza obrazów w systemach przemysłowych. Wybrane zastosowania Przetwarzanie i analiza obrazów w systemach przemysłowych. Wybrane zastosowania
(47,20 zł najniższa cena z 30 dni)
47.20 zł
59.00 zł(-20%) -
Promocja
Książka przedstawia wciągający i przystępny przegląd przełomowych odkryć w obszarze sztucznej inteligencji i uczenia maszynowego, które uczyniły dzisiejsze maszyny tak inteligentnymi. Autor Sean Gerrish opisuje: architekturę oprogramowania, która pozwala samochodom autonomicznym pozostać na drodze i poruszać się po zatłoczonym środowisku miejskim- ePub + Mobi pkt
(67,20 zł najniższa cena z 30 dni)
67.20 zł
84.00 zł(-20%) -
W monografii przedstawiono opis fleksji i składni języka polskiego przystosowany do automatycznego przetwarzania tekstów. Opis ten stał się podstawą dwóch programów komputerowych: analizatora fleksyjnego Morfeusz i analizatora składniowego Świgra 2. Wymienione narzędzia posłużyły do budowy korpusu składniowego Składnica, wykorzystanego następnie d
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Promocja
Książka poświęcona jest problematyce tłumaczenia maszynowego i wspomaganego komputerowo. Prezentuje narzędzia ułatwiające lub wręcz umożliwiające pracę tłumaczowi tekstów specjalistycznych. Rozwój technologii umożliwił ekspansję słowników elektronicznych oraz programów wykonujących w mniej lub bardziej udany sposób proste zadania tłumaczeniowe. A- ePub + Mobi pkt
(39,20 zł najniższa cena z 30 dni)
39.20 zł
49.00 zł(-20%) -
This book will help you get through the problems that you face during the execution of different tasks and understand hacks in deep learning. With unique recipes, you will implement various deep learning architectures using R 3.5.x. You will cover complex algorithms to perform tasks such as reinforcement learning, GANs, advanced neural networks and
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Deep Learning with R Cookbook. Over 45 unique recipes to delve into neural network techniques using R 3.5.x Deep Learning with R Cookbook. Over 45 unique recipes to delve into neural network techniques using R 3.5.x
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The place of machine learning in art is growing at a rapid pace because of recent advancements in the field. Magenta is at the forefront of that innovation. This book provides a hands-on approach to machine learning models for music generation and demonstrates how to integrate them into an existing music production workflow. Complete with practical
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Hands-On Music Generation with Magenta. Explore the role of deep learning in music generation and assisted music composition Hands-On Music Generation with Magenta. Explore the role of deep learning in music generation and assisted music composition
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A new second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems, updated to include Python 3.8 and TensorFlow 2.x as well as the latest in new algorithms and techniques.
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Mastering Machine Learning Algorithms. Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work - Second Edition Mastering Machine Learning Algorithms. Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work - Second Edition
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Feature engineering is invaluable for developing and enriching your machine learning models. In this book, you will work with the best Python tools to streamline your feature engineering pipelines, feature engineering techniques and simplify and improve the quality of your code.
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Python Feature Engineering Cookbook. Over 70 recipes for creating, engineering, and transforming features to build machine learning models Python Feature Engineering Cookbook. Over 70 recipes for creating, engineering, and transforming features to build machine learning models
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Deep Learning with TensorFlow 2 and Keras, Second Edition teaches deep learning techniques alongside TensorFlow (TF) and Keras. The book introduces neural networks with TensorFlow, runs through the main applications, covers two working example apps, and then dives into TF and cloudin production, TF mobile, and using TensorFlow with AutoML.
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Deep Learning with TensorFlow 2 and Keras. Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API - Second Edition Deep Learning with TensorFlow 2 and Keras. Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API - Second Edition
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This book will help readers to apply deep learning algorithms in R using advanced examples. You will cover variants of neural network models such as ANN, CNN, RNN, LSTM, and more using expert techniques. Readers will make use of popular deep learning libraries such as Keras-R, Tensorflow-R, and more to implement AI models.
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Advanced Deep Learning with R. Become an expert at designing, building, and improving advanced neural network models using R Advanced Deep Learning with R. Become an expert at designing, building, and improving advanced neural network models using R
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This third edition is updated with TensorFlow 2 and the latest additions to scikit-learn. Packed with clear explanations, visualizations, and working examples, the book covers essential machine learning techniques in depth, along with two cutting-edge machine learning techniques: reinforcement learning and generative adversarial networks.
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Python Machine Learning. Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 - Third Edition Python Machine Learning. Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 - Third Edition
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This book is an expert-level guide to master the neural network variants using the Python ecosystem. You will gain the skills to build smarter, faster, and efficient deep learning systems with practical examples. By the end of this book, you will be up to date with the latest advances and current researches in the deep learning domain.
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Advanced Deep Learning with Python. Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch Advanced Deep Learning with Python. Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch
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Quantum computing may change everything we know about computing. Dancing with Qubits is a quantum computing textbook that explains how quantum computing works in detail, and discusses the implications it has for computer applications of the future.
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Dancing with Qubits. How quantum computing works and how it can change the world Dancing with Qubits. How quantum computing works and how it can change the world
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Hands-On Machine Learning with TensorFlow.js is a comprehensive guide that will help you easily get started with machine learning algorithms and techniques using TensorFlow.js. By the end of this book, you will be able to create and optimize your own web-based machine learning applications using practical examples.
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Hands-On Machine Learning with TensorFlow.js. A guide to building ML applications integrated with web technology using the TensorFlow.js library Hands-On Machine Learning with TensorFlow.js. A guide to building ML applications integrated with web technology using the TensorFlow.js library
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This book helps data scientists and cybersecurity experts on implementing the latest AI techniques in cybersecurity. Concrete and clear steps for implementing ML security systems are provided, saving you months in research and development. By the end of this book, you will be able to build defensive systems to curb cybersecurity threats.
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Machine Learning for Cybersecurity Cookbook. Over 80 recipes on how to implement machine learning algorithms for building security systems using Python Machine Learning for Cybersecurity Cookbook. Over 80 recipes on how to implement machine learning algorithms for building security systems using Python
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Deep Learning is a trending topic in AI currently, as it allows you to make faster and more accurate predictions using the power of neural networks. This book will teach you the process of neural network design, and show you how to develop efficient deep learning applications using Deeplearning4j through practical and easy to implement recipes.
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Java Deep Learning Cookbook. Train neural networks for classification, NLP, and reinforcement learning using Deeplearning4j Java Deep Learning Cookbook. Train neural networks for classification, NLP, and reinforcement learning using Deeplearning4j
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Promocja
Develop smart applications without spending days and weeks building machine-learning models. With this practical book, you’ll learn how to apply automated machine learning (AutoML), a process that uses machine learning to help people build machine learning models. Deepak Mukunthu, Parashar Shah, and Wee Hyong Tok provide a mix of technical de- ePub + Mobi pkt
Practical Automated Machine Learning on Azure. Using Azure Machine Learning to Quickly Build AI Solutions Practical Automated Machine Learning on Azure. Using Azure Machine Learning to Quickly Build AI Solutions
(186,15 zł najniższa cena z 30 dni)
186.15 zł
219.00 zł(-15%) -
Practical Machine Learning with R gives you the complete knowledge to solve your business problems - starting by forming a good problem statement, selecting the most appropriate model to solve your problem, and then ensuring that you do not overtrain the model.
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Practical Machine Learning with R. Define, build, and evaluate machine learning models for real-world applications Practical Machine Learning with R. Define, build, and evaluate machine learning models for real-world applications
Brindha Priyadarshini Jeyaraman, Ludvig Renbo Olsen, Monicah Wambugu
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The Go ecosystem comprises some really powerful Deep Learning tools. This book shows you how to use these tools to train and deploy scalable Deep Learning models. You will explore a number of modern Neural Network architectures such as CNNs, RNNs, and more. By the end, you will be able to train your own Deep Learning models from scratch, using the
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Hands-On Deep Learning with Go. A practical guide to building and implementing neural network models using Go Hands-On Deep Learning with Go. A practical guide to building and implementing neural network models using Go
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This book introduces basic-to-advanced deep learning algorithms used in a production environment by AI researchers and principal data scientists; it explains algorithms intuitively, including the underlying math, and shows how to implement them using popular Python-based deep learning libraries such as TensorFlow.
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Hands-On Deep Learning Algorithms with Python. Master deep learning algorithms with extensive math by implementing them using TensorFlow Hands-On Deep Learning Algorithms with Python. Master deep learning algorithms with extensive math by implementing them using TensorFlow
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Ensemble learning can provide the necessary methods to improve the accuracy and performance of existing models. In this book, you'll understand how to combine different machine learning algorithms to produce more accurate results from your models.
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Hands-On Ensemble Learning with Python. Build highly optimized ensemble machine learning models using scikit-learn and Keras Hands-On Ensemble Learning with Python. Build highly optimized ensemble machine learning models using scikit-learn and Keras
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This book will provide you an overview of Deep Learning techniques to facilitate the analytics and learning in various IoT apps. We will take you through each process - from data collection, analysis, modeling, statistics, and monitoring. We will make IoT data speak with a set of popular frameworks, like TensorFlow, TensorFlow Lite, and Chainer.
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Hands-On Deep Learning for IoT. Train neural network models to develop intelligent IoT applications Hands-On Deep Learning for IoT. Train neural network models to develop intelligent IoT applications
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Starting with the basics, this book teaches you how to choose from the various text pre-processing techniques and select the best model from the several neural network architectures for NLP issues.
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Deep Learning for Natural Language Processing. Solve your natural language processing problems with smart deep neural networks Deep Learning for Natural Language Processing. Solve your natural language processing problems with smart deep neural networks
Karthiek Reddy Bokka, Shubhangi Hora, Tanuj Jain, Monicah Wambugu
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Machine Learning for Finance explores new advances in machine learning and shows how they can be applied in the financial sector. It explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself.
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Machine Learning for Finance. Principles and practice for financial insiders Machine Learning for Finance. Principles and practice for financial insiders
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A supervised learning task infers a function from flagged training data and maps an input to an output based on sample input-output pairs. In this book, you will learn various machine learning techniques (such as linear and logistic regression) and gain the practical knowledge you need to quickly and powerfully apply algorithms to new problems.
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Supervised Machine Learning with Python. Develop rich Python coding practices while exploring supervised machine learning Supervised Machine Learning with Python. Develop rich Python coding practices while exploring supervised machine learning
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Promocja
Rewolucja wywołana przez rozwój głębokiego uczenia przyniosła nam samochody jeżdżące bez kierowców, ulepszoną usługę Tłumacza Google, swobodne rozmowy z Siri i Aleksą oraz ogromne zyski z automatycznego tradingu na nowojorskiej giełdzie papierów wartościowych. Głębokie sieci potrafią grać w pokera lepiej niż zawodowi gracze i wygrywają z mistrzem- ePub + Mobi pkt
Deep learning Głęboka rewolucja. Kiedy sztuczna inteligencja spotyka się z ludzką Deep learning Głęboka rewolucja. Kiedy sztuczna inteligencja spotyka się z ludzką
(39,92 zł najniższa cena z 30 dni)
39.92 zł
49.90 zł(-20%) -
This Learning Path is your step-by-step guide to building deep learning models using R's wide range of deep learning libraries and frameworks. Through multiple real-world projects and expert guidance and tips, you'll gain the exact knowledge you need to get started with developing deep models using R.
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Deep Learning with R for Beginners. Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet Deep Learning with R for Beginners. Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet
Mark Hodnett, Joshua F. Wiley, Yuxi (Hayden) Liu, Pablo Maldonado
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This book will help you master your skills in various artificial intelligence and machine learning services available on AWS. Through practical hands-on examples, you’ll learn how to use these services to generate impressive results. You will have a tremendous understanding of how to use a wide range of AWS services in your own organization.
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Mastering Machine Learning on AWS. Advanced machine learning in Python using SageMaker, Apache Spark, and TensorFlow Mastering Machine Learning on AWS. Advanced machine learning in Python using SageMaker, Apache Spark, and TensorFlow
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This Learning Path is a comprehensive guide that gives you complete knowledge on how to implement the powerful supervised, unsupervised, and reinforcement learning techniques using R 3.5 in your data science projects. With this Learning Path, you'll gain insights from complex projects and learn advanced methods to build smart applications.
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Advanced Machine Learning with R. Tackle data analytics and machine learning challenges and build complex applications with R 3.5 Advanced Machine Learning with R. Tackle data analytics and machine learning challenges and build complex applications with R 3.5
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PyTorch Deep Learning Hands-On is a book for engineers who want a fast-paced guide to doing deep learning work with Pytorch. It is not an academic textbook and does not try to teach deep learning principles. The book will help you most if you want to get your hands dirty and put PyTorch to work quickly.
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PyTorch Deep Learning Hands-On. Build CNNs, RNNs, GANs, reinforcement learning, and more, quickly and easily PyTorch Deep Learning Hands-On. Build CNNs, RNNs, GANs, reinforcement learning, and more, quickly and easily
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Machine learning has become a core necessity for every business and organization. With this book, you will learn to analyze your Excel data to search for patterns and return a series of interesting facts or trends about the data. You will learn to perform machine learning tasks using Excel plugins and APIs without much code required.
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Hands-On Machine Learning with Microsoft Excel 2019. Build complete data analysis flows, from data collection to visualization Hands-On Machine Learning with Microsoft Excel 2019. Build complete data analysis flows, from data collection to visualization
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This book explains the essential learning algorithms used for deep and shallow architectures. Packed with practical implementations to help you understand the concepts and ideas required to build efficient artificial intelligence systems, this book will help you construct deep models using popular frameworks and datasets.
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Hands-On Deep Learning Architectures with Python. Create deep neural networks to solve computational problems using TensorFlow and Keras Hands-On Deep Learning Architectures with Python. Create deep neural networks to solve computational problems using TensorFlow and Keras
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Most data mining opportunities involve machine learning and often come with greater financial rewards. This book will help you bring the power of machine learning techniques into your data mining work. By the end of the book, you will be able to create accurate predictive models for data mining.
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Machine Learning for Data Mining. Improve your data mining capabilities with advanced predictive modeling Machine Learning for Data Mining. Improve your data mining capabilities with advanced predictive modeling
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Scala as a programming language is a highly scalable integration of object-oriented and functional programming, which makes it easy to build scalable and complex big data applications. This book is a handy guide for machine learning developers and data scientists who want to train effective machine learning models using this popular language.
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Machine Learning with Scala Quick Start Guide. Leverage popular machine learning algorithms and techniques and implement them in Scala Machine Learning with Scala Quick Start Guide. Leverage popular machine learning algorithms and techniques and implement them in Scala
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Applied Deep Learning with Keras takes you from a basic knowledge of machine learning and Python to an expert understanding of applying Keras to develop efficient deep learning solutions. This book teaches you new techniques to handle neural networks, and in turn, broadens your options as a data scientist.
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Applied Deep Learning with Keras. Solve complex real-life problems with the simplicity of Keras Applied Deep Learning with Keras. Solve complex real-life problems with the simplicity of Keras
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Brett Lantz teaches you how to uncover key insights and make new predictions with this hands-on, practical guide to machine learning with R. This third edition is for experienced R users and beginners. The book is fully updated to R 3.6, featuring newer and better libraries, advice on ethical and bias issues, and an introduction to deep learning.
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Machine Learning with R. Expert techniques for predictive modeling - Third Edition Machine Learning with R. Expert techniques for predictive modeling - Third Edition
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This book is an essential guide for anyone interested in Reinforcement Learning. The book provides an actionable reference for Reinforcement Learning algorithms and their applications using TensorFlow and Python. It will help readers leverage the power of algorithms such as Deep Q-Network (DQN), Deep Deterministic Policy Gradients (DDPG), and Proxi
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TensorFlow Reinforcement Learning Quick Start Guide. Get up and running with training and deploying intelligent, self-learning agents using Python TensorFlow Reinforcement Learning Quick Start Guide. Get up and running with training and deploying intelligent, self-learning agents using Python
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This book will intuitively build on the fundamentals of neural networks, deep learning and thoughtfully guide the readers through real-world use cases. You will learn to implement neural networks as well as how to develop and embed intelligence in products and services using the latest open source and industry level tools available in the market.
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Hands-On Neural Networks with Keras. Design and create neural networks using deep learning and artificial intelligence principles Hands-On Neural Networks with Keras. Design and create neural networks using deep learning and artificial intelligence principles
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With this book, you will learn how to perform various machine learning tasks in different environments. You’ll use a wide variety of machine learning algorithms using Python to solve real-world problems. By the end of the book, you will learn to implement most used machine learning algorithms using complex datasets and optimized techniques.
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Python Machine Learning Cookbook. Over 100 recipes to progress from smart data analytics to deep learning using real-world datasets - Second Edition Python Machine Learning Cookbook. Over 100 recipes to progress from smart data analytics to deep learning using real-world datasets - Second Edition
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A practical guide on Machine learning with IBM cloud to act as a solid yet concise reference for the readers. You will learn about the role of data representation and feature extraction in machine learning. This book will help you learn how to use the IBM Cloud and Watson Machine learning service to develop real-world machine learning solutions.
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Hands-On Machine Learning with IBM Watson. Leverage IBM Watson to implement machine learning techniques and algorithms using Python Hands-On Machine Learning with IBM Watson. Leverage IBM Watson to implement machine learning techniques and algorithms using Python
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TensorFlow is one of the most popular machine learning frameworks in Python. With this book, you will improve your knowledge of some of the latest TensorFlow features and will be able to perform supervised and unsupervised machine learning and also train neural networks.
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TensorFlow 2.0 Quick Start Guide. Get up to speed with the newly introduced features of TensorFlow 2.0 TensorFlow 2.0 Quick Start Guide. Get up to speed with the newly introduced features of TensorFlow 2.0
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Mastering OpenCV 4 with Python is a comprehensive guide to help you to get acquainted with various computer vision algorithms running in real-time. This book will help you to build complete projects on image processing, motion detection, and image segmentation where you can gain advanced computer vision techniques.
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Mastering OpenCV 4 with Python. A practical guide covering topics from image processing, augmented reality to deep learning with OpenCV 4 and Python 3.7 Mastering OpenCV 4 with Python. A practical guide covering topics from image processing, augmented reality to deep learning with OpenCV 4 and Python 3.7
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This book is ideal for people wanting to get up-and-running with the core concepts of machine learning using R 3.5. This book follows a step-by-step approach to implementing an end-to-end pipeline, addressing data collection and processing, various types of data analysis, and machine learning use cases.
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Machine Learning with R Quick Start Guide. A beginner's guide to implementing machine learning techniques from scratch using R 3.5 Machine Learning with R Quick Start Guide. A beginner's guide to implementing machine learning techniques from scratch using R 3.5
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Cognitive Toolkit is one of the most popular and recently open sourced deep learning toolkit by Microsoft. Cognitive Toolkit is used to train fast and effective deep learning models. This book will be a quick introduction to using Cognitive Toolkit and will teach you how to train and validate different types of neural networks.
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Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide. A practical guide to building neural networks using Microsoft's open source deep learning framework Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide. A practical guide to building neural networks using Microsoft's open source deep learning framework
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Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and all features of R that enable you to understand your data better and get answers to all your business questions.
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Applied Unsupervised Learning with R. Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA Applied Unsupervised Learning with R. Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA
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This book contains practical implementations of several deep learning projects in multiple domains, including in regression-based tasks such as taxi fare prediction in New York City, image classification of cats and dogs using a convolutional neural network, implementing a facial recognition security system using Siamese Neural Networks, and more.
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Neural Network Projects with Python. The ultimate guide to using Python to explore the true power of neural networks through six projects Neural Network Projects with Python. The ultimate guide to using Python to explore the true power of neural networks through six projects
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Python Machine Learning by Example covers in detail the most important concepts, techniques, algorithms, and libraries that every data scientist or machine learning practitioner needs to know. This example-enriched guide will make your learning journey easier and happier, enabling you to solve real-world data-driven problems.
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Python Machine Learning By Example. Implement machine learning algorithms and techniques to build intelligent systems - Second Edition Python Machine Learning By Example. Implement machine learning algorithms and techniques to build intelligent systems - Second Edition
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This book presents solutions to the majority of the challenges you will face while training neural networks to solve deep learning problems. It covers the trending deep learning architectures used in industry and tackles a variety of use cases in computer vision, text processing, audio analysis, recommender systems, and game bots
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Neural Networks with Keras Cookbook. Over 70 recipes leveraging deep learning techniques across image, text, audio, and game bots Neural Networks with Keras Cookbook. Over 70 recipes leveraging deep learning techniques across image, text, audio, and game bots
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Unsupervised learning is a key required block in both machine learning and deep learning domains. You will explore how to make your models learn, grow, change, and develop by themselves whenever they are exposed to a new set of data. With this book, you will learn the art of unsupervised learning for different real-world challenges.
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Hands-On Unsupervised Learning with Python. Implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more Hands-On Unsupervised Learning with Python. Implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more
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This book will take you through the process of efficiently training deep neural networks in Java for Computer Vision-related tasks. You will build real-world applications ranging from simple Java handwritten digit recognition models to real-time autonomous car driving systems and face recognition models using the popular Java-based libraries.
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Hands-On Java Deep Learning for Computer Vision. Implement machine learning and neural network methodologies to perform computer vision-related tasks Hands-On Java Deep Learning for Computer Vision. Implement machine learning and neural network methodologies to perform computer vision-related tasks
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This book uses a recipe-based approach to showcase the power of machine learning algorithms to build ensemble models using Python libraries. Through this book, you will be able to pick up the code, understand in depth how it works, execute and implement it efficiently. This will be a desk reference to implement a wide range of tasks and solve the c
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Ensemble Machine Learning Cookbook. Over 35 practical recipes to explore ensemble machine learning techniques using Python Ensemble Machine Learning Cookbook. Over 35 practical recipes to explore ensemble machine learning techniques using Python
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Elastic has announced the integration of Prelert machine learning technology within its ecosystem allowing real-time generation of business insights from the Elasticsearch data without it leaving the cluster at all. This book will demonstrate these unique features and teach you to perform machine learning on the Elastic Stack without any hassle.
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Machine Learning with the Elastic Stack. Expert techniques to integrate machine learning with distributed search and analytics Machine Learning with the Elastic Stack. Expert techniques to integrate machine learning with distributed search and analytics
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Machine learning is a field of AI where we build systems that learn from data. This book explains complicated concepts with real-world applications. It demonstrates the power of R and machine learning extensively while highlighting the constraints. Finally, it will walk you through topics such as text analysis, time series, and deep learning.
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Mastering Machine Learning with R. Advanced machine learning techniques for building smart applications with R 3.5 - Third Edition Mastering Machine Learning with R. Advanced machine learning techniques for building smart applications with R 3.5 - Third Edition
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In this book, we will use different complexities of datasets in order to build end-to-end projects. With every chapter, the level of complexity and operations will become advanced. It consists of 8 full-fledged projects covering approaches such as 3D-GAN, Age-cGAN, DCGAN, SRGAN, StackGAN, and CycleGAN with real-world use cases.
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Generative Adversarial Networks Projects. Build next-generation generative models using TensorFlow and Keras Generative Adversarial Networks Projects. Build next-generation generative models using TensorFlow and Keras
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Machine learning involves development and training of models used to predict future outcomes. This book is a practical guide to all the tips and tricks related to machine learning. It includes hands-on, easy to access techniques on topics like model selection, performance tuning, training neural networks, time series analysis and a lot more.
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Machine Learning Quick Reference. Quick and essential machine learning hacks for training smart data models Machine Learning Quick Reference. Quick and essential machine learning hacks for training smart data models
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Machine Learning (ML) is transforming the way we understand and interact with the world around us. This book is a perfect guide for you to use the Python ecosystem to cover key domains in the machine learning. You will learn to implement advanced concepts and most used machine learning algorithms using complex datasets and optimized techniques.
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Python Machine Learning Blueprints. Put your machine learning concepts to the test by developing real-world smart projects - Second Edition Python Machine Learning Blueprints. Put your machine learning concepts to the test by developing real-world smart projects - Second Edition
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This book includes 9 projects on building smart and practical AI-based systems. These projects cover solutions to different domain-specific problems in healthcare, e-commerce and more. With this book, you will apply different machine learning and deep learning techniques and learn how to build your own intelligent applications for smart predictions
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Intelligent Projects Using Python. 9 real-world AI projects leveraging machine learning and deep learning with TensorFlow and Keras Intelligent Projects Using Python. 9 real-world AI projects leveraging machine learning and deep learning with TensorFlow and Keras
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The book will help you learn deep neural networks and their applications in computer vision, generative models, and natural language processing. It will also introduce you to the area of reinforcement learning, where you’ll learn the state-of-the-art algorithms to teach the machines how to play games like Go and Atari.
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Python Deep Learning. Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow - Second Edition Python Deep Learning. Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow - Second Edition
Ivan Vasilev, Daniel Slater, Gianmario Spacagna, Peter Roelants, Valentino Zocca
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The purpose of the book is to help a machine learning practitioner gets hands-on experience in working with real-world data and apply modern machine learning algorithms. You will learn to implement each algorithm to a specific industry problem. It covers projects involving both supervised as well as unsupervised learning approaches.
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R Machine Learning Projects. Implement supervised, unsupervised, and reinforcement learning techniques using R 3.5 R Machine Learning Projects. Implement supervised, unsupervised, and reinforcement learning techniques using R 3.5
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Generative Adversarial Networks have opened up many new possibilities in the machine learning domain. This book is all you need to implement different types of GANs using TensorFlow and Keras, in order to provide optimized and efficient deep learning solutions.
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Generative Adversarial Networks Cookbook. Over 100 recipes to build generative models using Python, TensorFlow, and Keras Generative Adversarial Networks Cookbook. Over 100 recipes to build generative models using Python, TensorFlow, and Keras
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With the help of this book, you'll build smart algorithmic models using machine learning algorithms covering tasks such as time series forecasting, backtesting, trade predictions, and more using easy-to-follow examples. By the end, you'll be able to adopt algorithmic trading in your own business and implement intelligent investigative strategies.
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Hands-On Machine Learning for Algorithmic Trading. Design and implement investment strategies based on smart algorithms that learn from data using Python Hands-On Machine Learning for Algorithmic Trading. Design and implement investment strategies based on smart algorithms that learn from data using Python
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The book will allow readers to implement smart solutions to their existing cybersecurity products and effectively build intelligent solutions which cater to the needs of the future. By the end of this book, you will be able to build, apply, and evaluate machine learning algorithms to identify various cybersecurity potential threats.
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Hands-On Machine Learning for Cybersecurity. Safeguard your system by making your machines intelligent using the Python ecosystem Hands-On Machine Learning for Cybersecurity. Safeguard your system by making your machines intelligent using the Python ecosystem
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This hands-on guide for meta learning starts with exploring the principles, algorithms, and implementations of Meta learning with Tensorflow, Keras, and Python. Once it sets the foundation of learning to learn, the book will help you implement your meta learning algorithms from scratch.
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Hands-On Meta Learning with Python. Meta learning using one-shot learning, MAML, Reptile, and Meta-SGD with TensorFlow Hands-On Meta Learning with Python. Meta learning using one-shot learning, MAML, Reptile, and Meta-SGD with TensorFlow
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Keras is a deep learning library that enables the fast, efficient training of deep learning models. The book begins with setting up the environment, training various types of models in the domain of deep learning and reinforcement learning. The projects are exciting and are real-world market demanding projects which take you from simple to complex
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Keras 2.x Projects. 9 projects demonstrating faster experimentation of neural network and deep learning applications using Keras Keras 2.x Projects. 9 projects demonstrating faster experimentation of neural network and deep learning applications using Keras
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This book demonstrates techniques to leverage the power of Python, OpenCV, and TensorFlow to solve problems in Computer Vision. This book also shows you how to build an application that can estimate human poses within images. You will also classify images and identify humans in videos, and then develop your own handwritten digit classifier.
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Computer Vision Projects with OpenCV and Python 3. Six end-to-end projects built using machine learning with OpenCV, Python, and TensorFlow Computer Vision Projects with OpenCV and Python 3. Six end-to-end projects built using machine learning with OpenCV, Python, and TensorFlow
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PyTorch is extremely powerful and yet easy to learn. It provides advanced features such as supporting multiprocessor, distributed and parallel computation. This book is an excellent entry point for those wanting to explore deep learning with PyTorch to harness its power.
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Deep Learning with PyTorch Quick Start Guide. Learn to train and deploy neural network models in Python Deep Learning with PyTorch Quick Start Guide. Learn to train and deploy neural network models in Python
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This Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden
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Python: Advanced Guide to Artificial Intelligence. Expert machine learning systems and intelligent agents using Python Python: Advanced Guide to Artificial Intelligence. Expert machine learning systems and intelligent agents using Python
Giuseppe Bonaccorso, Armando Fandango, Rajalingappaa Shanmugamani
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Artificial Intelligence and Machine Learning Fundamentals teaches you machine learning and neural networks from the ground up using real-world examples. After you complete this book, you will be excited to revamp your current projects or build new intelligent networks.
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Artificial Intelligence and Machine Learning Fundamentals. Develop real-world applications powered by the latest AI advances Artificial Intelligence and Machine Learning Fundamentals. Develop real-world applications powered by the latest AI advances
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This book will help you to comprehensively understand the various facets and factors involved in progressing your career in Site Reliability Engineering (SRE). Enterprise and cloud IT teams and software engineers will learn to adopt the SRE concepts and culture in a simplified and streamlined fashion.
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Practical Site Reliability Engineering. Automate the process of designing, developing, and delivering highly reliable apps and services with SRE Practical Site Reliability Engineering. Automate the process of designing, developing, and delivering highly reliable apps and services with SRE
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Hands-On Data Science with R explore various popular R packages to perform various data science tasks, including core statistical concepts and a wide array of use cases. This practical book covers the entire data science ecosystem for aspiring data scientists, including machine learning, NLP, and neural networks
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Hands-On Data Science with R. Techniques to perform data manipulation and mining to build smart analytical models using R Hands-On Data Science with R. Techniques to perform data manipulation and mining to build smart analytical models using R
Vitor Bianchi Lanzetta, Nataraj Dasgupta, Ricardo Anjoleto Farias
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This book will show you how to take advantage of TensorFlow’s most appealing features - simplicity, efficiency, and flexibility - in various scenarios. You will gain cutting-edge insights into using TensorFlow’s offerings for your problems and learn practical hacks to successfully implement real-world machine learning projects.
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TensorFlow Machine Learning Projects. Build 13 real-world projects with advanced numerical computations using the Python ecosystem TensorFlow Machine Learning Projects. Build 13 real-world projects with advanced numerical computations using the Python ecosystem
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Developers struggle to find an easy to follow learning resource for implementing Recurrent Neural Network(RNN) models. RNNs are the state-of-the-art model in deep learning for dealing with sequential data. From language translation to generating captions for an image, RNNs are used to continuously improve the results. This book will teach you the f
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Recurrent Neural Networks with Python Quick Start Guide. Sequential learning and language modeling with TensorFlow Recurrent Neural Networks with Python Quick Start Guide. Sequential learning and language modeling with TensorFlow
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Go is a highly preferred language for machine learning. The code is close to how it’s actually executed in the machine. Over the course of this book, you will learn how to express complex ideas found in machine learning literature and implement them. You will also learn how to structure problems to solve them using machine learning with Go.
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Go Machine Learning Projects. Eight projects demonstrating end-to-end machine learning and predictive analytics applications in Go Go Machine Learning Projects. Eight projects demonstrating end-to-end machine learning and predictive analytics applications in Go
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Machine Learning in Java will provide you with the techniques and tools you need to quickly gain insight from complex data. You will start by learning how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering.
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Machine Learning in Java. Helpful techniques to design, build, and deploy powerful machine learning applications in Java - Second Edition Machine Learning in Java. Helpful techniques to design, build, and deploy powerful machine learning applications in Java - Second Edition
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This book will empower you to apply Artificial Intelligence techniques to design applications for natural language processing, robotics, and other real-world use-cases. You will be able to develop, debug, deploy and optimize intelligent AI systems for self-driving cars, game playing, and much more.
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Hands-On Artificial Intelligence for Beginners. An introduction to AI concepts, algorithms, and their implementation Hands-On Artificial Intelligence for Beginners. An introduction to AI concepts, algorithms, and their implementation
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This book gives you a practical, hands-on understanding of how you can leverage the power of Python and Keras to perform effective deep learning. It presents a unique problem-solution approach to tackle various problems in training different types of neural networks while taking care of the speed and accuracy of these models
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Keras Deep Learning Cookbook. Over 30 recipes for implementing deep neural networks in Python Keras Deep Learning Cookbook. Over 30 recipes for implementing deep neural networks in Python
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Choosing the right algorithm is often a key differentiator in the success or failure of a data model and its optimal performance. This book introduces you to 7 key machine learning algorithms which you can easily grasp within a week and includes exercises that will help you learn different aspects of machine learning without any hassle.
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Data Science Algorithms in a Week. Top 7 algorithms for scientific computing, data analysis, and machine learning - Second Edition Data Science Algorithms in a Week. Top 7 algorithms for scientific computing, data analysis, and machine learning - Second Edition
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This book will teach you how advanced machine learning can be performed in the cloud in a very cheap way. You will learn more about Azure ML processes as an enterprise-ready methodology. By the end of this book, you will implement machine learning and artificial intelligence concepts in your model to solve real-world problems.
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Hands-On Machine Learning with Azure. Build powerful models with cognitive machine learning and artificial intelligence Hands-On Machine Learning with Azure. Build powerful models with cognitive machine learning and artificial intelligence
Thomas K Abraham, Parashar Shah, Jen Stirrup, Lauri Lehman, Anindita Basak
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Python Deep Learning Projects book will simplify and ease how deep learning works, and demonstrate how neural networks play a vital role in exploring predictive analytics across different domains. You will explore projects in the field of computational linguistics, computer vision, machine translation, pattern recognition and many more
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Python Deep Learning Projects. 9 projects demystifying neural network and deep learning models for building intelligent systems Python Deep Learning Projects. 9 projects demystifying neural network and deep learning models for building intelligent systems
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This book covers advanced deep learning techniques to create successful AI. Using MLPs, CNNs, and RNNs as building blocks to more advanced techniques, you’ll study deep neural network architectures, Autoencoders, Generative Adversarial Networks (GANs), Variational AutoEncoders (VAEs), and Deep Reinforcement Learning (DRL) critical to many cu
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Advanced Deep Learning with Keras. Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more Advanced Deep Learning with Keras. Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more
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Machine learning on mobile devices is the next big thing. This book presents the implementation of 7 practical, real-world projects that will teach you how to leverage TensorFlow Lite and Core ML to perform efficient machine learning on a cross-platform mobile OS. You will get to work on image, text, and video datasets through these projects.
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Machine Learning Projects for Mobile Applications. Build Android and iOS applications using TensorFlow Lite and Core ML Machine Learning Projects for Mobile Applications. Build Android and iOS applications using TensorFlow Lite and Core ML
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Machine Learning in the healthcare domain is booming because of its abilities to provide accurate and stabilized techniques. This book is packed with new methodologies to create efficient solutions for healthcare analytics. We will build five end-to-end projects to evaluate the efficiency of AI apps to carry out simple-to-complex healthcare analyti
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Machine Learning for Healthcare Analytics Projects. Build smart AI applications using neural network methodologies across the healthcare vertical market Machine Learning for Healthcare Analytics Projects. Build smart AI applications using neural network methodologies across the healthcare vertical market
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Scikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize and evaluate all the important machine learning algorithms that scikit-learn provides.
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Machine Learning with scikit-learn Quick Start Guide. Classification, regression, and clustering techniques in Python Machine Learning with scikit-learn Quick Start Guide. Classification, regression, and clustering techniques in Python
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IBM Watson Projects demonstrate projects focused on cognitive computing & analytical capabilities of IBM Watson. You will explore designing smart business solutions and understand the effectiveness of a supply chain using projects including healthcare dialog system, sentimental analysis on social media, personalized recommendation systems and more.
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IBM Watson Projects. Eight exciting projects that put artificial intelligence into practice for optimal business performance IBM Watson Projects. Eight exciting projects that put artificial intelligence into practice for optimal business performance
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CompTIA Security+ Certification Guide makes the most complex Security+ concepts easy to understand despite having no prior knowledge. It offers exam tips in every chapter along with access to practical exercises and exam checklist that map to the exam objectives and it is the perfect study guide to help you pass CompTIA Security+ SY0-501 exam.
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CompTIA Security+ Certification Guide. Master IT security essentials and exam topics for CompTIA Security+ SY0-501 certification CompTIA Security+ Certification Guide. Master IT security essentials and exam topics for CompTIA Security+ SY0-501 certification
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This book will give you a complete walkthrough of the process of developing basic to advanced practical examples based on neural networks with C#. From simple perceptrons through GRU’s, you’ll learn to understand and implement these technologies using the concepts you’ve learned. Explore methods to optimize and adapt neural net
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Hands-On Neural Network Programming with C#. Add powerful neural network capabilities to your C# enterprise applications Hands-On Neural Network Programming with C#. Add powerful neural network capabilities to your C# enterprise applications
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Python Reinforcement Learning Projects brings various aspects and methodologies of RL using 8 real-world projects that explore RL and will have hands-on experience with real data and artificial intelligence problems. You will learn to build self-learning models using sophisticated techniques like Q-learning, Markov models and Monte-Carlo process.
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Python Reinforcement Learning Projects. Eight hands-on projects exploring reinforcement learning algorithms using TensorFlow Python Reinforcement Learning Projects. Eight hands-on projects exploring reinforcement learning algorithms using TensorFlow
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Keras Reinforcement Learning Projects book teaches you essential concept, techniques and, models of reinforcement learning using best real-world demonstrations. You will explore popular algorithms such as Markov decision process, Monte Carlo, Q-learning making you equipped with complex statistics in various projects with the help of Keras
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Keras Reinforcement Learning Projects. 9 projects exploring popular reinforcement learning techniques to build self-learning agents Keras Reinforcement Learning Projects. 9 projects exploring popular reinforcement learning techniques to build self-learning agents
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When data is presented to you in a graphical or pictorial format, you can analyze it more effectively. This book begins by introducing you to basic concepts, such as grammar of graphics and geometric objects. It then goes on to explain these concepts in detail with examples. Once you are comfortable with basics, you can learn all about the advanced
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Applied Data Visualization with R and ggplot2. Create useful, elaborate, and visually appealing plots Applied Data Visualization with R and ggplot2. Create useful, elaborate, and visually appealing plots
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Mastering Arduino is a practical, no-nonsense guide teaching you the electronics and programming skills that you need to create advanced Arduino projects. It is packed full of real-world projects leading up to the final project where you will build and control a basic robot.
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Mastering Arduino. A project-based approach to electronics, circuits, and programming Mastering Arduino. A project-based approach to electronics, circuits, and programming
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It's important to know project management best practices that are tailored to your enterprise environments. From project kick-off to formal project/phase closure, you’ll learn the skills needed to see a project through to completion. This book will help you to become certified and understand the project management best practices.
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CompTIA Project+ Certification Guide. Learn project management best practices and successfully pass the CompTIA Project+ PK0-004 exam CompTIA Project+ Certification Guide. Learn project management best practices and successfully pass the CompTIA Project+ PK0-004 exam
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This book will help you become familiar with HMMs and different inference algorithms by working on real-world problems. You will start with an introduction to the basic concepts of Markov chains, Markov processes and then delve deeper into understanding hidden Markov models and its types using practical examples.
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Hands-On Markov Models with Python. Implement probabilistic models for learning complex data sequences using the Python ecosystem Hands-On Markov Models with Python. Implement probabilistic models for learning complex data sequences using the Python ecosystem
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This book will introduce you to the APIs released as part of Cognitive Services platform and show you how to leverage their capabilities. More importantly, you'll see how the power of these APIs can be combined to build real-world apps that have cognitive capabilities.
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Learning Microsoft Cognitive Services. Use Cognitive Services APIs to add AI capabilities to your applications - Third Edition Learning Microsoft Cognitive Services. Use Cognitive Services APIs to add AI capabilities to your applications - Third Edition
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Data analysis is crucial to accurately predict the performance of an application. The book begins by getting you started with R, including basic programming and data import, data visualization, pivoting, merging, aggregating, and joins. Once you are comfortable with the basics, you can read ahead and learn all about data visualization and graphics.
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R Programming Fundamentals. Deal with data using various modeling techniques R Programming Fundamentals. Deal with data using various modeling techniques
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This book will introduce the AI algorithms to the beginners and will take on implementing AI tasks using various Java-based libraries. It will take a practical approach to get you up and running with building smarter applications using Java programming knowledge.
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Hands-On Artificial Intelligence with Java for Beginners. Build intelligent apps using machine learning and deep learning with Deeplearning4j Hands-On Artificial Intelligence with Java for Beginners. Build intelligent apps using machine learning and deep learning with Deeplearning4j
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The purpose of this book is two-fold, we focus on detailed coverage of deep learning and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is on real-world examples and research problems using TensorFlow, Keras and Python ecosystem with hands-on examples.
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Hands-On Transfer Learning with Python. Implement advanced deep learning and neural network models using TensorFlow and Keras Hands-On Transfer Learning with Python. Implement advanced deep learning and neural network models using TensorFlow and Keras
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This book will help you overcome any problem you might come across while training and deploying machine learning models using the recently released Tensorflow. This book includes recipes on important machine learning concepts such as supervised and unsupervised learning, as well as neural networks and their real-world applications.
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TensorFlow Machine Learning Cookbook. Over 60 recipes to build intelligent machine learning systems with the power of Python - Second Edition TensorFlow Machine Learning Cookbook. Over 60 recipes to build intelligent machine learning systems with the power of Python - Second Edition
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Artificial Intelligence is arguably the most exciting and disruptive field of robotics which makes a man-made machine intelligent. This book will fulfill this vision by introducing various machine learning concepts to make your robot efficient. It will also equip your robot to make decisions, learn, and adapt based on its environment.
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Artificial Intelligence for Robotics. Build intelligent robots that perform human tasks using AI techniques Artificial Intelligence for Robotics. Build intelligent robots that perform human tasks using AI techniques
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In this book, you will understand what artificial intelligence is. It explains in detail basic search methods: Depth-First Search (DFS), Breadth-First Search (BFS), and A* Search, which can be used to make intelligent decisions when the initial state, end state, and possible actions are known. Random solutions or greedy solutions can be found for s
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Hands-On Artificial Intelligence for Search. Building intelligent applications and perform enterprise searches Hands-On Artificial Intelligence for Search. Building intelligent applications and perform enterprise searches
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Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. This book will act as an entry point for anyone who wants to make a career in Machine Learning. It covers algorithms like Linear regression, Logistic Regression, SVM, Naïve Bayes, K-Means, Random Forest, and Feature engineeri
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Machine Learning Algorithms. Popular algorithms for data science and machine learning - Second Edition Machine Learning Algorithms. Popular algorithms for data science and machine learning - Second Edition
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Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time!
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Hands-On Convolutional Neural Networks with TensorFlow. Solve computer vision problems with modeling in TensorFlow and Python Hands-On Convolutional Neural Networks with TensorFlow. Solve computer vision problems with modeling in TensorFlow and Python
Iffat Zafar, Giounona Tzanidou, Richard Burton, Nimesh Patel, Leonardo Araujo
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This book demonstrates how to use deep Learning in R for machine learning, image classification, and natural language processing. It covers topics such as convolutional networks, recurrent neural networks, transfer learning and deep learning in the cloud. By the end of this book, you will be able to apply deep learning to real-world projects.
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R Deep Learning Essentials. A step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet - Second Edition R Deep Learning Essentials. A step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet - Second Edition
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In this book, you will come across various real-world projects which will teach you how to leverage Tensforflow’s capabilities to perform efficient image processing tasks. By the end of this book, you will have mastered all the concepts of deep learning for Images and their implementations with Tensorflow and Keras
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Hands-On Deep Learning for Images with TensorFlow. Build intelligent computer vision applications using TensorFlow and Keras Hands-On Deep Learning for Images with TensorFlow. Build intelligent computer vision applications using TensorFlow and Keras
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Walks through the hands-on process of building intelligent agents from the basics and all the way up to solving complex problems including playing Atari games and driving a car autonomously in the CARLA simulator. Discusses various learning environments and how to transform real-world problems into learning environments and solve using the agents.
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Hands-On Intelligent Agents with OpenAI Gym. Your guide to developing AI agents using deep reinforcement learning Hands-On Intelligent Agents with OpenAI Gym. Your guide to developing AI agents using deep reinforcement learning
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Machine learning allows models or systems to learn without being explicitly programmed. You will see how to use the best of libraries support such as scikit-learn, Tensorflow and much more to build efficient smart systems.
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Building Machine Learning Systems with Python. Explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow - Third Edition Building Machine Learning Systems with Python. Explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow - Third Edition
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This book introduces you to the concept of ensemble learning and demonstrates how different machine learning algorithms can be combined to build efficient machine learning models. Use R to implement the popular trilogy of ensemble techniques, i.e. bagging, random forest and boosting, to build faster and more accurate machine learning models.
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Hands-On Ensemble Learning with R. A beginner's guide to combining the power of machine learning algorithms using ensemble techniques Hands-On Ensemble Learning with R. A beginner's guide to combining the power of machine learning algorithms using ensemble techniques
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This book teaches you to leverage deep learning models in performing various NLP tasks along with showcasing the best practices in dealing with the NLP challenges. The book equips you with practical knowledge to implement deep learning in your linguistic applications using NLTk and Python's popular deep learning library, TensorFlow.
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Hands-On Natural Language Processing with Python. A practical guide to applying deep learning architectures to your NLP applications Hands-On Natural Language Processing with Python. A practical guide to applying deep learning architectures to your NLP applications
Rajesh Arumugam, Rajalingappaa Shanmugamani, Auguste Byiringiro, Chaitanya Joshi, Karthik Muthuswamy
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Apache Spark Deep Learning Cookbook presents useful tips and tricks to overcome any problem related to building efficient distributed deep learning applications on Apache Spark. With the help of this book, you will leverage powerful deep learning libraries such as TensorFlow to develop your models and ensure their optimum performance.
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Apache Spark Deep Learning Cookbook. Over 80 best practice recipes for the distributed training and deployment of neural networks using Keras and TensorFlow Apache Spark Deep Learning Cookbook. Over 80 best practice recipes for the distributed training and deployment of neural networks using Keras and TensorFlow
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This book is a thorough guide for developers who want to get started with building computer vision applications using Julia. Julia is well suited to image processing because of its ease of use and the fact that it lets you write easy-to-compile and efficient machine code.
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Hands-On Computer Vision with Julia. Build complex applications with advanced Julia packages for image processing, neural networks, and Artificial Intelligence Hands-On Computer Vision with Julia. Build complex applications with advanced Julia packages for image processing, neural networks, and Artificial Intelligence
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You will build full-fledged, deep learning applications with Java and different open-source libraries. Master numerical computing, deep learning, and the latest Java programming features to carry out complex advanced tasks. This book is filled with best practices/tips after every project to help you optimize your deep learning models with ease.
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Java Deep Learning Projects. Implement 10 real-world deep learning applications using Deeplearning4j and open source APIs Java Deep Learning Projects. Implement 10 real-world deep learning applications using Deeplearning4j and open source APIs
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Discover how you can perform your own modern text analysis, to make predictions, create inferences, and gain insights about the data around you today. Learn how to harness the powerful Python ecosystem and tools such as spaCy and Gensim to perform natural language processing, and computational linguistics algorithms.
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Natural Language Processing and Computational Linguistics. A practical guide to text analysis with Python, Gensim, spaCy, and Keras Natural Language Processing and Computational Linguistics. A practical guide to text analysis with Python, Gensim, spaCy, and Keras
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Reinforcement learning is a self-evolving type of machine learning that takes us closer to achieving true artificial intelligence. This easy-to-follow guide explains everything from scratch using rich examples written in Python.
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Hands-On Reinforcement Learning with Python. Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow Hands-On Reinforcement Learning with Python. Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow
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Discover the world of ML through the lens and application of Core ML. We will take you through examples; each example provides a new use case uncovering how ML can be applied specifically to computer vision tasks. By the end of the book, you will have the intuition and skills required to boost your iOS applications with the help of machine learning
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Machine Learning with Core ML. An iOS developer's guide to implementing machine learning in mobile apps Machine Learning with Core ML. An iOS developer's guide to implementing machine learning in mobile apps
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We live in an era where cyber security plays an important role. As systems are getting smarter, we now see machine learning interrupting computer security. With the adoption of machine learning in upcoming security products, it’s important for pentesters and security researchers to understand how these systems work, and to breach them for te
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Mastering Machine Learning for Penetration Testing. Develop an extensive skill set to break self-learning systems using Python Mastering Machine Learning for Penetration Testing. Develop an extensive skill set to break self-learning systems using Python
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Take your first foray into programming for Apple devices with Swift.Swift is fundamentally different from Objective-C, as it is a protocol-oriented language. While you can still write normal object-oriented code in Swift, it requires a new way of thinking to take advantage of its powerful features and a solid understanding of the basics to become p
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Beginning Swift. Master the fundamentals of programming in Swift 4 Beginning Swift. Master the fundamentals of programming in Swift 4
Jakie książki pozwolą na praktyczną naukę uczenia maszynowego?
Zastanawiasz się czym jest Uczenie Maszynowe (Machine Learning)? To technika programowania komputerów, które uczą się wykonywać określone zadania na podstawie ogromnych ilości zebranych danych. W wielu przypadkach to rozwiązanie sprawdza się znacznie lepiej niż tradycyjne metody programowania, szczególnie w obszarach, gdzie trudno jest sformułować jasne reguły decyzyjne. Jeśli szukasz praktycznych przykładów uczenia maszynowego, to książki z tej kategorii oferują wiele case studies i analiz. Nasza oferta obejmuje różnorodne książki, które skupiają się na wszystkich najważniejszych aspektach uczenia maszynowego.
Uczenie maszynowe jak zacząć? – książki dla początkujących
Jeśli dopiero zaczynasz swoją przygodę z tą dziedziną, polecamy książkę "Jak projektować systemy uczenia maszynowego”, która omawia temat uczenia maszynowego od podstaw. Znajdziesz tu także praktyczne poradniki, takie jak "Uczenie maszynowe dla programistów" czy „Uczenie głębokie od zera”, które krok po kroku pokażą Ci, jak zacząć przygodę z uczeniem maszynowym czy uczeniem głębokim. Książki te sprawnie wprowadzą Cię także w zagadnienia związane z metodami uczenia nadzorowanego i nienadzorowanego, omówią kluczowe algorytmy i dostarczą liczne przykłady uczenia maszynowego.
Uczenie maszynowe i Python: książki dla programistów
Jeśli Twoim językiem programowania jest Python, to mamy dla Ciebie wiele propozycji książek. Język ten idealnie nadaje się do programowania mechanizmów uczenia maszynowego. Znajdziesz tu takie książki jak „Uczenie maszynowe w Pythonie. Receptury” oraz „Machine learning, Python i data science. Wprowadzenie” czy „Uczenie maszynowe z użyciem Scikit-Learn, Keras i TensorFlow”, dzięki którym dowiesz się jak korzystać z bibliotek takich jak scikit-learn czy TensorFlow, by efektywnie budować i trenować inteligentne modele.
Uczenie maszynowe a sztuczna inteligencja: książki dla zaawansowanych
Różnica między uczeniem maszynowym a sztuczną inteligencją jest często niejasna. W skrócie, sztuczna inteligencja to szeroki obszar informatyki skupiający się na tworzeniu inteligentnych systemów i maszyn, podczas gdy uczenie maszynowe to jedna z technik stosowanych w AI. Wiele osób interesuje się również sieciami neuronowymi, które stanowią podstawę dla głębokiego uczenia maszynowego - jednego z najgorętszych tematów w dziedzinie AI.
W naszej ofercie znajdziesz również książki opisujące różne algorytmy uczenia maszynowego, w tym drzewa decyzyjne, oraz metody uczenia maszynowego, takie jak uczenie nienadzorowane,
Nie ważne, czy jesteś początkującym entuzjastą czy doświadczonym programistą, nasza oferta obejmuje książki dla każdego. A jeśli preferujesz materiały w formie elektronicznej, nie zapomnij sprawdzić naszych książek w formacie PDF, EPUB czy MOBI.
Zachęcamy do odkrywania świata uczenia maszynowego poprzez nasze książki, które rozwijają umiejętności i otwierają drzwi do nowoczesnej analizy danych i sztucznej inteligencji.