Founded in 2004 in Birmingham, UK, Packt's mission is to help the world put software to work in new ways, through the delivery of effective learning and information services to IT professionals. Working towards that vision, we have published over 6,500 books and videos so far, providing IT professionals with the actionable knowledge they need to get the job done - whether that's specific learning on an emerging technology or optimizing key skills in more established tools. As part of our mission, we have also awarded over $1,000,000 through our Open Source Project Royalty scheme, helping numerous projects become household names along the way.
Packt Publishing - ebooki
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Nowość
Dive into machine learning with our course, designed to guide you in building your own recommender system using Scikit-Learn, complete with practical datasets and ethical considerations.- PDF + ePub pkt
Machine Learning: Make Your Own Recommender System. Build Your Recommender System with Machine Learning Insights Machine Learning: Make Your Own Recommender System. Build Your Recommender System with Machine Learning Insights
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Nowość
Dive into the world of Machine Learning with Python, exploring essential algorithms, data scrubbing techniques, and model design to unlock the potential of AI and data science.- PDF + ePub pkt
Machine Learning with Python. Unlocking AI Potential with Python and Machine Learning Machine Learning with Python. Unlocking AI Potential with Python and Machine Learning
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Nowość
Master your expertise and skills in machine learning with this AWS Certified Machine Learning Specialty exam guide. Become proficient in designing robust solutions and prove expertise in deploying machine learning models on the AWS cloud.- PDF + ePub pkt
AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide. The ultimate guide to passing the MLS-C01 exam on your first attempt - Second Edition AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide. The ultimate guide to passing the MLS-C01 exam on your first attempt - Second Edition
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Bayesian inference uses probability distributions and Bayes' theorem to build flexible models. This book uses PyMC to abstract all mathematical and computational details from this process, allowing readers to solve a range of data science problems.
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Bayesian Analysis with Python. A practical guide to probabilistic modeling - Third Edition Bayesian Analysis with Python. A practical guide to probabilistic modeling - Third Edition
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Discover data labeling methods through Python libraries, ML algorithms, and generative AI with this guide covering best practices, advanced methods, and tools. This book will simplify model training for regression, classification, and clustering.
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Data Labeling in Machine Learning with Python. Explore modern ways to prepare labeled data for training and fine-tuning ML and generative AI models Data Labeling in Machine Learning with Python. Explore modern ways to prepare labeled data for training and fine-tuning ML and generative AI models
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Machine learning is an important driver of innovation in software products. This book will help you take your machine learning prototype to the next level and scale it up using concepts such as data provisioning, processing, and quality control.
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Machine Learning Infrastructure and Best Practices for Software Engineers. Take your machine learning software from a prototype to a fully fledged software system Machine Learning Infrastructure and Best Practices for Software Engineers. Take your machine learning software from a prototype to a fully fledged software system
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This book is your complete guide to seamless ML operations on OpenShift. You’ll master MLOps concepts, explore OpenShift Data Science, Pachyderm, and Intel OpenVino, along with the provision, configuration, and operationalization of the MLOps platform.
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MLOps with Red Hat OpenShift. A cloud-native approach to machine learning operations MLOps with Red Hat OpenShift. A cloud-native approach to machine learning operations
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Unlock the power of MATLAB for machine learning with this comprehensive guide. From data handling to advanced techniques like deep learning, NLP, and anomaly detection, gain practical skills applicable across a range of AI applications.
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MATLAB for Machine Learning. Unlock the power of deep learning for swift and enhanced results - Second Edition MATLAB for Machine Learning. Unlock the power of deep learning for swift and enhanced results - Second Edition
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This book explores MXNet/Gluon libraries, delves into toy dataset analysis, and assists in creating deep neural network architectures. With practical examples, it enables you to perform tasks such as transfer learning and transformers in with ease.
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Deep Learning with MXNet Cookbook. Discover an extensive collection of recipes for creating and implementing AI models on MXNet Deep Learning with MXNet Cookbook. Discover an extensive collection of recipes for creating and implementing AI models on MXNet
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Machine Learning Security with Azure will help you assess the vulnerabilities in your data, models, and environments, and implement the latest industry security best practices. You’ll get to grips with managing, securing, and monitoring your Azure machine learning workloads throughout the ML lifecycle while developing responsible AI applicat
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Machine Learning Security with Azure. Best practices for assessing, securing, and monitoring Azure Machine Learning workloads Machine Learning Security with Azure. Best practices for assessing, securing, and monitoring Azure Machine Learning workloads
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Discover the power of Conformal Prediction with the Practical Guide to Applied Conformal Prediction in Python. Master the latest techniques to quantify uncertainty in machine learning and computer vision models, and seamlessly apply them to your industry applications.
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Practical Guide to Applied Conformal Prediction in Python. Learn and apply the best uncertainty frameworks to your industry applications Practical Guide to Applied Conformal Prediction in Python. Learn and apply the best uncertainty frameworks to your industry applications
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With over 70 project-based recipes, the TinyML Cookbook is a practical guide that will help you to get the most out of your microcontrollers. It provides a comprehensive understanding of the theoretical foundations while giving you hands-on experience training ML models for deployment on Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun Re
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TinyML Cookbook. Combine machine learning with microcontrollers to solve real-world problems - Second Edition TinyML Cookbook. Combine machine learning with microcontrollers to solve real-world problems - Second Edition
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This hands-on book will help you make your machine learning models fairer, safer, and more reliable and in turn improve business outcomes. Every chapter introduces a new mission where you learn how to apply interpretation methods to realistic use cases with methods that work for any model type as well as methods specific for deep neural networks.
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Interpretable Machine Learning with Python. Build explainable, fair, and robust high-performance models with hands-on, real-world examples - Second Edition Interpretable Machine Learning with Python. Build explainable, fair, and robust high-performance models with hands-on, real-world examples - Second Edition
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Machine Learning with Qlik Sense enables you to master machine learning skills and techniques and expand the use of analytics to predictive use cases. The chapters are designed to help you learn the key concepts of machine learning using practical examples, enabling you to create better analytics applications and get the most out of your data.
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Machine Learning with Qlik Sense. Utilize different machine learning models in practical use cases by leveraging Qlik Sense Machine Learning with Qlik Sense. Utilize different machine learning models in practical use cases by leveraging Qlik Sense
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The book is a foundational guide for developers keen on mastering machine learning and statistical modeling in R. It offers a hands-on approach along with real-world applications. Whether you're a beginner or looking to upgrade your skills, the book equips you with practical knowledge to tackle a wide array of tasks in ML and statistics.
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The Statistics and Machine Learning with R Workshop. Unlock the power of efficient data science modeling with this hands-on guide The Statistics and Machine Learning with R Workshop. Unlock the power of efficient data science modeling with this hands-on guide
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Machine Learning with LightGBM and Python is a practical guide that takes you from the essentials of machine learning and the data science processes to building production-ready, high-performance machine learning solutions using LightGBM on -of-the-art platforms.
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Machine Learning with LightGBM and Python. A practitioner's guide to developing production-ready machine learning systems Machine Learning with LightGBM and Python. A practitioner's guide to developing production-ready machine learning systems
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TensorFlow finds applications in companies like Google, Twitter, Intel, and Airbnb for solving business problems. Get ready to validate your skills with TensorFlow Developer Certificate using this comprehensive guide to building deep learning models through hands-on, real-world exercises, step-by-step explanations, and case studies.
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TensorFlow Developer Certificate Guide. Efficiently tackle deep learning and ML problems to ace the Developer Certificate exam TensorFlow Developer Certificate Guide. Efficiently tackle deep learning and ML problems to ace the Developer Certificate exam
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With practical and in-depth coverage of everything from the basics of dataset construction to the intricacies of deep learning, Machine Learning for Emotion Analysis in Python introduces you to emotion analysis and helps you accelerate your business growth.
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Machine Learning for Emotion Analysis in Python. Build AI-powered tools for analyzing emotion using natural language processing and machine learning Machine Learning for Emotion Analysis in Python. Build AI-powered tools for analyzing emotion using natural language processing and machine learning
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Debugging Machine Learning Models with Python equips you with the skills needed to transition from a machine learning beginner to a specialist. It guides you through the tools, theoretical concepts, Python modules, and libraries for bringing a high-quality model into production and identifying opportunities to improve your models.
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Debugging Machine Learning Models with Python. Develop high-performance, low-bias, and explainable machine learning and deep learning models Debugging Machine Learning Models with Python. Develop high-performance, low-bias, and explainable machine learning and deep learning models
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Machine learning engineering is an in-demand skill set, and it can be difficult to find a helpful guide on the topic. This fully updated second edition will help you solve business problems by addressing the pain points in creating standardized pipelines for taking proof-of-concept ML models to production and producing trustworthy results.
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Machine Learning Engineering with Python. Manage the lifecycle of machine learning models using MLOps with practical examples - Second Edition Machine Learning Engineering with Python. Manage the lifecycle of machine learning models using MLOps with practical examples - Second Edition
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The book helps you grasp images, text, audio, and tabular augmentation methods using real-world datasets from the Kaggle website. Throughout the chapters, you’ll discover augmentation concepts and techniques, a review of available open source augmentation libraries, and a reinforcement learning section through Python Notebook coding exercise
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Data Augmentation with Python. Enhance deep learning accuracy with data augmentation methods for image, text, audio, and tabular data Data Augmentation with Python. Enhance deep learning accuracy with data augmentation methods for image, text, audio, and tabular data
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Computer Vision Using AWS AI Services enables machine learning engineers and data scientists to build and scale CV applications on AWS quickly. This comprehensive guide covers best practices to consider and shows you how to integrate AWS AI/ML services into production environments with little code.
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Computer Vision on AWS. Build and deploy real-world CV solutions with Amazon Rekognition, Lookout for Vision, and SageMaker Computer Vision on AWS. Build and deploy real-world CV solutions with Amazon Rekognition, Lookout for Vision, and SageMaker
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This is a book for anyone who wants to quickly learn to apply spatial thinking and leverage Geospatial Data Science in Python to better comprehend problem statements and come up with the best possible results.
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Applied Geospatial Data Science with Python. Leverage geospatial data analysis and modeling to find unique solutions to environmental problems Applied Geospatial Data Science with Python. Leverage geospatial data analysis and modeling to find unique solutions to environmental problems
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This is a practical workbook that analyzes past Kaggle competitions in hopes of helping readers understand the best practices on how to approach future challenges. With the help of this workbook, readers will experience the case studies as if they themselves were part of the competitions and gain hands-on experience on how to approach their first K
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The Kaggle Workbook. Self-learning exercises and valuable insights for Kaggle data science competitions The Kaggle Workbook. Self-learning exercises and valuable insights for Kaggle data science competitions
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Betty Blocks is a no-code platform that allows users to build their own applications. Developers can add functionality to the platform to help citizen developers do more. This book will help you understand the implementation and associated methodologies of Betty Blocks and get you up and running in no time.
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Democratizing Application Development with Betty Blocks. Build powerful applications that impact business immediately with no-code app development Democratizing Application Development with Betty Blocks. Build powerful applications that impact business immediately with no-code app development
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This book helps you understand the need and complexity of the transformation of healthcare using a comprehensive model (TiSH) that consists of 7 treads. You’ll learn about organizing healthcare in stepped, networked, and integrated care, in a fully patient-centric way and by using modern technologies and agile development methodologies.
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Transforming Healthcare with DevOps. A practical DevOps4Care guide to embracing the complexity of digital transformation Transforming Healthcare with DevOps. A practical DevOps4Care guide to embracing the complexity of digital transformation
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Quantum Machine Learning and Optimisation in Finance shows you how to create hybrid quantum-classical machine learning and optimisation models that can harness the power of NISQ hardware.
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Quantum Machine Learning and Optimisation in Finance. On the Road to Quantum Advantage Quantum Machine Learning and Optimisation in Finance. On the Road to Quantum Advantage
Antoine Jacquier, Oleksiy Kondratyev, Alexander Lipton, Marcos López de Prado
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Deep Learning with TensorFlow and Keras, Third 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 cloud in production, TF mobile, and using TensorFlow with AutoML.
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Deep Learning with TensorFlow and Keras. Build and deploy supervised, unsupervised, deep, and reinforcement learning models - Third Edition Deep Learning with TensorFlow and Keras. Build and deploy supervised, unsupervised, deep, and reinforcement learning models - Third Edition
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Machine Learning at Scale with H2O helps you to succeed in building and deploying state-of-the-art predictive models with large data volumes in an enterprise setting. The book enables you to understand the perspectives of varied stakeholders while navigating the journey from model development to business value.
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Machine Learning at Scale with H2O. A practical guide to building and deploying machine learning models on enterprise systems Machine Learning at Scale with H2O. A practical guide to building and deploying machine learning models on enterprise systems
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Learning to solve common NLP problems effectively is an important skill to master due to its popularity. TensorFlow is one of the leading frameworks for implementing production-grade machine learning solutions. By the end of this book, you’ll be ready to confidently develop end-to-end machine learning solutions efficiently with TensorFlow.
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Natural Language Processing with TensorFlow. The definitive NLP book to implement the most sought-after machine learning models and tasks - Second Edition Natural Language Processing with TensorFlow. The definitive NLP book to implement the most sought-after machine learning models and tasks - Second Edition
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This book is a comprehensive guide that teaches Android developers asynchronous programming using Kotlin flows and coroutines. You’ll begin with the basics of asynchronous programming, including an overview of coroutines and flows, while also integrating them into your Android projects. You’ll also learn how to create high-performant
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Simplifying Android Development with Coroutines and Flows. Learn how to use Kotlin coroutines and the flow API to handle data streams asynchronously in your Android app Simplifying Android Development with Coroutines and Flows. Learn how to use Kotlin coroutines and the flow API to handle data streams asynchronously in your Android app
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Machine Learning on Kubernetes guides you on how to implement the entire machine learning life cycle on the Kubernetes platform. The topics covered in this book help you to acquire the fundamental knowledge needed to set up and use a set of open source tools to deliver intelligent applications to production.
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Machine Learning on Kubernetes. A practical handbook for building and using a complete open source machine learning platform on Kubernetes Machine Learning on Kubernetes. A practical handbook for building and using a complete open source machine learning platform on Kubernetes
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This book helps you get to grips with the rapidly growing data science domain and takes you on a journey from understanding which algorithm family to use to how to prevent bias. Through hands-on examples and clear analogies, you’ll develop expertise in data science and open source tools no matter what your level of experience is.
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Building Data Science Solutions with Anaconda. A comprehensive starter guide to building robust and complete models Building Data Science Solutions with Anaconda. A comprehensive starter guide to building robust and complete models
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This updated second edition of Mastering Azure Machine Learning is a critically timed book. It builds upon simple and advanced NLP techniques, ML models such as boosted trees, deep neural network architectures, and more to help you leverage the power of Azure Machine Learning Services.
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Mastering Azure Machine Learning. Execute large-scale end-to-end machine learning with Azure - Second Edition Mastering Azure Machine Learning. Execute large-scale end-to-end machine learning with Azure - Second Edition
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This book will introduce you to the basics of PyTorch Lightning and how you can build deep learning models from scratch. You'll learn techniques for building a variety of model network architecture, debugging them, and tuning them by utilizing some of the advanced features of the PyTorch Lightning framework to research and invent new methods.
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Deep Learning with PyTorch Lightning. Swiftly build high-performance Artificial Intelligence (AI) models using Python Deep Learning with PyTorch Lightning. Swiftly build high-performance Artificial Intelligence (AI) models using Python
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Democratizing Artificial Intelligence with UiPath will help you understand the power of UiPath’s AI features and tools. This practical guide will help lower the barrier to using AI by teaching you how to integrate UiPath AI Center, UiPath Document Understanding, and Druid with automation. By taking a hands-on approach, you will quickly be ab
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Democratizing Artificial Intelligence with UiPath. Expand automation in your organization to achieve operational efficiency and high performance Democratizing Artificial Intelligence with UiPath. Expand automation in your organization to achieve operational efficiency and high performance
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Distributed Machine Learning with Python takes you through state-of-the-art techniques built on top of traditional data and model parallelism approaches. It explains the concept of hybrid data-model parallelism, federated learning, and edge device learning with elastic and in-parallel model training in multi-tenant clusters.
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Distributed Machine Learning with Python. Accelerating model training and serving with distributed systems Distributed Machine Learning with Python. Accelerating model training and serving with distributed systems
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Natural Language Processing with Flair is a book for NLP developers, data scientists, and machine learning enthusiasts who want to get up to speed with Flair to be able to put their knowledge to work by solving real-life problems. The book features a number of hands-on exercises showing how to create and deploy Flair NLP solutions with ease.
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Natural Language Processing with Flair. A practical guide to understanding and solving NLP problems with Flair Natural Language Processing with Flair. A practical guide to understanding and solving NLP problems with Flair
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Start your quest in quantum computing the right way with an understanding of the math that undergirds it, or come back to the math you may have missed when you started learning quantum computing. This book covers linear algebra, complex numbers, and probability - just enough for you to master all the math needed for quantum computing.
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Essential Mathematics for Quantum Computing. A beginner's guide to just the math you need without needless complexities Essential Mathematics for Quantum Computing. A beginner's guide to just the math you need without needless complexities
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This book serves as a concise path toward cracking Kaggle competitions as well as an all-encompassing guide to upgrading your skills and career as a data scientist.
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The Kaggle Book. Data analysis and machine learning for competitive data science The Kaggle Book. Data analysis and machine learning for competitive data science
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Automated Machine Learning on AWS is a practical guide that provides hands-on experience to help you learn how to automate a machine learning pipeline using the various AWS services. With this book, you will be able to successfully overcome any machine learning automation challenges on AWS in no time.
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Automated Machine Learning on AWS. Fast-track the development of your production-ready machine learning applications the AWS way Automated Machine Learning on AWS. Fast-track the development of your production-ready machine learning applications the AWS way
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Running machine learning on microcontrollers enables everyday objects to make smarter decisions through minimum interactions with the end user. This book features useful and exciting recipes that help you combine the capabilities of embedded ultra-low-power systems and machine learning for AI-powered IoT devices.
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TinyML Cookbook. Combine artificial intelligence and ultra-low-power embedded devices to make the world smarter TinyML Cookbook. Combine artificial intelligence and ultra-low-power embedded devices to make the world smarter
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Developers working with machine learning will be able to put their knowledge to work with this practical guide to Amazon SageMaker Studio. The book takes a hands-on approach to implementing real-world machine learning use cases that will have you up and running quickly.
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Getting Started with Amazon SageMaker Studio. Learn to build end-to-end machine learning projects in the SageMaker machine learning IDE Getting Started with Amazon SageMaker Studio. Learn to build end-to-end machine learning projects in the SageMaker machine learning IDE
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Unity AI Programming provides you with a variety of tools to implement artificial intelligence and leveraging these tools allows limitless possibilities for creating your own games. This book will break down AI into simple concepts to give you a fundamental understanding of the core topics, and practical guidance for implementing them.
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Unity Artificial Intelligence Programming. Add powerful, believable, and fun AI entities in your game with the power of Unity - Fifth Edition Unity Artificial Intelligence Programming. Add powerful, believable, and fun AI entities in your game with the power of Unity - Fifth Edition
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Being the first book in the market to dive deep into the Transformers, this book is a step-by-step guide for data and AI practitioners to help enhance the performance of language understanding and gain expertise with hands-on implementation of transformers with Python, PyTorch, and TensorFlow
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Transformers for Natural Language Processing. Build, train, and fine-tune deep neural network architectures for NLP with Python, Hugging Face, and OpenAI's GPT-3, ChatGPT, and GPT-4 - Second Edition Transformers for Natural Language Processing. Build, train, and fine-tune deep neural network architectures for NLP with Python, Hugging Face, and OpenAI's GPT-3, ChatGPT, and GPT-4 - Second Edition
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Pachyderm enables you to create collaborative data science workflows and reproduce your experiments at scale. This book will help you leverage Pachyderm's data versioning and lineage features to build scalable end-to-end AI/ML pipelines and show you how to deploy Pachyderm in leading cloud platforms, use its SaaS offering PachHub, and much more.
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Reproducible Data Science with Pachyderm. Learn how to build version-controlled, end-to-end data pipelines using Pachyderm 2.0 Reproducible Data Science with Pachyderm. Learn how to build version-controlled, end-to-end data pipelines using Pachyderm 2.0
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Time Series Analysis on AWS teaches you how to leverage AI/ML managed services to deliver desired business outcomes with time series data. This book will help you solve modern time series analysis problems such as forecasting and anomaly detection using industrial equipment sensors or business metrics.
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Time Series Analysis on AWS. Learn how to build forecasting models and detect anomalies in your time series data Time Series Analysis on AWS. Learn how to build forecasting models and detect anomalies in your time series data
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Fully updated with PyTorch 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: transformers and graph neural networks.
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Machine Learning with PyTorch and Scikit-Learn. Develop machine learning and deep learning models with Python Machine Learning with PyTorch and Scikit-Learn. Develop machine learning and deep learning models with Python
Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili, Dmytro Dzhulgakov
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Machine Learning in Biotechnology and Life Sciences is a critically timed book for scientists and developers working in the biotechnology sector. This book takes a hands-on approach to bridging the gap for scientists who have a strong desire to learn and apply AI methods within their roles to drive efficiency and agility in their company.
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Machine Learning in Biotechnology and Life Sciences. Build machine learning models using Python and deploy them on the cloud Machine Learning in Biotechnology and Life Sciences. Build machine learning models using Python and deploy them on the cloud
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Intelligent Workloads at the Edge is a practical guide to designing, deploying, and operating data-driven workloads on edge IoT devices for delivering cyber-physical outcomes. You'll work with hands-on projects to learn how to build intelligent applications that run on edge devices with AWS IoT Greengrass.
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Intelligent Workloads at the Edge. Deliver cyber-physical outcomes with data and machine learning using AWS IoT Greengrass Intelligent Workloads at the Edge. Deliver cyber-physical outcomes with data and machine learning using AWS IoT Greengrass
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This book can help you rapidly explore and build a broader range of machine learning models using DataRobot's capabilities. You'll learn how to deploy ML models and operationalize and monitor their performance using practical examples. Data science experts will learn how to scale model building by using the Python API, among other advanced topics.
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Agile Machine Learning with DataRobot. Automate each step of the machine learning life cycle, from understanding problems to delivering value Agile Machine Learning with DataRobot. Automate each step of the machine learning life cycle, from understanding problems to delivering value
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This Workshop will teach you how to build deep learning models from scratch using real-world datasets with the TensorFlow framework. You will gain the knowledge you need to process a variety of data types, perform tensor computations, and understand the different layers in a deep learning model.
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The TensorFlow Workshop. A hands-on guide to building deep learning models from scratch using real-world datasets The TensorFlow Workshop. A hands-on guide to building deep learning models from scratch using real-world datasets
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The Azure Data Scientist Associate Certification Guide offers complete coverage of the DP-100 exam content to help you to pass it with confidence. This book goes beyond the requirements for the exam and provides additional knowledge to ensure success in your real-life data science projects too.
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Azure Data Scientist Associate Certification Guide. A hands-on guide to machine learning in Azure and passing the Microsoft Certified DP-100 exam Azure Data Scientist Associate Certification Guide. A hands-on guide to machine learning in Azure and passing the Microsoft Certified DP-100 exam
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This updated second edition of Learn Amazon SageMaker will teach you how to move quickly from business questions to high performance models in production. Using machine learning and deep learning examples implemented with Python and Jupyter notebooks, you’ll learn how to make the most of the many features and APIs of Amazon SageMaker.
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Learn Amazon SageMaker. A guide to building, training, and deploying machine learning models for developers and data scientists - Second Edition Learn Amazon SageMaker. A guide to building, training, and deploying machine learning models for developers and data scientists - Second Edition
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IBM Cloud Pak for Data is a data and AI platform that helps businesses with their data modernization initiatives and to democratize data access to accelerate advanced analytics. This book will teach you how to develop a trusted data foundation to operationalize AI with trust, reliability, and transparency in your business processes.
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IBM Cloud Pak for Data. An enterprise platform to operationalize data, analytics, and AI IBM Cloud Pak for Data. An enterprise platform to operationalize data, analytics, and AI
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Machine learning engineering is an in-demand skill set, and it can be difficult to find a helpful guide on the topic. This book will help you solve business problems by addressing the pain points in creating standardized pipelines for taking proof-of-concept ML models to production and producing trustworthy results.
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Machine Learning Engineering with Python. Manage the production life cycle of machine learning models using MLOps with practical examples Machine Learning Engineering with Python. Manage the production life cycle of machine learning models using MLOps with practical examples
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The book contains the most common as well as state-of-the-art methods in machine learning for time-series, and examples that every data scientist or analyst would have encountered, if not in their job, then in a job interview.
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Machine Learning for Time-Series with Python. Forecast, predict, and detect anomalies with state-of-the-art machine learning methods Machine Learning for Time-Series with Python. Forecast, predict, and detect anomalies with state-of-the-art machine learning methods
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Explore the capabilities of Amazon SageMaker to effectively train, deploy, and manage machine learning and deep learning models in AWS with complete and thoroughly explained hands-on examples. With 80 proven recipes, this step-by-step guide will help you build the skills necessary to contribute to real-life ML requirements and projects.
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Machine Learning with Amazon SageMaker Cookbook. 80 proven recipes for data scientists and developers to perform machine learning experiments and deployments Machine Learning with Amazon SageMaker Cookbook. 80 proven recipes for data scientists and developers to perform machine learning experiments and deployments
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Rasa provides an open-source framework for developing AI chatbots that uses natural language processing (NLP) and machine learning technologies. With this book, you'll become well-versed with all that Rasa offers to help you build a next-level customer experience at scale.
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Conversational AI with Rasa. Build, test, and deploy AI-powered, enterprise-grade virtual assistants and chatbots Conversational AI with Rasa. Build, test, and deploy AI-powered, enterprise-grade virtual assistants and chatbots
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Deep Learning with fastai Cookbook helps you to quickly apply fastai to create deep learning applications. This recipe-based book teaches you how to use fastai for each of the four application areas that the framework explicitly supports: tabular data, text data (NLP), recommender systems, and vision data.
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Deep Learning with fastai Cookbook. Leverage the easy-to-use fastai framework to unlock the power of deep learning Deep Learning with fastai Cookbook. Leverage the easy-to-use fastai framework to unlock the power of deep learning
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Gain a high-level introduction to GPT-3 and the OpenAI API with Exploring GPT-3. Get started without prior programming experience and learn how to work with GPT-3 for text generation, classification, semantic search, and more either without coding using the OpenAI Playground or by following simple code examples in both JavaScript and Python.
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Exploring GPT-3. An unofficial first look at the general-purpose language processing API from OpenAI Exploring GPT-3. An unofficial first look at the general-purpose language processing API from OpenAI
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Machine Learning Engineering with MLflow is a step-by-step guide that will have you up and running, and productive in no time with MLflow using the most effective machine learning engineering approach. You will also learn how to scale MLflow in big data environments and for high computing demands.
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Machine Learning Engineering with MLflow. Manage the end-to-end machine learning life cycle with MLflow Machine Learning Engineering with MLflow. Manage the end-to-end machine learning life cycle with MLflow
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Getting Started with Streamlit for Data Science takes you from the basics of web app development in the Streamlit library and machine learning model deployment in Heroku and AWS. The book shows you how you can use Streamlit for a variety of projects to improve job applications and resumes and much more.
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Getting Started with Streamlit for Data Science. Create and deploy Streamlit web applications from scratch in Python Getting Started with Streamlit for Data Science. Create and deploy Streamlit web applications from scratch in Python
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Data scientists working with network data will be able to put their knowledge to work with this practical guide to building machine learning algorithms using graph data. 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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Graph Machine Learning. Take graph data to the next level by applying machine learning techniques and algorithms Graph Machine Learning. Take graph data to the next level by applying machine learning techniques and algorithms
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This book helps you accelerate machine learning model development with BigQuery ML. Throughout the book, you'll use various ML models to learn about BigQuery ML features and discover how to apply them to different business scenarios. This book will help you to extend existing SQL capabilities to leverage the full potential of machine learning.
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Machine Learning with BigQuery ML. Create, execute, and improve machine learning models in BigQuery using standard SQL queries Machine Learning with BigQuery ML. Create, execute, and improve machine learning models in BigQuery using standard SQL queries
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Machine Learning with the Elastic Stack, Second Edition, provides a comprehensive overview of Elastic Stack's machine learning features for both time series data analysis as well as for supervised learning and unsupervised learning that helps make machine learning truly operational for you.
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Machine Learning with the Elastic Stack. Gain valuable insights from your data with Elastic Stack's machine learning features - Second Edition Machine Learning with the Elastic Stack. Gain valuable insights from your data with Elastic Stack's machine learning features - Second Edition
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AutoKeras is a very simple and popular open source AutoML framework that provides easy access to deep learning models. This book will help you to explore the basics of automated machine learning using practical examples, enabling you to create and use your own models in your company or project.
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Automated Machine Learning with AutoKeras. Deep learning made accessible for everyone with just few lines of coding Automated Machine Learning with AutoKeras. Deep learning made accessible for everyone with just few lines of coding
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If you are a developer looking to build machine learning models without spending months and years learning machine learning prerequisites, look no further than AutoML. This practical and concise guide will show you how to build automated models for regression and classification, both with traditional algorithms and neural networks.
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Machine Learning Automation with TPOT. Build, validate, and deploy fully automated machine learning models with Python Machine Learning Automation with TPOT. Build, validate, and deploy fully automated machine learning models with Python
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A practical, step-by-step guide to using Microsoft's AutoML technology on the Azure Machine Learning service for developers and data scientists working with the Python programming language
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Automated Machine Learning with Microsoft Azure. Build highly accurate and scalable end-to-end AI solutions with Azure AutoML Automated Machine Learning with Microsoft Azure. Build highly accurate and scalable end-to-end AI solutions with Azure AutoML
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Get to grips with ML lifecycle management and MLOps implementation for your organization. This book will give you comprehensive insights into MLOps coupled with real-world examples in Azure that will teach you how to write programs, train robust and scalable ML models, and build ML pipelines to train, deploy, and monitor models securely in producti
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Engineering MLOps. Rapidly build, test, and manage production-ready machine learning life cycles at scale Engineering MLOps. Rapidly build, test, and manage production-ready machine learning life cycles at scale
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This hands-on book will help you make your machine learning models fairer, safer, and more reliable and in turn improve business outcomes. Every chapter introduces a new mission where you learn how to apply interpretation methods to realistic use cases with methods that work for any model type as well as methods specific for deep neural networks.
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Interpretable Machine Learning with Python. Learn to build interpretable high-performance models with hands-on real-world examples Interpretable Machine Learning with Python. Learn to build interpretable high-performance models with hands-on real-world examples
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The AWS Certified Machine Learning Specialty 2020 Certification Guide covers everything you need to pass the MLS-C01 certification exam and serves as a handy, on-the-job reference guide. You'll also find the book useful if you're looking to get up to speed with AWS services for machine learning.
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AWS Certified Machine Learning Specialty: MLS-C01 Certification Guide. The definitive guide to passing the MLS-C01 exam on the very first attempt AWS Certified Machine Learning Specialty: MLS-C01 Certification Guide. The definitive guide to passing the MLS-C01 exam on the very first attempt
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This guide will help you to explore automated machine learning (AutoML), a rapidly growing subfield of machine learning. You’ll learn how you can use AutoML to fully automate the machine learning process even if you’re not an expert, and in turn increase your productivity drastically.
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Automated Machine Learning. Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms Automated Machine Learning. Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms
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This book is designed to guide you through TensorFlow and how to use it effectively. Throughout the book, you will work through recipes and get hands-on experience to perform complex data computations, gain insights into your data, and more.
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Machine Learning Using TensorFlow Cookbook. Create powerful machine learning algorithms with TensorFlow Machine Learning Using TensorFlow Cookbook. Create powerful machine learning algorithms with TensorFlow
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Starting with an easy introduction to KNIME Analytics Platform, this book will take you through the key features of the platform and cover the advanced and latest deep learning concepts in neural networks. In each chapter, you’ll solve real-world case studies based on deep learning networks to spark your creativity for new projects.
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Codeless Deep Learning with KNIME. Build, train, and deploy various deep neural network architectures using KNIME Analytics Platform Codeless Deep Learning with KNIME. Build, train, and deploy various deep neural network architectures using KNIME Analytics Platform
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Microsoft Power BI Quick Start Guide, Second Edition gets you up to speed with Power BI quickly, enabling you to derive actionable insights from your data using the data visualization capabilities of Microsoft Power BI within a short span of time.
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Microsoft Power BI Quick Start Guide. Bring your data to life through data modeling, visualization, digital storytelling, and more - Second Edition Microsoft Power BI Quick Start Guide. Bring your data to life through data modeling, visualization, digital storytelling, and more - Second Edition
Devin Knight, Mitchell Pearson, Bradley Schacht, Erin Ostrowsky
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Equipped with the latest updates, this third edition of Python Machine Learning By Example provides a comprehensive course for ML enthusiasts to strengthen their command of ML concepts, techniques, and algorithms.
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Python Machine Learning By Example. Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn - Third Edition Python Machine Learning By Example. Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn - Third Edition
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This book is for beginners who are looking for a strong foundation to build deep learning models from scratch. You will test your understanding of the concepts and measure your progress at the end of each chapter. You will have a firm understanding of deep learning and will be able to identify which algorithms are appropriate for different tasks.
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Deep Learning for Beginners. A beginner's guide to getting up and running with deep learning from scratch using Python Deep Learning for Beginners. A beginner's guide to getting up and running with deep learning from scratch using Python
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The Natural Language Processing Workshop takes you through fundamental NLP techniques, such as preparing datasets, collecting text, extracting text, and sentiment analysis. As you progress, you’ll get to grips with creating your own chatbots and dynamic models.
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The Natural Language Processing Workshop. Confidently design and build your own NLP projects with this easy-to-understand practical guide The Natural Language Processing Workshop. Confidently design and build your own NLP projects with this easy-to-understand practical guide
Rohan Chopra, Aniruddha M. Godbole, Nipun Sadvilkar, Muzaffar Bashir Shah, Sohom Ghosh, Dwight Gunning
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This book teaches you the different techniques and methodologies associated while implementing deep learning solutions in self-driving cars. You will use real-world examples to implement various neural network architectures to develop your own autonomous and automated vehicle using the Python environment.
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Applied Deep Learning and Computer Vision for Self-Driving Cars. Build autonomous vehicles using deep neural networks and behavior-cloning techniques Applied Deep Learning and Computer Vision for Self-Driving Cars. Build autonomous vehicles using deep neural networks and behavior-cloning techniques
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This thoroughly revised and expanded second edition demonstrates on over 800 pages how machine learning can add value to algorithmic trading in a practical yet comprehensive way. It has four parts that cover how to work with a diverse set of market, fundamental, and alternative data sources, design ML solutions for real-world trading challenges, an
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Machine Learning for Algorithmic Trading. Predictive models to extract signals from market and alternative data for systematic trading strategies with Python - Second Edition Machine Learning for Algorithmic Trading. Predictive models to extract signals from market and alternative data for systematic trading strategies with Python - Second Edition
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With The Deep Learning Workshop, you’ll learn about essential deep learning concepts, such as image recognition, text embedding, and neural networks, all so that you can build your own smart machine learning models. You'll be able to learn at your own pace with the help of interesting activities and hands-on exercises that will keep you hook
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The Deep Learning Workshop. Learn the skills you need to develop your own next-generation deep learning models with TensorFlow and Keras The Deep Learning Workshop. Learn the skills you need to develop your own next-generation deep learning models with TensorFlow and Keras
Mirza Rahim Baig, Thomas V. Joseph, Nipun Sadvilkar, Mohan Kumar Silaparasetty, Anthony So
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The Deep Learning with Keras Workshop outlines a simple and straightforward way for you to understand deep learning with Keras. Starting with basic concepts such as data preprocessing, this book equips you with all the tools and techniques required for training your neural networks to solve various modeling problems.
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The Deep Learning with Keras Workshop. Learn how to define and train neural network models with just a few lines of code The Deep Learning with Keras Workshop. Learn how to define and train neural network models with just a few lines of code
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With the help of engaging practical activities, The Unsupervised Learning Workshop teaches you how to apply unsupervised machine learning algorithms on enormous, cluttered datasets. You’ll learn the best techniques and approaches and work on real-time datasets with this hands-on guide for beginners.
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The Unsupervised Learning Workshop. Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions The Unsupervised Learning Workshop. Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions
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This book covers the theory and practice of building data-driven solutions. Includes the end-to-end process, using supervised and unsupervised algorithms. With each algorithm, you will learn the data acquisition and data engineering methods, the apt metrics, and the available hyper-parameters. You will learn how to deploy the models in production.
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Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits. A practical guide to implementing supervised and unsupervised machine learning algorithms in Python Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits. A practical guide to implementing supervised and unsupervised machine learning algorithms in Python
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With this hands-on, self-paced guide, you'll explore crucial deep learning topics and discover the structure and syntax of PyTorch. Challenging activities and interactive exercises will keep you motivated and encourage you to build intelligent applications effectively.
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The Deep Learning with PyTorch Workshop. Build deep neural networks and artificial intelligence applications with PyTorch The Deep Learning with PyTorch Workshop. Build deep neural networks and artificial intelligence applications with PyTorch
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With expert guidance and real-world examples, The Machine Learning Workshop gets you up and running with programming machine learning algorithms. By showing you how to leverage scikit-learn's flexibility, it teaches you all the skills you need to use machine learning to solve real-world problems.
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The Machine Learning Workshop. Get ready to develop your own high-performance machine learning algorithms with scikit-learn - Second Edition The Machine Learning Workshop. Get ready to develop your own high-performance machine learning algorithms with scikit-learn - Second Edition
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Developers working with the simulation models will be able to put their knowledge to work with this practical guide. You will work with real-world data to uncover various patterns used in complex systems using Python. The book provides a hands-on approach to implementation and associated methodologies to improve or optimize systems.
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Hands-On Simulation Modeling with Python. Develop simulation models to get accurate results and enhance decision-making processes Hands-On Simulation Modeling with Python. Develop simulation models to get accurate results and enhance decision-making processes
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The main aim of this book is to make the advanced mathematical background accessible to someone with a programming background. This book will equip the readers with not only deep learning architectures but the mathematics behind them. With this book, you will understand the relevant mathematics that goes behind building deep learning models.
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Hands-On Mathematics for Deep Learning. Build a solid mathematical foundation for training efficient deep neural networks Hands-On Mathematics for Deep Learning. Build a solid mathematical foundation for training efficient deep neural networks
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This book will help you explore how to implement different well-known machine learning algorithms with various C++ frameworks and libraries. You will cover basic to advanced machine learning concepts with practical and easy to follow examples. By the end of the book, you will be able to build various machine learning models with ease.
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Hands-On Machine Learning with C++. Build, train, and deploy end-to-end machine learning and deep learning pipelines Hands-On Machine Learning with C++. Build, train, and deploy end-to-end machine learning and deep learning pipelines
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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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With this book, you will learn to build ML.NET applications by exploring various machine learning models using C# code. You will cover basic to advanced level topics using step by step explanation and example-driven approach. You will learn about Integrating TensorFlow in ML.NET application to extend the capabilities of machine learning models.
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Hands-On Machine Learning with ML.NET. Getting started with Microsoft ML.NET to implement popular machine learning algorithms in C# Hands-On Machine Learning with ML.NET. Getting started with Microsoft ML.NET to implement popular machine learning algorithms in C#
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Taking an engaging and practical approach, The Supervised Learning Workshop teaches you how to predict the output of new data, based on the relationship and behavior of?existing datasets. You’ll learn at your own pace and use Python libraries and Jupyter to build intelligent predictive models.?
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The Supervised Learning Workshop. Predict outcomes from data by building your own powerful predictive models with machine learning in Python - Second Edition The Supervised Learning Workshop. Predict outcomes from data by building your own powerful predictive models with machine learning in Python - Second Edition
Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston, Ishita Mathur
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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 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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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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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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Machine Learning for OpenCV 4, Second Edition will help the readers to implement and train machine learning algorithms with OpenCV 4 and scikit-learn in Python. By the end of this book, you will be able to build intelligent applications with OpenCV 4 using various optimization techniques for your machine learning algorithms.
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Machine Learning for OpenCV 4. Intelligent algorithms for building image processing apps using OpenCV 4, Python, and scikit-learn - Second Edition Machine Learning for OpenCV 4. Intelligent algorithms for building image processing apps using OpenCV 4, Python, and scikit-learn - Second Edition
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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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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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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