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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Deep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision, the science of manipulating and processing images. In this book, you will learn different techniques in deep learning to accomplish tasks related to object classification, object detection, image segmentation, captioning, image
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Deep Learning for Computer Vision. Expert techniques to train advanced neural networks using TensorFlow and Keras Deep Learning for Computer Vision. Expert techniques to train advanced neural networks using TensorFlow and Keras
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We cover advanced deep learning concepts (such as transfer learning, generative adversarial models, and reinforcement learning), and implement them using TensorFlow and Keras. We cover how to build and deploy at scale with distributed models. You will learn to build TensorFlow models using R, Keras, TensorFlow Learn, TensorFlow Slim and Sonnet
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Mastering TensorFlow 1.x. Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras Mastering TensorFlow 1.x. Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras
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Computer vision is found everywhere in modern technology. OpenCV for Python enables us to run computer vision algorithms in real time. With the advent of powerful machines, we have more processing power to work with. Using this technology, we can seamlessly integrate our computer vision applications into the cloud. Focusing on OpenCV 3.x and Python
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OpenCV 3.x with Python By Example. Make the most of OpenCV and Python to build applications for object recognition and augmented reality - Second Edition OpenCV 3.x with Python By Example. Make the most of OpenCV and Python to build applications for object recognition and augmented reality - Second Edition
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Developers have been using OpenCV library to develop computer vision applications for a long time. However, they now need a more effective tool to get the job done and in a much better and modern way. Qt is one of the major frameworks available for this task at the moment. This book will teach you to develop applications with the combination of Ope
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Computer Vision with OpenCV 3 and Qt5. Build visually appealing, multithreaded, cross-platform computer vision applications Computer Vision with OpenCV 3 and Qt5. Build visually appealing, multithreaded, cross-platform computer vision applications
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The book is designed in such a way that you can explore it chapter-by-chapter or skip any chapter without missing a beat. If you are a network operator and want to use Cacti for implementing performance measurement for trending, troubleshooting, and reporting purposes, then this book is for you. You only need to know the basics of network managemen
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Cacti Beginner's Guide. Leverage Cacti to design a robust network operations center - Second Edition Cacti Beginner's Guide. Leverage Cacti to design a robust network operations center - Second Edition
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Deep Neural Networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. In this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning, and implement different deep learning networks with easy to follow independent recipes.
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TensorFlow 1.x Deep Learning Cookbook. Over 90 unique recipes to solve artificial-intelligence driven problems with Python TensorFlow 1.x Deep Learning Cookbook. Over 90 unique recipes to solve artificial-intelligence driven problems with Python
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Statistics is an absolute must prerequisite for any task in the area of data science. This is also the most feared hindrance for developers to entering in the data science field. This book will start off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for the statistical comput
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Statistics for Data Science. Leverage the power of statistics for Data Analysis, Classification, Regression, Machine Learning, and Neural Networks Statistics for Data Science. Leverage the power of statistics for Data Analysis, Classification, Regression, Machine Learning, and Neural Networks
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If you’re aware of the buzz surrounding the terms such as machine learning, artificial intelligence or deep learning, you might know what neural networks are. TensorFlow is a popular framework which can be used to implement efficient neural networks and deep learning models. This book will show you how to leverage the power of TensorFlow to
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Neural Network Programming with Tensorflow. Unleash the power of TensorFlow to train efficient neural networks Neural Network Programming with Tensorflow. Unleash the power of TensorFlow to train efficient neural networks
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Deep Learning is a rapidly evolving field of Machine Learning science which gives machines the ability to learn from information. This book contains detailed recipes to tackle with the common and not so common problems while dealing with deep learning algorithms and models in Python.You will benefit from this book by finding technical solutions to
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Python Deep Learning Cookbook. Over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python Python Deep Learning Cookbook. Over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python
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Microsoft has revamped its Project Oxford to launch the all new Cognitive Services platform-a set of 30 APIs to add speech, vision, language, and knowledge capabilities to apps. This book will introduce you to 24 of the APIs released as part of Cognitive Services platform and show you how to leverage their capabilities. More importantly, you'll see
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Learning Microsoft Cognitive Services. Leverage Machine Learning APIs to build smart applications - Second Edition Learning Microsoft Cognitive Services. Leverage Machine Learning APIs to build smart applications - Second Edition
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The R language is a powerful open source functional programming language. At its core, R is a statistical language that provides impressive tools to analyze data and create high-level graphics.This book covers the basics of R by setting up a user-friendly programming environment and programming ETL in R. Data exploration examples are provided that
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Machine Learning with R Cookbook. Analyze data and build predictive models - Second Edition Machine Learning with R Cookbook. Analyze data and build predictive models - Second Edition
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The book helps you learn neural networks and implement them in R. It covers real-world use cases that will help you better understand their concepts. A basic understanding of R and mathematics is required.
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Neural Networks with R. Build smart systems by implementing popular deep learning models in R Neural Networks with R. Build smart systems by implementing popular deep learning models in R
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Do you have a colleague who always seems to be organized, finishes tasks on time, meets every deadline, and that too with a smile on their face? If you ever wanted to become that IT developer/ manager/administrator who is not just a perfect employee, but also has brilliant balance and composure over their emotional intelligence, then this book is f
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Emotional Intelligence for IT Professionals. The must-have guide for a successful career in IT Emotional Intelligence for IT Professionals. The must-have guide for a successful career in IT
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The mission of this book is to turn readers into productive, innovative data analysts who leverage Go to build robust and valuable applications. To this end, the book clearly introduces the technical aspects of building predictive models in Go, but it also helps the reader understand how machine learning workflows are being applied in real world sc
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Machine Learning With Go. Implement Regression, Classification, Clustering, Time-series Models, Neural Networks, and More using the Go Programming Language Machine Learning With Go. Implement Regression, Classification, Clustering, Time-series Models, Neural Networks, and More using the Go Programming Language
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Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability, and optimization. This book begins with a quick overview of setting up the necessary IDEs to facilitate the execution of code examples that will be covered in various chapters. It also highlights some key issues develo
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Apache Spark 2.x Machine Learning Cookbook. Over 100 recipes to simplify machine learning model implementations with Spark Apache Spark 2.x Machine Learning Cookbook. Over 100 recipes to simplify machine learning model implementations with Spark
Siamak Amirghodsi, Shuen Mei, Meenakshi Rajendran, Broderick Hall
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This second edition of Python Machine Learning by Sebastian Raschka is for developers and data scientists looking for a practical approach to machine learning and deep learning. In this updated edition, you’ll explore the machine learning process using Python and the latest open source technologies, including scikit-learn and TensorFlow 1.x.
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Python Machine Learning. Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow - Second Edition Python Machine Learning. Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow - Second Edition
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MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will build a foundation for machine learning using MATLAB for beginners. It will also help you learn regression, clustering, classification, predictive analytics, artificial neural networks, and more with MATLAB.
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MATLAB for Machine Learning. Practical examples of regression, clustering and neural networks MATLAB for Machine Learning. Practical examples of regression, clustering and neural networks
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Deep Learning is the next big thing. It is a part of machine learning. Its favorable results in application with huge and complex data is remarkable. This book will help you to get through the problems that you face during the execution of different tasks and understand hacks in deep learning, neural networks, and advanced machine learning techniqu
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R Deep Learning Cookbook. Solve complex neural net problems with TensorFlow, H2O and MXNet R Deep Learning Cookbook. Solve complex neural net problems with TensorFlow, H2O and MXNet
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This book covers a complete overview of Deep Learning with Theano, a Python-based library that makes optimizing numerical expressions easy.Practical code examples address supervised, unsupervised, generative and reinforcement learning for image recognition, natural language processing, or game strategy, with best performing nets and principles.
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Deep Learning with Theano. Perform large-scale numerical and scientific computations efficiently Deep Learning with Theano. Perform large-scale numerical and scientific computations efficiently
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With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible. Dan Van Boxel is your guide to exploring the possibilities with deep learning; he will enable you to understand data like never before. With the efficiency and simplicity of TensorFlow, you will be able to process your data and ga
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Hands-On Deep Learning with TensorFlow. Uncover what is underneath your data! Hands-On Deep Learning with TensorFlow. Uncover what is underneath your data!
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Machine learning for OpenCV begins by introducing you to the essential concepts of statistical learning, such as classification and regression. Once all the basics are covered, you will start exploring various algorithms such as decision trees, support vector machines, and Bayesian networks, and learn how to combine them with other OpenCV functiona
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Machine Learning for OpenCV. Intelligent image processing with Python Machine Learning for OpenCV. Intelligent image processing with Python
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As we become more capable of handling data in every kind, we are becoming more reliant on visual input and what we can do with those self-driving cars, face recognition, and even augmented reality applications and games. This is all powered by Computer Vision.This book will put you straight to work in creating powerful and unique computer vision ap
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Mastering OpenCV 3. Get hands-on with practical Computer Vision using OpenCV 3 - Second Edition Mastering OpenCV 3. Get hands-on with practical Computer Vision using OpenCV 3 - Second Edition
Shervin Emami, David Millán Escrivá, Daniel Lelis Baggio, Roy Shilkrot, Eugene Khvedchenia, Jason Saragih
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Starting with a quick recap of important machine learning concepts, the book will delve straight into deep learning principles using Sci-kit learn. Moving ahead, you will learn to use the latest open source libraries such as Theano, Keras, Google's TensorFlow, and H20. Use this guide to uncover the difficulties of pattern recognition, scaling data
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Python Deep Learning. Next generation techniques to revolutionize computer vision, AI, speech and data analysis Python Deep Learning. Next generation techniques to revolutionize computer vision, AI, speech and data analysis
Valentino Zocca, Gianmario Spacagna, Daniel Slater, Peter Roelants
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Keras is a high-level neural network library written in Python that runs on top of either Theano or TensorFlow. With this book, you’ll learn the basics of Keras in a highly practical way and understand how this minimal, highly modular framework runs on both CPU and GPU, allowing you to put your ideas into action in the shortest possible time
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Deep Learning with Keras. Implementing deep learning models and neural networks with the power of Python Deep Learning with Keras. Implementing deep learning models and neural networks with the power of Python
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Machine learning is concerned with algorithms for transforming data into actionable intelligence and predictive analytics. Deep learning is a branch of machine learning based on multiple levels of representations. This book introduces the core concepts of deep learning using the latest version of TensorFlow to get implementation and research detail
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Deep Learning with TensorFlow. Explore neural networks with Python Deep Learning with TensorFlow. Explore neural networks with Python
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Create intelligent apps with vision, speech, language and knowledge capabilities using Microsoft Cognitive Services. Harness the power of all 21 APIs released as part of the platform, and discover how they can be combined to build real-word apps with cognitive capabilities. Learn how to easily add these features for smarter, more human-like applica
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Learning Microsoft Cognitive Services. Click here to enter text Learning Microsoft Cognitive Services. Click here to enter text
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This book is designed to guide you through TensorFlow and how to use it effectively. You’ll be able to apply it for complex data computations, gain insights into your data, and more. Throughout the book, you’ll work through the recipes and get hands-on experience.
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TensorFlow Machine Learning Cookbook. Over 60 practical recipes to help you master Google’s TensorFlow machine learning library TensorFlow Machine Learning Cookbook. Over 60 practical recipes to help you master Google’s TensorFlow machine learning library
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OpenCV 3 Computer Vision Application Programming Cookbook. Recipes to make your applications see - Third Edition OpenCV 3 Computer Vision Application Programming Cookbook. Recipes to make your applications see - Third Edition
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This Hyper-V book is designed to help advanced-level administrators benefit fully from the new Windows Server. With over 80 hands-on recipes, the Hyper-V Cookbook gives you tips, tricks and best practices to deploy, maintain and upgrade your virtual machines.
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Windows Server 2016 Hyper-V Cookbook. Save time and resources by getting to know the best practices and intelligence from industry experts - Second Edition Windows Server 2016 Hyper-V Cookbook. Save time and resources by getting to know the best practices and intelligence from industry experts - Second Edition
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Python: Deeper Insights into Machine Learning. Deeper Insights into Machine Learning Python: Deeper Insights into Machine Learning. Deeper Insights into Machine Learning
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Large Scale Machine Learning with Python. Click here to enter text Large Scale Machine Learning with Python. Click here to enter text
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Test-Driven Machine Learning. Control your machine learning algorithms using test-driven development to achieve quantifiable milestones Test-Driven Machine Learning. Control your machine learning algorithms using test-driven development to achieve quantifiable milestones
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Learning Bayesian Models with R. Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems Learning Bayesian Models with R. Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems
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Mastering Machine Learning with R. Master machine learning techniques with R to deliver insights for complex projects Mastering Machine Learning with R. Master machine learning techniques with R to deliver insights for complex projects
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Building a Recommendation System with R. Learn the art of building robust and powerful recommendation engines using R Building a Recommendation System with R. Learn the art of building robust and powerful recommendation engines using R
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Mastering Probabilistic Graphical Models Using Python. Master probabilistic graphical models by learning through real-world problems and illustrative code examples in Python Mastering Probabilistic Graphical Models Using Python. Master probabilistic graphical models by learning through real-world problems and illustrative code examples in Python
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This second edition provides focused and practical knowledge to help you to build algorithms and crunch your data. You'll learn how to apply machine learning methods to deal with common tasks, discover the analytical tools that you need to gain insights from complex data, and choose the correct algorithm for your specific needs.
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Machine Learning with R. Expert techniques for predictive modeling to solve all your data analysis problems - Second Edition Machine Learning with R. Expert techniques for predictive modeling to solve all your data analysis problems - Second Edition
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Apache Mahout Essentials. Implement top-notch machine learning algorithms for classification, clustering, and recommendations with Apache Mahout Apache Mahout Essentials. Implement top-notch machine learning algorithms for classification, clustering, and recommendations with Apache Mahout
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Microsoft Azure Machine Learning. Explore predictive analytics using step-by-step tutorials and build models to make prediction in a jiffy with a few mouse clicks Microsoft Azure Machine Learning. Explore predictive analytics using step-by-step tutorials and build models to make prediction in a jiffy with a few mouse clicks
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Learning Apache Mahout. Acquire practical skills in Big Data Analytics and explore data science with Apache Mahout Learning Apache Mahout. Acquire practical skills in Big Data Analytics and explore data science with Apache Mahout
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Scala for Machine Learning. Leverage Scala and Machine Learning to construct and study systems that can learn from data Scala for Machine Learning. Leverage Scala and Machine Learning to construct and study systems that can learn from data
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R Machine Learning Essentials. Gain quick access to the machine learning concepts and practical applications using the R development environment R Machine Learning Essentials. Gain quick access to the machine learning concepts and practical applications using the R development environment
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Machine Learning with R. R gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required – this book will take you methodically through every stage of applying machine learning Machine Learning with R. R gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required – this book will take you methodically through every stage of applying machine learning