Tensorflow Machine Learning Cookbook

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Tensorflow Machine Learning Cookbook

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Author : Nick McClure
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-02-14

Tensorflow Machine Learning Cookbook written by Nick McClure and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-02-14 with Computers categories.

Explore machine learning concepts using the latest numerical computing library — TensorFlow — with the help of this comprehensive cookbook About This Book Your quick guide to implementing TensorFlow in your day-to-day machine learning activities Learn advanced techniques that bring more accuracy and speed to machine learning Upgrade your knowledge to the second generation of machine learning with this guide on TensorFlow Who This Book Is For This book is ideal for data scientists who are familiar with C++ or Python and perform machine learning activities on a day-to-day basis. Intermediate and advanced machine learning implementers who need a quick guide they can easily navigate will find it useful. What You Will Learn Become familiar with the basics of the TensorFlow machine learning library Get to know Linear Regression techniques with TensorFlow Learn SVMs with hands-on recipes Implement neural networks and improve predictions Apply NLP and sentiment analysis to your data Master CNN and RNN through practical recipes Take TensorFlow into production In Detail TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You'll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google's machine learning library TensorFlow. This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP. Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production. Style and approach This book takes a recipe-based approach where every topic is explicated with the help of a real-world example.

Tensorflow Machine Learning Cookbook

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Author : Nick McClure
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-08-31

Tensorflow Machine Learning Cookbook written by Nick McClure and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-31 with Computers categories.

Skip the theory and get the most out of Tensorflow to build production-ready machine learning models Key Features Exploit the features of Tensorflow to build and deploy machine learning models Train neural networks to tackle real-world problems in Computer Vision and NLP Handy techniques to write production-ready code for your Tensorflow models Book Description TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and allow you to dig deeper and gain more insights into your data than ever before. With the help of this book, you will work with recipes for training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and more. You will explore RNNs, CNNs, GANs, reinforcement learning, and capsule networks, each using Google's machine learning library, TensorFlow. Through real-world examples, you will get hands-on experience with linear regression techniques with TensorFlow. Once you are familiar and comfortable with the TensorFlow ecosystem, you will be shown how to take it to production. By the end of the book, you will be proficient in the field of machine intelligence using TensorFlow. You will also have good insight into deep learning and be capable of implementing machine learning algorithms in real-world scenarios. What you will learn Become familiar with the basic features of the TensorFlow library Get to know Linear Regression techniques with TensorFlow Learn SVMs with hands-on recipes Implement neural networks to improve predictive modeling Apply NLP and sentiment analysis to your data Master CNN and RNN through practical recipes Implement the gradient boosted random forest to predict housing prices Take TensorFlow into production Who this book is for If you are a data scientist or a machine learning engineer with some knowledge of linear algebra, statistics, and machine learning, this book is for you. If you want to skip the theory and build production-ready machine learning models using Tensorflow without reading pages and pages of material, this book is for you. Some background in Python programming is assumed.

Tensorflow 2 Reinforcement Learning Cookbook

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Author : Praveen Palanisamy
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-01-15

Tensorflow 2 Reinforcement Learning Cookbook written by Praveen Palanisamy and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-15 with Computers categories.

Discover recipes for developing AI applications to solve a variety of real-world business problems using reinforcement learning Key FeaturesDevelop and deploy deep reinforcement learning-based solutions to production pipelines, products, and servicesExplore popular reinforcement learning algorithms such as Q-learning, SARSA, and the actor-critic methodCustomize and build RL-based applications for performing real-world tasksBook Description With deep reinforcement learning, you can build intelligent agents, products, and services that can go beyond computer vision or perception to perform actions. TensorFlow 2.x is the latest major release of the most popular deep learning framework used to develop and train deep neural networks (DNNs). This book contains easy-to-follow recipes for leveraging TensorFlow 2.x to develop artificial intelligence applications. Starting with an introduction to the fundamentals of deep reinforcement learning and TensorFlow 2.x, the book covers OpenAI Gym, model-based RL, model-free RL, and how to develop basic agents. You'll discover how to implement advanced deep reinforcement learning algorithms such as actor-critic, deep deterministic policy gradients, deep-Q networks, proximal policy optimization, and deep recurrent Q-networks for training your RL agents. As you advance, you’ll explore the applications of reinforcement learning by building cryptocurrency trading agents, stock/share trading agents, and intelligent agents for automating task completion. Finally, you'll find out how to deploy deep reinforcement learning agents to the cloud and build cross-platform apps using TensorFlow 2.x. By the end of this TensorFlow book, you'll have gained a solid understanding of deep reinforcement learning algorithms and their implementations from scratch. What you will learnBuild deep reinforcement learning agents from scratch using the all-new TensorFlow 2.x and Keras APIImplement state-of-the-art deep reinforcement learning algorithms using minimal codeBuild, train, and package deep RL agents for cryptocurrency and stock tradingDeploy RL agents to the cloud and edge to test them by creating desktop, web, and mobile apps and cloud servicesSpeed up agent development using distributed DNN model trainingExplore distributed deep RL architectures and discover opportunities in AIaaS (AI as a Service)Who this book is for The book is for machine learning application developers, AI and applied AI researchers, data scientists, deep learning practitioners, and students with a basic understanding of reinforcement learning concepts who want to build, train, and deploy their own reinforcement learning systems from scratch using TensorFlow 2.x.

Machine Learning Using Tensorflow Cookbook

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Author : Alexia Audevart
language : en
Publisher:
Release Date : 2021-02-05

Machine Learning Using Tensorflow Cookbook written by Alexia Audevart and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-05 with categories.

Master TensorFlow to create powerful machine learning algorithms, with valuable insights on Keras, Boosted Trees, Tabular Data, Transformers, Reinforcement Learning and more Key Features Work with the latest code and examples for TensorFlow 2 Get to grips with the fundamentals including variables, matrices, and data sources Learn advanced deep learning techniques to make your algorithms faster and more accurate Book Description The independent recipes in the Machine Learning Using TensorFlow Cookbook will teach you how to perform complex data computations and gain valuable insights into your data. You will work through recipes on training models, model evaluation, sentiment analysis, regression analysis, artificial neural networks, and deep learning - each using Google's machine learning library, TensorFlow. This cookbook begins by introducing you to the fundamentals of the TensorFlow library, including variables, matrices, and various data sources. You'll then take a deep dive into some real-world implementations of Keras and TensorFlow and learn how to use estimators to train linear models and boosted trees, both for classification and for regression to provide a baseline for tabular data problems. As you progress, you'll explore the practical applications of a variety of deep learning architectures, such as recurrent neural networks and Transformers, and see how they can be applied to computer vision and natural language processing (NLP) problems. Once you are familiar with the TensorFlow ecosystem, the final chapter will teach you how to take a project to production. By the end of this machine learning book, you will be proficient in using TensorFlow 2. You'll also understand deep learning from the fundamentals and be able to implement machine learning algorithms in real-world scenarios. What You Will Learn Grasp Linear Regression techniques with TensorFlow Use Estimators to train linear models and boosted trees for classification or regression Execute neural networks and improve predictions on tabular data Master convolutional neural networks and recurrent neural networks through practical recipes Apply reinforcement learning algorithms using the TF-agents framework Implement and fine-tune Transformer models for various NLP tasks Take TensorFlow into production Who this book is for If you are a data scientist or a machine learning engineer, and you want to skip detailed theoretical explanations in favor of building production-ready machine learning models using TensorFlow, this book is for you. Basic familiarity with Python, linear algebra, statistics, and machine learning is necessary to make the most out of this book.

Tensorflow 1 X Deep Learning Cookbook

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Author : Antonio Gulli
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-12-12

Tensorflow 1 X Deep Learning Cookbook written by Antonio Gulli and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-12 with Computers categories.

Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x About This Book Skill up and implement tricky neural networks using Google's TensorFlow 1.x An easy-to-follow guide that lets you explore reinforcement learning, GANs, autoencoders, multilayer perceptrons and more. Hands-on recipes to work with Tensorflow on desktop, mobile, and cloud environment Who This Book Is For This book is intended for data analysts, data scientists, machine learning practitioners and deep learning enthusiasts who want to perform deep learning tasks on a regular basis and are looking for a handy guide they can refer to. People who are slightly familiar with neural networks, and now want to gain expertise in working with different types of neural networks and datasets, will find this book quite useful. What You Will Learn Install TensorFlow and use it for CPU and GPU operations Implement DNNs and apply them to solve different AI-driven problems. Leverage different data sets such as MNIST, CIFAR-10, and Youtube8m with TensorFlow and learn how to access and use them in your code. Use TensorBoard to understand neural network architectures, optimize the learning process, and peek inside the neural network black box. Use different regression techniques for prediction and classification problems Build single and multilayer perceptrons in TensorFlow Implement CNN and RNN in TensorFlow, and use it to solve real-world use cases. Learn how restricted Boltzmann Machines can be used to recommend movies. Understand the implementation of Autoencoders and deep belief networks, and use them for emotion detection. Master the different reinforcement learning methods to implement game playing agents. GANs and their implementation using TensorFlow. In Detail Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. The entire world is filled with excitement about how deep networks are revolutionizing artificial intelligence. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve the real-life problems in artificial intelligence domain. In this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning. You will implement different deep learning networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs) with easy to follow independent recipes. You will learn how to make Keras as backend with TensorFlow. With a problem-solution approach, you will understand how to implement different deep neural architectures to carry out complex tasks at work. You will learn the performance of different DNNs on some popularly used data sets such as MNIST, CIFAR-10, Youtube8m, and more. You will not only learn about the different mobile and embedded platforms supported by TensorFlow but also how to set up cloud platforms for deep learning applications. Get a sneak peek of TPU architecture and how they will affect DNN future. By using crisp, no-nonsense recipes, you will become an expert in implementing deep learning techniques in growing real-world applications and research areas such as reinforcement learning, GANs, autoencoders and more. Style and approach This book consists of hands-on recipes where you'll deal with real-world problems. You'll execute a series of tasks as you walk through data mining challenges using TensorFlow 1.x. Your one-stop solution for common and not-so-common pain points, this is a book that you must have on the shelf.

Deep Learning Cookbook

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Author : Douwe Osinga
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2018-06-05

Deep Learning Cookbook written by Douwe Osinga and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-05 with Computers categories.

Deep learning doesn’t have to be intimidating. Until recently, this machine-learning method required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a background in machine learning can quickly enter the field. With the recipes in this cookbook, you’ll learn how to solve deep-learning problems for classifying and generating text, images, and music. Each chapter consists of several recipes needed to complete a single project, such as training a music recommending system. Author Douwe Osinga also provides a chapter with half a dozen techniques to help you if you’re stuck. Examples are written in Python with code available on GitHub as a set of Python notebooks. You’ll learn how to: Create applications that will serve real users Use word embeddings to calculate text similarity Build a movie recommender system based on Wikipedia links Learn how AIs see the world by visualizing their internal state Build a model to suggest emojis for pieces of text Reuse pretrained networks to build an inverse image search service Compare how GANs, autoencoders and LSTMs generate icons Detect music styles and index song collections

Machine Learning Using Tensorflow Cookbook

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Author : Alexia Audevart
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-02-08

Machine Learning Using Tensorflow Cookbook written by Alexia Audevart and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-08 with Mathematics categories.

Comprehensive recipes to give you valuable insights on Transformers, Reinforcement Learning, and more Key FeaturesDeep Learning solutions from Kaggle Masters and Google Developer ExpertsGet to grips with the fundamentals including variables, matrices, and data sourcesLearn advanced techniques to make your algorithms faster and more accurateBook Description The independent recipes in Machine Learning Using TensorFlow Cookbook will teach you how to perform complex data computations and gain valuable insights into your data. Dive into recipes on training models, model evaluation, sentiment analysis, regression analysis, artificial neural networks, and deep learning - each using Google’s machine learning library, TensorFlow. This cookbook covers the fundamentals of the TensorFlow library, including variables, matrices, and various data sources. You’ll discover real-world implementations of Keras and TensorFlow and learn how to use estimators to train linear models and boosted trees, both for classification and regression. Explore the practical applications of a variety of deep learning architectures, such as recurrent neural networks and Transformers, and see how they can be used to solve computer vision and natural language processing (NLP) problems. With the help of this book, you will be proficient in using TensorFlow, understand deep learning from the basics, and be able to implement machine learning algorithms in real-world scenarios. What you will learnTake TensorFlow into productionImplement and fine-tune Transformer models for various NLP tasksApply reinforcement learning algorithms using the TF-Agents frameworkUnderstand linear regression techniques and use Estimators to train linear modelsExecute neural networks and improve predictions on tabular dataMaster convolutional neural networks and recurrent neural networks through practical recipesWho this book is for If you are a data scientist or a machine learning engineer, and you want to skip detailed theoretical explanations in favor of building production-ready machine learning models using TensorFlow, this book is for you. Basic familiarity with Python, linear algebra, statistics, and machine learning is necessary to make the most out of this book.

Pytorch 1 X Reinforcement Learning Cookbook

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Author : Yuxi (Hayden) Liu
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-10-31

Pytorch 1 X Reinforcement Learning Cookbook written by Yuxi (Hayden) Liu and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-31 with Computers categories.

Implement reinforcement learning techniques and algorithms with the help of real-world examples and recipes Key FeaturesUse PyTorch 1.x to design and build self-learning artificial intelligence (AI) modelsImplement RL algorithms to solve control and optimization challenges faced by data scientists todayApply modern RL libraries to simulate a controlled environment for your projectsBook Description Reinforcement learning (RL) is a branch of machine learning that has gained popularity in recent times. It allows you to train AI models that learn from their own actions and optimize their behavior. PyTorch has also emerged as the preferred tool for training RL models because of its efficiency and ease of use. With this book, you'll explore the important RL concepts and the implementation of algorithms in PyTorch 1.x. The recipes in the book, along with real-world examples, will help you master various RL techniques, such as dynamic programming, Monte Carlo simulations, temporal difference, and Q-learning. You'll also gain insights into industry-specific applications of these techniques. Later chapters will guide you through solving problems such as the multi-armed bandit problem and the cartpole problem using the multi-armed bandit algorithm and function approximation. You'll also learn how to use Deep Q-Networks to complete Atari games, along with how to effectively implement policy gradients. Finally, you'll discover how RL techniques are applied to Blackjack, Gridworld environments, internet advertising, and the Flappy Bird game. By the end of this book, you'll have developed the skills you need to implement popular RL algorithms and use RL techniques to solve real-world problems. What you will learnUse Q-learning and the state–action–reward–state–action (SARSA) algorithm to solve various Gridworld problemsDevelop a multi-armed bandit algorithm to optimize display advertisingScale up learning and control processes using Deep Q-NetworksSimulate Markov Decision Processes, OpenAI Gym environments, and other common control problemsSelect and build RL models, evaluate their performance, and optimize and deploy themUse policy gradient methods to solve continuous RL problemsWho this book is for Machine learning engineers, data scientists and AI researchers looking for quick solutions to different reinforcement learning problems will find this book useful. Although prior knowledge of machine learning concepts is required, experience with PyTorch will be useful but not necessary.

Machine Learning With Python Cookbook

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Author : Chris Albon
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2018-03-09

Machine Learning With Python Cookbook written by Chris Albon and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-09 with Computers categories.

This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics. Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications. You’ll find recipes for: Vectors, matrices, and arrays Handling numerical and categorical data, text, images, and dates and times Dimensionality reduction using feature extraction or feature selection Model evaluation and selection Linear and logical regression, trees and forests, and k-nearest neighbors Support vector machines (SVM), naïve Bayes, clustering, and neural networks Saving and loading trained models

Learning Tensorflow

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Author : Tom Hope
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-08-09

Learning Tensorflow written by Tom Hope and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-09 with Computers categories.

Roughly inspired by the human brain, deep neural networks trained with large amounts of data can solve complex tasks with unprecedented accuracy. This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for computer vision, natural language processing (NLP), speech recognition, and general predictive analytics. Authors Tom Hope, Yehezkel Resheff, and Itay Lieder provide a hands-on approach to TensorFlow fundamentals for a broad technical audience—from data scientists and engineers to students and researchers. You’ll begin by working through some basic examples in TensorFlow before diving deeper into topics such as neural network architectures, TensorBoard visualization, TensorFlow abstraction libraries, and multithreaded input pipelines. Once you finish this book, you’ll know how to build and deploy production-ready deep learning systems in TensorFlow. Get up and running with TensorFlow, rapidly and painlessly Learn how to use TensorFlow to build deep learning models from the ground up Train popular deep learning models for computer vision and NLP Use extensive abstraction libraries to make development easier and faster Learn how to scale TensorFlow, and use clusters to distribute model training Deploy TensorFlow in a production setting