Learn Tensorflow
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Learn Tensorflow 2 0
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Author : Pramod Singh
language : en
Publisher: Apress
Release Date : 2019-12-17
Learn Tensorflow 2 0 written by Pramod Singh and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-17 with Computers categories.
Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0. It also demonstrates how to build models using customer estimators. Further, it explains how to use TensorFlow 2.0 API to build machine learning and deep learning models for image classification using the standard as well as custom parameters. You'll review sequence predictions, saving, serving, deploying, and standardized datasets, and then deploy these models to production. All the code presented in the book will be available in the form of executable scripts at Github which allows you to try out the examples and extend them in interesting ways. What You'll Learn Review the new features of TensorFlow 2.0 Use TensorFlow 2.0 to build machine learning and deep learning models Perform sequence predictions using TensorFlow 2.0 Deploy TensorFlow 2.0 models with practical examples Who This Book Is For Data scientists, machine and deep learning engineers.
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
Learn Tensorflow In 24 Hours
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Author : Alex Nordeen
language : en
Publisher: Guru99
Release Date : 2020-10-31
Learn Tensorflow In 24 Hours written by Alex Nordeen and has been published by Guru99 this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-31 with Computers categories.
Tensorflow is the most popular Deep Learning Library out there. It has fantastic graph computations feature which helps data scientist to visualize his designed neural network using TensorBoard. This Machine learning library supports both Convolution as well as Recurrent Neural network. It supports parallel processing on CPU as well as GPU. Prominent machine learning algorithms supported by TensorFlow are Deep Learning Classification, wipe & deep, Boston Tree amongst others. The book is very hands-on and gives you industry ready deep learnings practices. Here is what is covered in the book – Table Of Content Chapter 1: What is Deep learning? Chapter 2: Machine Learning vs Deep Learning Chapter 3: What is TensorFlow? Chapter 4: Comparison of Deep Learning Libraries Chapter 5: How to Download and Install TensorFlow Windows and Mac Chapter 6: Jupyter Notebook Tutorial Chapter 7: Tensorflow on AWS Chapter 8: TensorFlow Basics: Tensor, Shape, Type, Graph, Sessions & Operators Chapter 9: Tensorboard: Graph Visualization with Example Chapter 10: NumPy Chapter 11: Pandas Chapter 12: Scikit-Learn Chapter 13: Linear Regression Chapter 14: Linear Regression Case Study Chapter 15: Linear Classifier in TensorFlow Chapter 16: Kernel Methods Chapter 17: TensorFlow ANN (Artificial Neural Network) Chapter 18: ConvNet(Convolutional Neural Network): TensorFlow Image Classification Chapter 19: Autoencoder with TensorFlow Chapter 20: RNN(Recurrent Neural Network) TensorFlow
Hands On Machine Learning With Scikit Learn Keras And Tensorflow
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Author : Aurélien Géron
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2019-09-05
Hands On Machine Learning With Scikit Learn Keras And Tensorflow written by Aurélien Géron 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 2019-09-05 with Computers categories.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets
Tensorflow Reinforcement Learning Quick Start Guide
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Author : Kaushik Balakrishnan
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-03-30
Tensorflow Reinforcement Learning Quick Start Guide written by Kaushik Balakrishnan 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 2019-03-30 with Computers categories.
Leverage the power of Tensorflow to Create powerful software agents that can self-learn to perform real-world tasks Key FeaturesExplore efficient Reinforcement Learning algorithms and code them using TensorFlow and PythonTrain Reinforcement Learning agents for problems, ranging from computer games to autonomous driving.Formulate and devise selective algorithms and techniques in your applications in no time.Book Description Advances in reinforcement learning algorithms have made it possible to use them for optimal control in several different industrial applications. With this book, you will apply Reinforcement Learning to a range of problems, from computer games to autonomous driving. The book starts by introducing you to essential Reinforcement Learning concepts such as agents, environments, rewards, and advantage functions. You will also master the distinctions between on-policy and off-policy algorithms, as well as model-free and model-based algorithms. You will also learn about several Reinforcement Learning algorithms, such as SARSA, Deep Q-Networks (DQN), Deep Deterministic Policy Gradients (DDPG), Asynchronous Advantage Actor-Critic (A3C), Trust Region Policy Optimization (TRPO), and Proximal Policy Optimization (PPO). The book will also show you how to code these algorithms in TensorFlow and Python and apply them to solve computer games from OpenAI Gym. Finally, you will also learn how to train a car to drive autonomously in the Torcs racing car simulator. By the end of the book, you will be able to design, build, train, and evaluate feed-forward neural networks and convolutional neural networks. You will also have mastered coding state-of-the-art algorithms and also training agents for various control problems. What you will learnUnderstand the theory and concepts behind modern Reinforcement Learning algorithmsCode state-of-the-art Reinforcement Learning algorithms with discrete or continuous actionsDevelop Reinforcement Learning algorithms and apply them to training agents to play computer gamesExplore DQN, DDQN, and Dueling architectures to play Atari's Breakout using TensorFlowUse A3C to play CartPole and LunarLanderTrain an agent to drive a car autonomously in a simulatorWho this book is for Data scientists and AI developers who wish to quickly get started with training effective reinforcement learning models in TensorFlow will find this book very useful. Prior knowledge of machine learning and deep learning concepts (as well as exposure to Python programming) will be useful.
Learn Python From An Expert The Complete Guide With Artificial Intelligence
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Author : Edson L P Camacho
language : en
Publisher:
Release Date : 2023-06-08
Learn Python From An Expert The Complete Guide With Artificial Intelligence written by Edson L P Camacho and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-08 with Computers categories.
The Ultimate Guide to Advanced Python and Artificial Intelligence: Unleash the Power of Code! Are you ready to take your Python programming skills to the next level and dive into the exciting world of artificial intelligence? Look no further! We proudly present the comprehensive book written by renowned author Edson L P Camacho: "Advanced Python: Mastering AI." In today's rapidly evolving technological landscape, the demand for AI professionals is soaring. Python, with its simplicity and versatility, has become the go-to language for AI development. Whether you are a seasoned Pythonista or a beginner eager to learn, this book is your gateway to mastering AI concepts and enhancing your programming expertise. What sets "Advanced Python: Mastering AI" apart from other books is its unparalleled combination of in-depth theory and hands-on practicality. Edson L P Camacho, a leading expert in the field, guides you through every step, from laying the foundation of Python fundamentals to implementing cutting-edge AI algorithms. Here's a glimpse of what you'll find within the pages of this comprehensive guide: 1. Python Fundamentals: Review and reinforce your knowledge of Python basics, including data types, control flow, functions, and object-oriented programming. Build a solid foundation to tackle complex AI concepts. 2. Data Manipulation and Visualization: Learn powerful libraries such as NumPy, Pandas, and Matplotlib to handle and analyze data. Understand how to preprocess and visualize data effectively for AI applications. 3. Machine Learning Essentials: Dive into the world of machine learning and explore popular algorithms like linear regression, decision trees, support vector machines, and neural networks. Discover how to train, evaluate, and optimize models for various tasks. 4. Deep Learning and Neural Networks: Delve deeper into neural networks, the backbone of modern AI. Gain insights into deep learning architectures, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Implement advanced techniques like transfer learning and generative models. 5. Natural Language Processing (NLP): Explore the fascinating field of NLP and learn how to process and analyze textual data using Python. Discover techniques like sentiment analysis, named entity recognition, and text generation. 6. Computer Vision: Unleash the power of Python for image and video analysis. Build computer vision applications using popular libraries like OpenCV and TensorFlow. Understand concepts like object detection, image segmentation, and image captioning. 7. Reinforcement Learning: Embark on the exciting journey of reinforcement learning. Master the fundamentals of Q-learning, policy gradients, and deep Q-networks. Create intelligent agents that can learn and make decisions in dynamic environments. "Advanced Python: Mastering AI" not only equips you with the theoretical knowledge but also provides numerous real-world examples and projects to reinforce your understanding. Each chapter is accompanied by practical exercises and coding challenges to sharpen your skills and boost your confidence. Don't miss the opportunity to stay ahead in this AI-driven era. Order your copy of "Advanced Python: Mastering AI" today and unlock the full potential of Python programming with artificial intelligence. Take your career to new heights and become a proficient AI developer. Get ready to write the code that shapes the future!
Tensorflow For Dummies
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Author : Matthew Scarpino
language : en
Publisher: John Wiley & Sons
Release Date : 2018-03-07
Tensorflow For Dummies written by Matthew Scarpino and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-07 with Computers categories.
Become a machine learning pro! Google TensorFlow has become the darling of financial firms and research organizations, but the technology can be intimidating and the learning curve is steep. Luckily, TensorFlow For Dummies is here to offer you a friendly, easy-to-follow book on the subject. Inside, you’ll find out how to write applications with TensorFlow, while also grasping the concepts underlying machine learning—all without ever losing your cool! Machine learning has become ubiquitous in modern society, and its applications include language translation, robotics, handwriting analysis, financial prediction, and image recognition. TensorFlow is Google's preeminent toolset for machine learning, and this hands-on guide makes it easy to understand, even for those without a background in artificial intelligence. Install TensorFlow on your computer Learn the fundamentals of statistical regression and neural networks Visualize the machine learning process with TensorBoard Perform image recognition with convolutional neural networks (CNNs) Analyze sequential data with recurrent neural networks (RNNs) Execute TensorFlow on mobile devices and the Google Cloud Platform (GCP) If you’re a manager or software developer looking to use TensorFlow for machine learning, this is the book you’ll want to have close by.
Learn Tensorflow
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Author : Diego Rodrigues
language : en
Publisher: StudioD21
Release Date : 2024-12-12
Learn Tensorflow written by Diego Rodrigues and has been published by StudioD21 this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-12 with Business & Economics categories.
LEARN TENSORFLOW Master AI Model Development with Scalability and Precision. From Fundamentals to Practical Applications. This comprehensive guide is aimed at developers and students who want to create robust, high-performance, and scalable solutions with TensorFlow. You will learn to apply deep learning efficiently, master data pipelines, build advanced models, and deploy them professionally into production. Includes: • Tensor manipulation and model structuring with Keras • Building and training CNNs, RNNs, Transformers, and GANs • Regularization techniques, hyperparameter tuning, and performance optimization • Practical implementation with tf.data, TensorBoard, and TensorFlow Lite • Deployment with TensorFlow Serving, IoT integration, and use of GPUs and TPUs • Real-world cases in NLP, computer vision, healthcare, and enterprise systems By the end, you'll be fully equipped to develop TensorFlow applications for critical scenarios and scalable environments with technical excellence. tensorflow, keras, deep learning, cnn, rnn, gpu, deployment, iot, scalable models
Introduction To Machine Learning
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Author : Aditya Tandon
language : en
Publisher: Book Bazooka
Release Date : 2019-11-28
Introduction To Machine Learning written by Aditya Tandon and has been published by Book Bazooka this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-28 with Computers categories.
Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and nonexpert readers in statistics, computer science, mathematics, and engineering.
Mastering Tensorflow 1 X
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Author : Armando Fandango
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-01-22
Mastering Tensorflow 1 X written by Armando Fandango 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 2018-01-22 with Computers categories.
Build, scale, and deploy deep neural network models using the star libraries in Python Key Features Delve into advanced machine learning and deep learning use cases using Tensorflow and Keras Build, deploy, and scale end-to-end deep neural network models in a production environment Learn to deploy TensorFlow on mobile, and distributed TensorFlow on GPU, Clusters, and Kubernetes Book Description TensorFlow is the most popular numerical computation library built from the ground up for distributed, cloud, and mobile environments. TensorFlow represents the data as tensors and the computation as graphs. This book is a comprehensive guide that lets you explore the advanced features of TensorFlow 1.x. Gain insight into TensorFlow Core, Keras, TF Estimators, TFLearn, TF Slim, Pretty Tensor, and Sonnet. Leverage the power of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Throughout the book, you will obtain hands-on experience with varied datasets, such as MNIST, CIFAR-10, PTB, text8, and COCO-Images. You will learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF Clusters, deploy production models with TensorFlow Serving, and build and deploy TensorFlow models for mobile and embedded devices on Android and iOS platforms. You will see how to call TensorFlow and Keras API within the R statistical software, and learn the required techniques for debugging when the TensorFlow API-based code does not work as expected. The book helps you obtain in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems. By the end of this guide, you will have mastered the offerings of TensorFlow and Keras, and gained the skills you need to build smarter, faster, and efficient machine learning and deep learning systems. What you will learn Master advanced concepts of deep learning such as transfer learning, reinforcement learning, generative models and more, using TensorFlow and Keras Perform supervised (classification and regression) and unsupervised (clustering) learning to solve machine learning tasks Build end-to-end deep learning (CNN, RNN, and Autoencoders) models with TensorFlow Scale and deploy production models with distributed and high-performance computing on GPU and clusters Build TensorFlow models to work with multilayer perceptrons using Keras, TFLearn, and R Learn the functionalities of smart apps by building and deploying TensorFlow models on iOS and Android devices Supercharge TensorFlow with distributed training and deployment on Kubernetes and TensorFlow Clusters Who this book is for This book is for data scientists, machine learning engineers, artificial intelligence engineers, and for all TensorFlow users who wish to upgrade their TensorFlow knowledge and work on various machine learning and deep learning problems. If you are looking for an easy-to-follow guide that underlines the intricacies and complex use cases of machine learning, you will find this book extremely useful. Some basic understanding of TensorFlow is required to get the most out of the book.