tensorflow for machine intelligence

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Tensorflow For Machine Intelligence

Author : Sam Abrahams
ISBN : 1939902452
Genre : Artificial intelligence
File Size : 45. 78 MB
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Tensorflow For Machine Intelligence

Author : Sam Abrahams
ISBN : 1939902452
Genre : Artificial intelligence
File Size : 45. 22 MB
Format : PDF, ePub
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Tensorflow Machine Learning Cookbook

Author : Nick McClure
ISBN : 1786462168
Genre :
File Size : 32. 11 MB
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Cook up the machine learning recipe of your choice using the latest numerical computing library-TensorFlow-with the help of this easy-to-follow cookbookAbout 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 with the help of this cookbook* Upgrade yourself to the second generation of machine learning with this guide on TensorFlowWho This Book Is ForThis book caters to data scientists who are familiar with C++ or Python and perform machine learning activities on a day-to-day basis. This book is for both intermediate as well as advanced machine learning implementers who need a quick guide they can easily navigate through.What You Will Learn* Become familiar with the basics of the TensorFlow machine learning library* Get to know Linear Regression techniques with TensorFlow* Learn SVM's with practical hands-on recipes* Implement neural networks and improve predictions* Apply NLP and sentiment analysis to your data* Master CNN and RNN through real-world recipes* Take TensorFlow into productionIn DetailTensorFlow 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 help you 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.We'll start with the fundamentals of the TensorFlow library and you will learn about variables, matrices, and various data sources. Moving ahead, you will get hands-on experience of Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP through real-world examples in every recipe.Once you are well versed with the TensorFlow ecosystem, the last chapter will teach you to take it to production.

First Contact With Tensorflow

Author : Jordi Torres
ISBN : 9781326569334
Genre : Technology & Engineering
File Size : 25. 62 MB
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The purpose of this book is to help to spreadTensorFlowknowledge among engineers who want to expand their wisdom in the exciting world of Machine Learning. We believe that anyone with an engineering background might require from now on Deep Learning, an"

Getting Started With Tensorflow

Author : Giancarlo Zaccone
ISBN : 9781786469069
Genre : Computers
File Size : 67. 16 MB
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Get up and running with the latest numerical computing library by Google and dive deeper into your data! About This Book Get the first book on the market that shows you the key aspects TensorFlow, how it works, and how to use it for the second generation of machine learning Want to perform faster and more accurate computations in the field of data science? This book will acquaint you with an all-new refreshing library—TensorFlow! Dive into the next generation of numerical computing and get the most out of your data with this quick guide Who This Book Is For This book is dedicated to all the machine learning and deep learning enthusiasts, data scientists, researchers, and even students who want to perform more accurate, fast machine learning operations with TensorFlow. Those with basic knowledge of programming (Python and C/C++) and math concepts who want to be introduced to the topics of machine learning will find this book useful. What You Will Learn Install and adopt TensorFlow in your Python environment to solve mathematical problems Get to know the basic machine and deep learning concepts Train and test neural networks to fit your data model Make predictions using regression algorithms Analyze your data with a clustering procedure Develop algorithms for clustering and data classification Use GPU computing to analyze big data In Detail Google's TensorFlow engine, after much fanfare, has evolved in to a robust, user-friendly, and customizable, application-grade software library of machine learning (ML) code for numerical computation and neural networks. This book takes you through the practical software implementation of various machine learning techniques with TensorFlow. In the first few chapters, you'll gain familiarity with the framework and perform the mathematical operations required for data analysis. As you progress further, you'll learn to implement various machine learning techniques such as classification, clustering, neural networks, and deep learning through practical examples. By the end of this book, you'll have gained hands-on experience of using TensorFlow and building classification, image recognition systems, language processing, and information retrieving systems for your application. Style and approach Get quickly up and running with TensorFlow using this fast-paced guide. You will get to know everything that can be done with TensorFlow and we'll show you how to implement it in your environment. The examples in the book are from the core of the computation industry—something you can connect to and will find familiar.

Machine Learning With Tensorflow

Author : Nishant Shukla
ISBN : 1617293873
Genre : Computers
File Size : 36. 86 MB
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Being able to make near-real-time decisions is becoming increasingly crucial. To succeed, we need machine learning systems that can turn massive amounts of data into valuable insights. But when you're just starting out in the data science field, how do you get started creating machine learning applications? The answer is TensorFlow, a new open source machine learning library from Google. The TensorFlow library can take your high level designs and turn them into the low level mathematical operations required by machine learning algorithms. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. Machine Learning with TensorFlow teaches readers about machine learning algorithms and how to implement solutions with TensorFlow. It starts with an overview of machine learning concepts and moves on to the essentials needed to begin using TensorFlow. Each chapter zooms into a prominent example of machine learning. Readers can cover them all to master the basics or skip around to cater to their needs. By the end of this book, readers will be able to solve classification, clustering, regression, and prediction problems in the real world.

Hands On Machine Learning With Scikit Learn And Tensorflow

Author : Aurélien Géron
ISBN : 1491962291
Genre : Computers
File Size : 64. 71 MB
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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 Aur�lien G�ron 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 Apply practical code examples without acquiring excessive machine learning theory or algorithm details

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