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TensorFlow Machine Learning Cookbook
TensorFlow Machine Learning Cookbook

TensorFlow Machine Learning Cookbook: Over 60 recipes to build intelligent machine learning systems with the power of Python , Second Edition

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Profile Icon Nick McClure Profile Icon Sujit Pal
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€8.99 €23.99
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2 (3 Ratings)
eBook Aug 2018 422 pages 2nd Edition
eBook
€8.99 €23.99
Paperback
€29.99
Subscription
Free Trial
Renews at €18.99p/m
Arrow left icon
Profile Icon Nick McClure Profile Icon Sujit Pal
Arrow right icon
€8.99 €23.99
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2 (3 Ratings)
eBook Aug 2018 422 pages 2nd Edition
eBook
€8.99 €23.99
Paperback
€29.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€8.99 €23.99
Paperback
€29.99
Subscription
Free Trial
Renews at €18.99p/m

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TensorFlow Machine Learning Cookbook

Getting Started with TensorFlow

In this chapter, we will cover some basic recipes in order to understand how TensorFlow works and how to access data for this book and additional resources.

By the end of this chapter, you should have knowledge of the following:

  • How TensorFlow works
  • Declaring variables and tensors
  • Using placeholders and variables
  • Working with matrices
  • Declaring operations
  • Implementing activation functions
  • Working with data sources
  • Additional resources

Introduction

Google's TensorFlow engine has a unique way of solving problems. This unique way allows us to solve machine learning problems very efficiently. Machine learning is used in almost all areas of life and work, but some of the more famous areas are computer vision, speech recognition, language translations, healthcare, and many more. We will cover the basic steps to understand how TensorFlow operates and eventually build up to production code techniques later in this book. These fundamentals are important to understanding recipes for the rest of this book.

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Key benefits

  • 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

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.

Who is this book 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.

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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Aug 31, 2018
Length: 422 pages
Edition : 2nd
Language : English
ISBN-13 : 9781789130768
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Product Details

Publication date : Aug 31, 2018
Length: 422 pages
Edition : 2nd
Language : English
ISBN-13 : 9781789130768
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Table of Contents

12 Chapters
Getting Started with TensorFlow Chevron down icon Chevron up icon
The TensorFlow Way Chevron down icon Chevron up icon
Linear Regression Chevron down icon Chevron up icon
Support Vector Machines Chevron down icon Chevron up icon
Nearest-Neighbor Methods Chevron down icon Chevron up icon
Neural Networks Chevron down icon Chevron up icon
Natural Language Processing Chevron down icon Chevron up icon
Convolutional Neural Networks Chevron down icon Chevron up icon
Recurrent Neural Networks Chevron down icon Chevron up icon
Taking TensorFlow to Production Chevron down icon Chevron up icon
More with TensorFlow Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
(3 Ratings)
5 star 0%
4 star 0%
3 star 33.3%
2 star 33.3%
1 star 33.3%
science-dude Feb 29, 2024
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
Sadly this book is a bit out of date when Tensorflow 2 was released and Tensorflow 1 made obsolete.
Amazon Verified review Amazon
Stephen P. Owens Jul 31, 2022
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
For starters the book is dated, and the code examples are no longer functional so the first thing I had to do was go in and fix all the code before I could run the examples. The second thing is that this book really doesn't explain or teach anything, you could save the money on the book and go to their Github website download all the example code, fix it till it works and then read the code, you will get just as much out of that as you will by the useless info they add to the code base within the book.
Amazon Verified review Amazon
Amazon Customer Feb 15, 2019
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Purchased the latest Edition as I read the first one had lot of errors and typos.When I read around first 50 pages of the book, there were around many typos and more than 8 syntactical errors.Right now I have even stopped counting. The language is very poor. Very poorly written. It miserably fails to explain the concepts. I have read lots of other books on DL but honestly this is the worst book ever. Even if you go to the github repository of this book to refer codes, you will find two versions, one is .ipynb and the other one is .py. Even those files have errors. I don't know how they published the books with out sorting out all these problems.I have finished many DL books within 2-3 weeks because they felt really interesting. Examples: Deep Learning with python, Deep Learning BookBut right now I don't even feel like reading the book because the author has has made simple things way too complicated.Example: we can split data into train n test using sklearn train_test_split. Instead the author is writing 4 lines of code to split them every time. Why man why???I would recommend to go for some other book
Amazon Verified review Amazon
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