This is the code repository for TensorFlow Machine Learning Cookbook, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.

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.

All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter 03.

The code will look like the following:

     import matplotlib.pyplot as plt
     import numpy as np
     import tensorflow as tf
     from sklearn import datasets
     from tensorflow.python.framework import ops
     ops.reset_default_graph()

Python 3, with the following installed Python libraries: TensorFlow, Numpy, Scikit-Learn, Requests, and Jupyter. It is compatible in all three major operating systems, Mac, Windows, and Linux. It requires no special hardware to run the scripts.

• Getting Started with TensorFlow

• Deep Learning with TensorFlow [Video]

• Building Machine Learning Systems with TensorFlow [Video]

###Suggestions and Feedback Click here if you have any feedback or suggestions.