Hands-On Deep Learning with TensorFlow

This is the code repository for Hands-On Deep Learning with TensorFlow, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.

About the Book

Dan Van Boxel’s Deep Learning with TensorFlow is based on Dan’s best-selling TensorFlow video course. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible. Dan Van Boxel will be your guide to exploring the possibilities with deep learning; he will enable you to understand data like never before. With the efficiency and simplicity of TensorFlow, you will be able to process your data and gain insights that will change how you look at data.

With Dan’s guidance, you will dig deeper into the hidden layers of abstraction using raw data. Dan then shows you various complex algorithms for deep learning and various examples that use these deep neural networks. You will also learn how to train your machine to craft new features to make sense of deeper layers of data.

In this book, Dan shares his knowledge across topics such as logistic regression, convolutional neural networks, recurrent neural networks, training deep networks, and high level interfaces. With the help of novel practical examples, you will become an ace at advanced multilayer networks, image recognition, and beyond.

Instructions and Navigation

All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.

The code will look like the following:

import tensorflow as tf
# You can create constants in TF to hold specific values
a = tf.constant(1)
b = tf.constant(2)

While this book will show you how to install TensorFlow, there are a few dependencies you need to be aware of. At a minimum, you need a recent version of Python 2 or 3 and NumPy. To get the most out of the book, you should also have Matplotlib and IPython.

grseb9s / hands-on-deep-learning-with-tensorflow Goto Github PK