Book Image

Deep Learning with TensorFlow 2 and Keras - Second Edition

By : Antonio Gulli, Amita Kapoor, Sujit Pal
Book Image

Deep Learning with TensorFlow 2 and Keras - Second Edition

By: Antonio Gulli, Amita Kapoor, Sujit Pal

Overview of this book

Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before. This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.
Table of Contents (19 chapters)
17
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18
Index

Some mathematical tools

Before introducing backpropagation, we need to review some mathematical tools from calculus. Don't worry too much; we'll briefly review a few areas, all of which are commonly covered in high school-level mathematics.

Derivatives and gradients everywhere

Derivatives are a powerful mathematical tool. We are going to use derivatives and gradients for optimizing our network. Let's look at the definition. The derivative of a function y = f(x) of a variable x is a measure of the rate at which the value y of the function changes with respect to the change of the variable x. If x and y are real numbers, and if the graph of f is plotted against x, the derivative is the "slope" of this graph at each point.

If the function is linear, y = f(x) = ax + b, the slope is . This is a simple result of calculus that can be derived by considering that:

In Figure 1 we show the geometrical meaning of , and the angle between the linear...