Book Image

Machine Learning Using TensorFlow Cookbook

By : Luca Massaron, Alexia Audevart, Konrad Banachewicz
Book Image

Machine Learning Using TensorFlow Cookbook

By: Luca Massaron, Alexia Audevart, Konrad Banachewicz

Overview of this book

The independent recipes in Machine Learning Using TensorFlow Cookbook will teach you how to perform complex data computations and gain valuable insights into your data. Dive into recipes on training models, model evaluation, sentiment analysis, regression analysis, artificial neural networks, and deep learning - each using Google’s machine learning library, TensorFlow. This cookbook covers the fundamentals of the TensorFlow library, including variables, matrices, and various data sources. You’ll discover real-world implementations of Keras and TensorFlow and learn how to use estimators to train linear models and boosted trees, both for classification and regression. Explore the practical applications of a variety of deep learning architectures, such as recurrent neural networks and Transformers, and see how they can be used to solve computer vision and natural language processing (NLP) problems. With the help of this book, you will be proficient in using TensorFlow, understand deep learning from the basics, and be able to implement machine learning algorithms in real-world scenarios.
Table of Contents (15 chapters)
5
Boosted Trees
11
Reinforcement Learning with TensorFlow and TF-Agents
13
Other Books You May Enjoy
14
Index

Implementing operational gates

One of the most fundamental concepts of neural networks is its functioning as an operational gate. In this section, we will start with a multiplication operation as a gate, before moving on to consider nested gate operations.

Getting ready

The first operational gate we will implement is f(x) = a · x:

To optimize this gate, we declare the a input as a variable and x as the input tensor of our model. This means that TensorFlow will try to change the a value and not the x value. We will create the loss function as the difference between the output and the target value, which is 50.

The second, nested, operational gate will be f(x) = a · x + b:

Again, we will declare a and b as variables and x as the input tensor of our model. We optimize the output toward the target value of 50 again. The interesting thing to note is that the solution for this...