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

Declaring variables and tensors

Tensors are the primary data structure that TensorFlow uses to operate on the computational graph. Even if now, in TensorFlow 2.x, this aspect is hidden, the data flow graph is still operating behind the scenes. This means that the logic of building a neural network doesn't change all that much between TensorFlow 1.x and TensorFlow 2.x. The most eye-catching aspect is that you no longer have to deal with placeholders, the previous entry gates for data in a TensorFlow 1.x graph.

Now, you simply declare tensors as variables and proceed to building your graph.

A tensor is a mathematical term that refers to generalized vectors or matrices. If vectors are one-dimensional and matrices are two-dimensional, a tensor is n-dimensional (where n could be 1, 2, or even larger).

We can declare these tensors as variables and use them for our computations. To do this, first, we must learn how to create tensors.

Getting ready

When...