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

How TensorFlow works

Started as an internal project by researchers and engineers from the Google Brain team, initially named DistBelief, an open source framework for high performance numerical computations was released in November 2015 under the name TensorFlow (tensors are a generalization of scalars, vectors, matrices, and higher dimensionality matrices). You can read the original paper on the project here: http://download.tensorflow.org/paper/whitepaper2015.pdf. After the appearance of version 1.0 in 2017, last year, Google released TensorFlow 2.0, which continues the development and improvement of TensorFlow by making it more user-friendly and accessible.

Production-oriented and capable of handling different computational architectures (CPUs, GPUs, and now TPUs), TensorFlow is a framework for any kind of computation that requires high performance and easy distribution. It excels at deep learning, making it possible to create everything from shallow networks (neural networks...