Machine Learning Using TensorFlow Cookbook

Master TensorFlow to create powerful machine learning algorithms, with valuable insights on Keras, Boosted Trees, Tabular Data, Transformers, Reinforcement Learning and more

Key Features
Work with the latest code and examples for TensorFlow 2
Get to grips with the fundamentals including variables, matrices, and data sources
Learn advanced deep learning techniques to make your algorithms faster and more accurate

What You Will Learn
Grasp linear regression techniques with TensorFlow
Use Estimators to train linear models and boosted trees for classification or regression
Execute neural networks and improve predictions on tabular data
Master convolutional neural networks and recurrent neural networks through practical recipes
Apply reinforcement learning algorithms using the TF-Agents framework
Implement and fine-tune Transformer models for various NLP tasks
Take TensorFlow into production

About
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. You will work through 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 begins by introducing you to the fundamentals of the TensorFlow library, including variables, matrices, and various data sources. You'll then take a deep dive into some real-world implementations of Keras and TensorFlow and learn how to use estimators to train linear models and boosted trees, both for classification and for regression to provide a baseline for tabular data problems.

As you progress, you'll explore the practical applications of a variety of deep learning architectures, such as recurrent neural networks and Transformers, and see how they can be applied to computer vision and natural language processing (NLP) problems. Once you are familiar with the TensorFlow ecosystem, the final chapter will teach you how to take a project to production.

By the end of this machine learning book, you will be proficient in using TensorFlow 2. You'll also understand deep learning from the fundamentals and be able to implement machine learning algorithms in real-world scenarios.