Machine Learning Using TensorFlow Cookbook: Over 60 recipes on machine learning using deep learning solutions from Kaggle Masters and Google Developer

Audevart, Alexia, Banachewicz, Konrad, Massaron, Luca



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

Book Description

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.

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

Who this book is for

If you are a data scientist or a machine learning engineer, and you want to skip detailed theoretical explanations in favor of building production-ready machine learning models using TensorFlow, this book is for you.

Basic familiarity with Python, linear algebra, statistics, and machine learning is necessary to make the most out of this book.


Alexia Audevart, also a Google Developer Expert in machine learning, is the founder of datactik. She is a data scientist and helps her clients solve business problems by making their applications smarter. Her first book is a collaboration on artificial intelligence and neuroscience.

Konrad Banachewicz holds a PhD in statistics from Vrije Universiteit Amsterdam. He is a lead data scientist at eBay and a Kaggle Grandmaster. He worked in a variety of financial institutions on a wide array of quantitative data analysis problems. In the process, he became an expert on the entire lifetime of a data product cycle.

Luca Massaron is a Google Developer Expert in machine learning with more than a decade of experience in data science. He is also the author of several best-selling books on AI and a Kaggle master who reached number 7 for his performance in data science competitions.


  1. Getting Started with TensorFlow 2.x
  2. The TensorFlow Way
  3. Keras
  4. Linear Regression
  5. Boosted Trees
  6. Neural Networks
  7. Predicting with Tabular Data
  8. Convolutional Neural Networks
  9. Recurrent Neural Networks
  10. Transformers
  11. Reinforcement Learning with TensorFlow and TF-Agents
  12. Taking TensorFlow to Production