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

✍ Scribed by Alexia Audevart, Konrad Banachewicz, Luca Massaron

• Publisher: Packt Publishing
• Year: 2021
• Tongue: English
• Leaves: 417
• Category: Library

✦ Synopsis

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 the 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.

✦ Table of Contents

Cover
Copyright
Contributors
Table of Contents
Preface
Chapter 1: Getting Started with TensorFlow 2.x
How TensorFlow works
Declaring variables and tensors
Using eager execution
Working with matrices
Declaring operations
Implementing activation functions
Working with data sources
Additional resources
Chapter 2: The TensorFlow Way
Operations using eager execution
Layering nested operations
Working with multiple layers
Implementing loss functions
Implementing backpropagation
Working with batch and stochastic training
Combining everything together
Chapter 3: Keras
Introduction
Understanding Keras layers
Using the Keras Sequential API
Using the Keras Functional API
Using the Keras Subclassing API
Using the Keras Preprocessing API
Chapter 4: Linear Regression
Learning the TensorFlow way of linear regression
Turning a Keras model into an Estimator
Understanding loss functions in linear regression
Implementing Lasso and Ridge regression
Implementing logistic regression
Resorting to non-linear solutions
Using Wide & Deep models
Chapter 5: Boosted Trees
Introduction
Chapter 6: Neural Networks
Implementing operational gates
Working with gates and activation functions
Implementing a one-layer neural network
Implementing different layers
Using a multilayer neural network
Improving the predictions of linear models
Learning to play Tic-Tac-Toe
Chapter 7: Predicting with Tabular Data
Processing numerical data
Processing dates
Processing categorical data
Processing ordinal data
Processing high-cardinality categorical data
Wrapping up all the processing
Setting up a data generator
Creating custom activations for tabular data
Running a test on a difficult problem
Chapter 8: Convolutional Neural Networks
Introduction
Implementing a simple CNN
Implementing an advanced CNN
Retraining existing CNN models
Applying StyleNet and the neural style project
Implementing DeepDream
Chapter 9: Recurrent Neural Networks
Text generation
Sentiment classification
Stock price prediction
Open-domain question answering
Summary
Chapter 10: Transformers
Text generation
Sentiment analysis
Open-domain question answering
Chapter 11: Reinforcement Learning with TensorFlow and TF-Agents
GridWorld
CartPole
MAB
Chapter 12: Taking TensorFlow to Production
Visualizing Graphs in TensorBoard
Managing Hyperparameter tuning with TensorBoard's HParams
Implementing unit tests
Using multiple executors
Parallelizing TensorFlow
Saving and restoring a TensorFlow model
Using TensorFlow Serving
Packt Page
Other Books You May Enjoy
Index