Machine Learning Using TensorFlow Cookbook

Create powerful machine learning algorithms with TensorFlow

Alexia Audevart , Konrad Banachewicz , Luca Massaron 지음

Packt(GCO Science) 2021년 02월 08일 출간

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Comprehensive recipes to give you valuable insights on Transformers, Reinforcement Learning, and more

▶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. 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.

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

▶Key Features
-Deep Learning solutions from Kaggle Masters and Google Developer Experts
-Get to grips with the fundamentals including variables, matrices, and data sources
-Learn advanced techniques to make your algorithms faster and more accurate

▶Who This Book Is For
If you are a data scientist or a machine learning engineer, and you

목차

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

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▶What this book covers
- Chapter 1, Getting Started with TensorFlow 2.x, covers the main objects and concepts in TensorFlow. We introduce tensors, variables, and placeholders. We also show how to work with matrices and various mathematical operations in TensorFlow. At the end of the chapter, we show how to access the data sources used in the rest of the book.

- Chapter 2, The TensorFlow Way, establishes how to connect all the algorithm components from Chapter 1, Getting Started with TensorFlow, into a computational graph in multiple ways to create a simple classifier. Along the way, we cover computational graphs, loss functions, backpropagation, and training with data.

- Chapter 3, Keras, focuses on the high-level TensorFlow API named Keras. After having introduced the layers that are the building blocks of the models, we will cover the Sequential, Functional, and Sub-Classing APIs to create Keras models.

- Chapter 4, Linear Regression, focuses on using TensorFlow for exploring various linear regression techniques, such as Lasso and Ridge, ElasticNet, and logistic regression. We conclude extending linear models with Wide & Deep. We show how to implement each model using estimators.

- Chapter 5, Boosted Trees, discusses the TensorFlow implementation of boosted trees ？ one of the most popular models for tabular data. We demonstrate the functionality by addressing a business problem of predicting hotel booking cancellations.

- Chapter 6, Neural Networks, covers how to implement neural networks in TensorFlow, starting with the operational gates and activation function concepts. We then show a shallow neural network and how to build up various different types of layers. We end the chapter by teaching a TensorFlow neural network to play tic tac toe.

- Chapter 7, Predicting with Tabular Data, this chapter extends the previous one by demonstrating how to use TensorFlow for tabular data. We show how to process data handling missing values, binary, nominal, ordinal, and

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▶ Preface
TensorFlow 2.x, developed by Google, is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push state-of-the-art ML and developers easily build and deploy ML-powered applications.

The independent recipes in this book will teach you how to use TensorFlow for complex data computations and allow you to dig deeper and gain more insights into your data than ever before. With the help of this book, you will work with recipes for training models, model evaluation, regression analysis, tabular data, image and text processing and prediction, and much more. You will explore RNNs, CNNs, GANs, and reinforcement learning, each using the latest version of Google's machine learning library, TensorFlow. Through real-world examples, you will get hands-on experience with various data problems and solving techniques using TensorFlow. Once you are familiar and comfortable with the TensorFlow ecosystem, you will be shown how to take it to production.

By the end of the book, you will be proficient in the field of machine learning using TensorFlow 2.x. You will also have good insight into deep learning and be capable of implementing machine learning algorithms in real-world scenarios.

작가정보

저자(글) Alexia Audevart

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.

저자(글) Konrad Banachewicz

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.

저자(글) Luca Massaron

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.