Machine Learning Using TensorFlow Cookbook: Create powerful machine learning algorithms with TensorFlow - Couverture souple

Audevart, Alexia; Banachewicz, Konrad; Massaron, Luca

Couverture souple

ISBN 10 : 1800208863 ISBN 13 : 9781800208865

Editeur : Packt Publishing, 2021

Synopsis

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

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

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

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

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

A propos du livre

À propos de l?auteur

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.

Détails de l'édition

• Éditeur: Packt Publishing
• Date d'édition: 2021
• Langue: anglais
• ISBN 10: 1800208863
• ISBN 13: 9781800208865
• Reliure: Broché
• Nombre de pages: 416
• Coordonnées du fabricant: non disponible
• Personne responsable: non disponible