Machine Learning with TensorFlow, Second Edition

  • 7h 36m
  • Chris A. Mattmann
  • Manning Publications
  • 2020

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.

By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and Tensor Flow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.

  • Explore the machine learning landscape, particularly neural nets
  • Use Scikit-Learn to track an example machine-learning project end-to-end
  • Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
  • Use the Tensor Flow library to build and train neural nets
  • Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
  • Learn techniques for training and scaling deep neural nets.

In this Book

  • Foreword
  • About This Book
  • A Machine-Learning Odyssey
  • TensorFlow Essentials
  • Linear Regression and Beyond
  • Using Regression for Call-Center Volume Prediction
  • A Gentle Introduction to Classification
  • Sentiment Classification—Large Movie-Review Dataset
  • Automatically Clustering Data
  • Inferring User Activity from Android Accelerometer Data
  • Hidden Markov Models
  • Part-of-Speech Tagging and Word-Sense Disambiguation
  • A Peek into Autoencoders
  • Applying Autoencoders—The CIFAR-10 Image Dataset
  • Reinforcement Learning
  • Convolutional Neural Networks
  • Building a Real-World CNN—VGG -Face and VGG -Face Lite
  • Recurrent Neural Networks
  • LSTMs and Automatic Speech Recognition
  • Sequence-to-Sequence Models for Chatbots
  • Utility Landscape


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