Fundamentals of deep learning : designing next-generation machine intelligence algorithms / Nikhil Buduma ; with contributions by Nicholas Locascio.

Format:

Book

Author/Creator:

Buduma, Nikhil, author.

Contributor:

Locascio, Nicholas, contributor.

Language:

English

Subjects (All):

Artificial intelligence.

Machine learning.

Neural networks (Computer science).

Physical Description:

1 online resource (298 pages) : illustrations

Edition:

First edition.

Other Title:

Designing next-generation machine intelligence algorithms

Place of Publication:

Beijing, [China] : O'Reilly, 2017.

System Details:

text file

Summary:

With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that’s paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field. Companies such as Google, Microsoft, and Facebook are actively growing in-house deep-learning teams. For the rest of us, however, deep learning is still a pretty complex and difficult subject to grasp. If you’re familiar with Python, and have a background in calculus, along with a basic understanding of machine learning, this book will get you started. Examine the foundations of machine learning and neural networks Learn how to train feed-forward neural networks Use TensorFlow to implement your first neural network Manage problems that arise as you begin to make networks deeper Build neural networks that analyze complex images Perform effective dimensionality reduction using autoencoders Dive deep into sequence analysis to examine language Understand the fundamentals of reinforcement learning

Contents:

The neural network

Training feed-forward neural networks

Implementing neural networks in TensorFlow

Beyond gradient descent

Convolutional neural networks

Embedding and representation learning

Models for sequence analysis

Memory augmented neural networks

Deep reinforcement learning.

Notes:

Includes bibliographical references at the end of each chapters and index.

Description based on online resource; title from PDF title page (ebrary, viewed June 22, 2017).

Includes index.

ISBN:

9781491925607

1491925604

9781491925560

1491925566

9781491925584

1491925582

OCLC:

989166788