Error loading page.
Try refreshing the page. If that doesn't work, there may be a network issue, and you can use our self test page to see what's preventing the page from loading.
Learn more about possible network issues or contact support for more help.

Fundamentals of Deep Learning

ebook

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
  • Learn the fundamentals of reinforcement learning

  • Expand title description text
    Publisher: O'Reilly Media

    OverDrive Read

    • ISBN: 9781491925560
    • File size: 17082 KB
    • Release date: May 25, 2017

    EPUB ebook

    • ISBN: 9781491925560
    • File size: 16022 KB
    • Release date: May 25, 2017

    Formats

    OverDrive Read
    EPUB ebook

    Languages

    English

    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
  • Learn the fundamentals of reinforcement learning

  • Expand title description text