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Deep Learning from Scratch: Building with Python from First Principles

Deep Learning from Scratch: Building with Python from First Principles

Deep Learning from Scratch: Building with Python from First Principles
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Deep Learning from Scratch: Building with Python from First Principles Paperback - 2019

by Weidman, Seth

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O'Reilly Media, 31/10/2019 00:00:00. paperback. Good. 3.9970 in x 23.2823 in x 17.6866 in.
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Details

  • Title Deep Learning from Scratch: Building with Python from First Principles
  • Author Weidman, Seth
  • Binding Paperback
  • Condition Used - Good
  • Pages 250
  • Volumes 1
  • Language ENG
  • Publisher O'Reilly Media
  • Publication date 31/10/2019 00:00:00
  • Features Bibliography, Index
  • Bookseller's Inventory # mon0000009499
  • ISBN 9781492041412 / 1492041416
  • Weight 0.9 lbs (0.41 kg)
  • Dimensions 9.19 x 7 x 0.53 in (23.34 x 17.78 x 1.35 cm)
  • Size 3.9970 in x 23.2823 in x 17.6866
  • Category Computers - Other Applications
  • Library of Congress subjects Artificial intelligence, Python (Computer program language)
  • Dewey Decimal Code 006.32
  • Quantity available 1

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From the publisher

With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers with machine learning experience. You ll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way.

Author Seth Weidman shows you how neural networks work using a first principles approach. You ll learn how to apply multilayer neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural networks work mathematically, computationally, and conceptually, you ll be set up for success on all future deep learning projects.

This book provides:

  • Extremely clear and thorough mental models accompanied by working code examples and mathematical explanations for understanding neural networks
  • Methods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented framework
  • Working implementations and clear-cut explanations of convolutional and recurrent neural networks
  • Implementation of these neural network concepts using the popular PyTorch framework

About the author

Seth Weidman is a data scientist who has applied and taught machine learning concepts for several years. He started out as the first data scientist at Trunk Club, where he built lead scoring models and recommender systems, and currently works at Facebook, where he builds machine learning models for their infrastructure team. In between he taught data science and machine learning for the bootcamps and on the corporate training team at Metis. He is passionate about explaining complex concepts simply, striving to find the simplicity on the other side of complexity.

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