Deep Learning from Scratch (Paperback)

Seth Weidman

ISBN 10: 1492041416 / ISBN 13: 9781492041412

Published by O'Reilly Media, Sebastopol, 2019

About this Item

Paperback. 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. Youll 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. Youll 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, youll be set up for success on all future deep learning projects.This book provides:Extremely clear and thorough mental modelsaccompanied by working code examples and mathematical explanationsfor understanding neural networksMethods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented frameworkWorking implementations and clear-cut explanations of convolutional and recurrent neural networksImplementation of these neural network concepts using the popular PyTorch frameworkAbout the AuthorSeth is a data scientist who lives in San Francisco. He has been obsessed with understanding Deep Learning ever since he began learning about it in late 2016 and has been writing and speaking about it whenever he can ever since. Professionally, he has applied a variety of machine learning models in industry, taught data science to individuals and companies, and works on modeling and Python projects on the side. Full time, he teaches data science to companies via the Corporate Training team at Metis. He strives to find the simplicity on the other side of complexity. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9781492041412

Bibliographic Details

• Title: Deep Learning from Scratch (Paperback)
• Publisher: O'Reilly Media, Sebastopol
• Publication Date: 2019
• Language: English
• ISBN 10: 1492041416
• ISBN 13: 9781492041412
• Binding: Paperback
• Condition: new

About this title

Synopsis

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. Youâ??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. Youâ??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, youâ??ll be set up for success on all future deep learning projects.

This book provides:

• Extremely clear and thorough mental modelsâ??accompanied by working code examples and mathematical explanationsâ??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.