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Overall Score : 96 / 100

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Course Description

Deep Learning with PyTorch teaches you how to implement deep learning algorithms with Python and PyTorch. This book takes you into a fascinating case study: building an algorithm capable of detecting malignant lung tumors using CT scans. As the authors guide you through this real example, you'll discover just how effective and fun PyTorch can be. After a quick introduction to the deep learning landscape, you'll explore the use of pre-trained networks and start sharpening your skills on working with tensors. You'll find out how to represent the most common types of data with tensors and how to build and train neural networks from scratch on practical examples, focusing on images and sequences.

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Instructor Details

Eli Stevens

Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software.

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Reviews

4.8

4 total reviews

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By Manuel C. on 03/30/2020

It was a great pleasure reading the first edition. Very clear and illustrative explanations of the concepts.

By Chuck C. on 07/28/2019

A surprisingly good book. The code examples even work.

By Ben on 05/18/2019

I was introduced to pyTorch by doing the Fast.ai MOOC. I've just previewed the book and the concepts are broken into digestible chucks and it also gets me excited going through the chapters.

By Hujun on 11/22/2019

To summarize, this book is a very book starting point for those who are new to PyTorch (even better than the official tutorial). The content is sequential and easy to understand, also thanks to the simplicity of PyTorch itself, a reader with no any ML background can easily understand all necessary basis knowledge and immediately start simply tasks. Highly recommended for 101 class.