• Title: Dive into Deep Learning
• Author(s) Aston Zhang, Zack C. Lipton, Mu Li, Alex J. Smola
• Publisher: Amazon Science (Mar 25, 2022 - Date)
• Permission(s): Free Online (interactive) and PDF Download
• Hardcover/Paberback: N/A
• eBook: HTML and PDF
• Language: English
• ISBN-10: N/A
• ISBN-13: N/A
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Book Description

This is an open source, interactive book provided in a unique form factor that integrates text, mathematics and code, now supports the TensorFlow, PyTorch, and Apache MXNet programming frameworks, drafted entirely through Jupyter notebooks.

The book is designed to teach people different algorithms used in machine learning. A big asset of the book is the fact it provides all the coding information.

Over the past few years, a team of Amazon scientists has been developing a book that is gaining popularity with students and developers attracted to the booming field of deep learning, a subset of machine learning focused on large-scale artificial neural networks.

The book arrives in a unique form factor, integrating text, mathematics, and runnable code. Drafted entirely through Jupyter notebooks, the book is a fully open source living document, with each update triggering updates to the PDF, HTML, and notebook versions.

Recently the authors added two programming frameworks to their book: PyTorch and TensorFlow. That gives the book - originally written for MXNet - even broader appeal within the open-source machine-learning community of students, developers, and scientists.

It will teaches you how to run Jupyter notebooks in Kaggle, Google Colab, and Amazon SageMaker.

• Aston Zhang, an AWS senior applied scientist; Zachary Lipton, an AWS scientist and assistant professor of Operations Research and Machine Learning at Carnegie Mellon University;

• Amazon
• Amazon (Deep Learning for Coders with Fastai and PyTorch)

• Deep Learning and Neural Networks
• Machine Learning
• Data Science
• Artificial Intelligence
• Data Analysis and Data Mining

• Dive into Deep Learning (Aston Zhang, et al)
• PDF - Tensorflow Edition
• PDF - PyTorch Edition

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