Deep Learning is a subfield of artificial intelligence, it is a vast field and has a wide range of applications. The core idea of Deep Learning is the neural networks, which future we trained them to learn from various kinds of datasets such as times-series or images dataset gives protection about the unseen data.
If you are a beginner and aiming to learn Deep Learning, today in this article I’m going to share with you a beautiful book written by Aston Zhang (Amazon Senior Scientist), Zack C. Lipton (Amazon Scientist CMU Assistant Professor), Mu Li (Amazon Principal Scientist), Alex J. Smola (Amazon VP/Distinguished Scientist).
There are a number of things great about this book & the most I like is that it covers almost every area of Deep Learning such as for beginner there are chapters like Linear Neural Networks, Multilayer Perceptron, Deep Learning Computation to advance chapters like the CNN, RNN, Computer Vision & NLP also included.
The book delivers on the promise of being interactive. The book is written in Jupyter notebooks, and so the code in chapters can be executed to see immediate results, as well as fine-tuned for inquisitive comparison.
There is flexibility in how to execute these notebooks, you can download the entirety of the book in notebook form to read and execute locally or execute them on AWS using Amazon SageMaker. If you can also open Google Colab notebooks directly from chapters by clicking the “Colab” link in the online version of the book.
This book will teach deep learning concepts from scratch. Sometimes, we want to delve into fine details about the models that would typically be hidden from the user by deep learning frameworks’ advanced abstractions.
This comes up especially in the basic tutorials, where we want you to understand everything that happens in a given layer or optimizer. In these cases, we will often present two versions of the example: one where we implement everything from scratch, relying only on the NumPy interface and automatic differentiation.
The more practical example, where we write succinct code using Gluon. Once we have taught you how some component works, we can just use the Gluon version in subsequent tutorials.
In a world where numerous deep learning frameworks have implemented their own API styles, it’s nice to see this text adopts the use of tools such as PyTorch and MXNet’s Gluon and their Numpy-like interface approach.
The full table of contents is as follows:
Linear Neural Networks
Deep Learning Computation
Convolutional Neural Networks
Modern Convolutional Neural Networks
Recurrent Neural Networks
Modern Recurrent Neural Networks
Natural Language Processing: Pretraining
Natural Language Processing: Applications
Generative Adversarial Networks
Appendix: Mathematics for Deep Learning
Appendix: Tools for Deep Learning
Are you interested, but don’t know if you should take my word for it? Here’s what others have said about the book.
“In less than a decade, the AI revolution has swept from research labs to broad industries to every corner of our daily life. Dive into Deep Learning is an excellent text on deep learning and deserves attention from anyone who wants to learn why deep learning has ignited the AI revolution: the most powerful technology force of our time.”
— Jensen Huang, Founder, and CEO, NVIDIA
“This is a timely, fascinating book, provided with not only a comprehensive overview of deep learning principles but also detailed algorithms with hands-on programming code, and moreover, a state-of-the-art introduction to deep learning in computer vision and natural language processing. Dive into this book if you want to dive into deep learning!”
— Jiawei Han, Michael Aiken Chair Professor, the University of Illinois at Urbana-Champaign
“This is a highly welcome addition to the machine learning literature, with a focus on hands-on experience implemented via the integration of Jupyter notebooks. Students of deep learning should find this invaluable to become proficient in this field.”
— Bernhard Schölkopf, Director, Max Planck Institute for Intelligent Systems
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