Dive Into Deep Learning Book – The scientist authored the popular deep learning book Dive Into Deep Learning, which combines detailed tutorials and math with real examples and code.
Machine learning, the branch of computer science that enables computers to learn, is changing the world. It is used to improve weather forecasting, provide better health care, create self-driving cars, and more. Be an industry pioneer and use machine learning to make product recommendations, detect fraud, predict demand, power Alexa, power the Go Store, and more. And of course, with SageMaker, the company provides developers and data scientists with the ability to build, train, and deploy machine learning (ML) models quickly and at scale.
Dive Into Deep Learning Book
This book includes PyTorch and TensorFlow. We asked the authors why they decided to update their deep learning book. they
Dive Into Deep Learning
The demand for scientists, data scientists, and developers skilled in machine learning is increasing, with demand outstripping supply.
To close that gap, a group of scholars over the past two years have compiled a book that is appealing to universities that teach machine learning, as well as developers looking to build their own machine learning games. This book is called “Dive into Deep Learning” and it is an open source, interactive book that teaches the concepts, mathematical theories, and code of deep learning, all through a unified environment.
Its authors are Aston Zhang, AWS Senior Applications Scientist; Zachary Lipton, Scientific and Operations Researcher at AWS and Machine Learning Collaborative at Carnegie Mellon University; Mu Li, Chief Scientist of AWS; and Alex Smola, AWS Vice President and Distinguished Scientist.
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Dive into Deep Learning is an open source, interactive book that teaches the concepts, mathematical theories, and coding implications of deep learning.
“Dive into Deep Learning is the book I wish I had when I started learning machine learning,” says Smola. “It’s easy to fall into the general theory of machine learning without being able to create things. Immersing yourself in deep learning makes it easy for everyone to experiment and learn. Moreover, this method of publication forces us, the authors of the book, to focus on the important effects. After all, everything taught must be demonstrated with code and data.”
The book began in 2017, when the author began teaching the wider ML community how to use the new Gluon interface, an open-source deep learning interface that helps developers build machine learning models more easily and quickly.
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At the time, there were a number of classic textbooks that taught the mathematics of machine learning and a wide range of open-source implementations of deep learning models, but the available resources did not combine the quality of good textbooks with the best parts. in the manual. This is a particular problem for deep learning, which is a very general discipline. In other words, really understanding how it works requires experimentation. So, during the internship, Lipton created an open source project, a suite of courses called Deep Learning; Direct drug (now obsolete).
Although the project was originally created as a material for practical teaching, it quickly became popular and began to take the form of a book as an open source community that contributes together to improve and expand the offer. When Lipton took over as a professor at CMU, Zhang and Li expanded his coverage of some of his foundational topics and added many more to keep up with the latest innovations in machine learning. They then created a series of video lectures on deep learning in Chinese that became popular with students in China.
“We get a lot of feedback from students who say that our lectures help them ‘get their hands dirty,'” said Jang, the leader of the book. “They ask us to make our lectures like textbooks.”
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The goal is to make machine learning more accessible to everyone, Lee said. “We want to teach ‘just-in-time’ concepts, giving people ideas when they need them to complete certain tasks,” he said. “We want people to get the satisfaction of building their first prototype before worrying about more esoteric concepts.”
From the beginning, the author’s main desire is to make the book enjoyable to read, not an endless scroll. His writing is conversational and understandable, even for newcomers.
It’s easy to get caught up in machine learning theory in general without being able to make things up. Immersing yourself in deep learning makes it easy for everyone to experiment and learn.
Dive Into Deep Learning
However, creating a book that combines accessibility, breadth, and hands-on learning is not easy. To provide convenient access, Dive into Deep Learning is published on GitHub, which also allows GitHub users to suggest changes and new content. The notebook was created with Jupyter Notebooks, which allows you to calculate interactively in many programming languages.
“One of the cool things about Jupyter Notebooks,” says Lipton, “is that not only can you write plain text (in Markdown) and code (here, Python), but you can also include pure mathematical expressions using the LaTeX plug-in. Which. allows you to write pure mathematical expressions.”
Dive into Deep Learning was originally published in Chinese. The author then translated it into English while adding many new topics to include user comments.
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Perhaps the most interesting aspect of the book is the emphasis on learning through learning. Lipton said: “I’ve always thought of computer science and engineering as an autodidactic subject, and certainly one of the ideas behind this book is to get people to try things quickly. This book provides an opportunity to study independently. You’re unlikely to get stuck, even if you go it alone.”
In the general chapter, “Computer Vision,” for example, the author begins by discussing topics such as image manipulation – the ability of a computer to identify something (in the example of a book, a cat) even if the image is modified by cutting, coloring. and so on. , or light. Finally, readers are encouraged to use the dataset to help the computer identify 120 different dog breeds. They were taught how to download relevant data sets, organize and train models to identify species.
For the most part, the book’s chapters were written by different team members based on their interests and expertise. After that, all authors reviewed and edited each chapter.
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To date, the book has become extremely popular and has helped cement its status as a center of excellence in machine learning. About 70 universities use the book in their machine learning courses, and the number is growing.
“This is a timely, interesting book that not only provides an overview of the principles of deep learning, but also detailed algorithms with practical programming code, plus a modern introduction to computer vision and natural learning. Jiawei Han, Professor Michael Aiken President of the University of Illinois at Urbana-Champaign, said, “Go into this book if you want to get into deep learning.”
NVIDIA founder and CEO Jensen Huang added: “Dive into Deep Learning is an excellent book about deep learning and deserves the attention of anyone who wants to know why deep learning has fueled the AI revolution, the most powerful technological force of our time.”
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It is at this point, the author’s focus is to continue improving and improving the book based on input from its many users. “It’s a two-way collaboration,” Zhang said. “We help our readers with machine learning knowledge, and they give us feedback to improve its quality and relevance.”
While working on the book, Aston Zhang and Mu Li revised some of its main topics, added additional topics, and created a series of video lectures about learning Chinese in depth that have become popular with students in China. There are a total of 20 videos that you can watch from the playlist below.
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