d2l-en

rameworks, the code descriptions won't be too different. We use MXNet because we are familiar with it. No matter which DL framework one uses, it should be easy to switch to another one. As a concrete example, in the case of applying RNN to language models, the implementation includes data preprocessing, model construction, and training loops. D2L will guide you through how to transform text data to allow efficient mini-batch iteration, how to implement RNN (with or without using RNN api), and how to efficiently and effectively train a language model. On one hand, even if a DL novice can memorize the algorithms in a traditional textbook, it is still hard to apply it into a real project without knowing implementation details. On the other hand, such implementations are general: the code will be similar even when being re-implemented with another framework. 3. We thank institutions for adopting or recommending D2L in their courses, such as UCLA CS 269 Foundations of Deep Learning, University of Science and Technology of China Deep Learning, UIUC CS 498 Introduction to Deep Learning, and UW CSE 599W Systems for ML. When we wrote the book in Chinese, the book benefited from a lot of feedbacks at https://discuss.gluon.ai/latest?order=views and pull requests from 120+ contributors. It would be very helpful if we could get feedbacks and help from more readers when we are editing. Happy weekends!