本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。
This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch.
Một cuốn sách về Học Sâu đề cập đến nhiều framework phổ biến, được sử dụng trên 300 trường Đại học từ 55 đất nước bao gồm MIT, Stanford, Harvard, và Cambridge.
The Java implementation of Dive into Deep Learning (D2L.ai)
Dive to Deep Learning with Pytorch C++ API
Slides for Dive into Deep Learning book
This repo provides Pytorch implementation for codes in the book "Dive Into Deep Learning" (http://d2l.ai/) and course Berkeley STAT 157 (https://courses.d2l.ai), which gives a brief tutorial on deep learning methods.
Adds a few Quality of Life tweaks to the online educational platform Brightspace Desire2Learn (D2L).
Updated content templates (originally designed by D2L Brightspace) with more accessibility and interactivity with flip cards and knowledge checks
Just the flip cards and knowledge checks (multiple choice, short answer, matching, ordering) from d2l-content-templates as standalone HTML pages
This is a series of R Programs used to randomly generate questions and answers within WMU's D2L E-learning system. Several nice features are included or in production, such as user-friendly function wrappers and clear comments to outline the code. The final results are thousands of multiple-choice questions as D2L-compatible CSV and JPG files.
Demonstrate how to integrate a Node.js-based LTI 1.3 tool using ltijs with Brightspace LMS. The final result enables a static HTML UI to be launched via "Insert Stuff" using Deep Linking, hosted on Google Cloud Run {GCP} with MongoDB Atlas.
Deep Learning basics in Python using NumPy, PyTorch, and TensorFlow/Keras: linear regression, softmax regression, multilayer perceptron, etc.
[WIP] Dive into Deep Learning (Vietnamese Version) https://github.com/d2l-ai/d2l-en
This repository contains Tensorflow 2 code for Attention Mechanisms chapter of Dive into Deep Learning (D2L) book.
Learn from *Dive into Deep Learning*, take notes, and do some modification.
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