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Jun 21, 2021 · This open-source book represents our attempt to make deep learning approachable, teaching readers the concepts, the context, and the code. The entire book is drafted in Jupyter notebooks, seamlessly integrating exposition figures, math, and interactive examples with self-contained code.
Dive into Deep Learning. Interactive deep learning book with code, math, and discussions. Implemented with PyTorch, NumPy/MXNet, JAX, and TensorFlow. Adopted at 500 universities from 70 countries. Star 22,136. Follow @D2L_ai. [Feb 2023] The book is forthcoming on Cambridge University Press ( order ). The Chinese version is the best seller at ...
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.
Jun 21, 2021 · This open-source book represents our attempt to make deep learning approachable, teaching readers the concepts, the context, and the code. The entire book is drafted in Jupyter notebooks ...
The best way to understand deep learning is learning by doing. This open-source book represents our attempt to make deep learning approachable, teaching you the concepts, the context, and the code. The entire book is drafted in Jupyter notebooks, seamlessly integrating exposition figures, math, and interactive examples with self-contained code.
Dive into Deep Learning — Dive into Deep Learning 0.17.6 documentation
Jun 21, 2021 · This open-source book represents our attempt to make deep learning approachable, teaching readers the concepts, the context, and the code. The entire book is drafted in Jupyter notebooks, seamlessly integrating exposition figures, math, and interactive examples with self-contained code.
An interactive deep learning book for students, engineers, and researchers. The contents are under revision.
Deep Learning
It covers topics including the basics of deep learning, gradient descent, convolutional neural networks, recurrent neural networks, computer vision, natural language processing, recommender systems, and generative adversarial networks.
