Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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D2l Zh | 41,093 | 1 | 12 hours ago | 45 | March 25, 2022 | 24 | apache-2.0 | Python | ||
《动手学深度学习》:面向中文读者、能运行、可讨论。中英文版被60多个国家的400多所大学用于教学。 | ||||||||||
Machine Learning For Software Engineers | 26,596 | a month ago | 22 | cc-by-sa-4.0 | ||||||
A complete daily plan for studying to become a machine learning engineer. | ||||||||||
Typescript Book | 18,960 | 24 days ago | 145 | other | TypeScript | |||||
:books: The definitive guide to TypeScript and possibly the best TypeScript book :book:. Free and Open Source 🌹 | ||||||||||
Fastbook | 17,796 | 21 hours ago | 26 | May 19, 2022 | 98 | other | Jupyter Notebook | |||
The fastai book, published as Jupyter Notebooks | ||||||||||
D2l En | 17,167 | a day ago | 87 | other | Python | |||||
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 400 universities from 60 countries including Stanford, MIT, Harvard, and Cambridge. | ||||||||||
Deep Learning With Python Notebooks | 16,438 | 2 months ago | 161 | mit | Jupyter Notebook | |||||
Jupyter notebooks for the code samples of the book "Deep Learning with Python" | ||||||||||
Neural Networks And Deep Learning | 14,073 | a month ago | 8 | Python | ||||||
Code samples for my book "Neural Networks and Deep Learning" | ||||||||||
Deep Learning With Tensorflow Book | 11,864 | 2 years ago | 78 | Jupyter Notebook | ||||||
深度学习入门开源书,基于TensorFlow 2.0案例实战。Open source Deep Learning book, based on TensorFlow 2.0 framework. | ||||||||||
Mit Deep Learning Book Pdf | 10,775 | 3 months ago | 10 | Java | ||||||
MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville | ||||||||||
Awesome Rl | 8,109 | 7 days ago | 4 | |||||||
Reinforcement learning resources curated |
Book website | STAT 157 Course at UC Berkeley
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.
Our goal is to offer a resource that could
Descending through a Crowded Valley--Benchmarking Deep Learning Optimizers. R. Schmidt, F. Schneider, P. Hennig. International Conference on Machine Learning, 2021
Universal Average-Case Optimality of Polyak Momentum. D. Scieur, F. Pedregosan. International Conference on Machine Learning, 2020
2D Digital Image Correlation and Region-Based Convolutional Neural Network in Monitoring and Evaluation of Surface Cracks in Concrete Structural Elements. M. Słoński, M. Tekieli. Materials, 2020
GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing. J. Guo, H. He, T. He, L. Lausen, M. Li, H. Lin, X. Shi, C. Wang, J. Xie, S. Zha, A. Zhang, H. Zhang, Z. Zhang, Z. Zhang, S. Zheng, and Y. Zhu. Journal of Machine Learning Research, 2020
Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges. M. Alkinani, W. Khan, Q. Arshad. IEEE Access, 2020
Diagnosing Parkinson by Using Deep Autoencoder Neural Network. U. Kose, O. Deperlioglu, J. Alzubi, B. Patrut. Deep Learning for Medical Decision Support Systems, 2020
Deep Learning Architectures for Medical Diagnosis. U. Kose, O. Deperlioglu, J. Alzubi, B. Patrut. Deep Learning for Medical Decision Support Systems, 2020
ControlVAE: Tuning, Analytical Properties, and Performance Analysis. H. Shao, Z. Xiao, S. Yao, D. Sun, A. Zhang, S. Liu, T. Abdelzaher.
Potential, challenges and future directions for deep learning in prognostics and health management applications. O. Fink, Q. Wang, M. Svensén, P. Dersin, W-J. Lee, M. Ducoffe. Engineering Applications of Artificial Intelligence, 2020
Learning User Representations with Hypercuboids for Recommender Systems. S. Zhang, H. Liu, A. Zhang, Y. Hu, C. Zhang, Y. Li, T. Zhu, S. He, W. Ou. ACM International Conference on Web Search and Data Mining, 2021
If you find this book useful, please star (★) this repository or cite this book using the following bibtex entry:
@article{zhang2021dive,
title={Dive into Deep Learning},
author={Zhang, Aston and Lipton, Zachary C. and Li, Mu and Smola, Alexander J.},
journal={arXiv preprint arXiv:2106.11342},
year={2021}
}
"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, providing 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, 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
This open source book has benefited from pedagogical suggestions, typo corrections, and other improvements from community contributors. Your help is valuable for making the book better for everyone.
Dear D2L contributors, please email your GitHub ID and name to d2lbook.en AT gmail DOT com so your name will appear on the acknowledgments. Thanks.
This open source book is made available under the Creative Commons Attribution-ShareAlike 4.0 International License. See LICENSE file.
The sample and reference code within this open source book is made available under a modified MIT license. See the LICENSE-SAMPLECODE file.
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