Ultimate-Guide-To-Deep-Learning
this a roadmap for Deep learning studying contains the MOST useful Courses, Books, Papers, Tools, and Websites that Helped others to contribute in this Vital field
First of all this REPO contains Arabic and English notes to help both side
هذه خارطة الطريق لتعلم العميق تتضمن أهم الدورات, والكتب, والأبحاث العلمية, وأهم الأدوات والمواقع التي ساعدت الكثير في الدخول لهذا المجال الممتع
وقد كتبت الملاحظات باللغتين العربية والإنجليزية مع التشديد على أهمية اللغة الإنجليزية للطلاب العرب
سألين المولى أن يوفقكم ولا تنسونا من دعائكم
Courses
courses from @dair-ai on this Repo which contains all youtube FREE courses and the contributors wrote their notes on it
الكورست ستجدونها مجانية هنا وقد كتبوا ما يحتويه كل كورس والاستفاده منه
Books
- Probabilistic Machine Learning: An Introduction by Kevin Patrick Murphy
- Neural Networks and Deep Learning by Michael Nielsen (Dec 2014)
- Deep Learning by Microsoft Research (2013)
- Deep Learning Tutorial by LISA lab, University of Montreal (Jan 6 2015)
- neuraltalk by Andrej Karpathy : numpy-based RNN/LSTM implementation
- An introduction to genetic algorithms
- Artificial Intelligence: A Modern Approach
- Deep Learning in Neural Networks: An Overview
- Artificial intelligence and machine learning: Topic wise explanation
- Grokking Deep Learning for Computer Vision
- Dive into Deep Learning - numpy based interactive Deep Learning book
- Practical Deep Learning for Cloud, Mobile, and Edge - A book for optimization techniques during production.
- Math and Architectures of Deep Learning - by Krishnendu Chaudhury
- TensorFlow 2.0 in Action - by Thushan Ganegedara
- Deep Learning for Natural Language Processing - by Stephan Raaijmakers
- Deep Learning Patterns and Practices - by Andrew Ferlitsch
- Inside Deep Learning - by Edward Raff
- Deep Learning with Python, Second Edition - by François Chollet
- Evolutionary Deep Learning - by Micheal Lanham
- Engineering Deep Learning Platforms - by Chi Wang and Donald Szeto
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron | Oct 15, 2019
- The Elements of Statistical Learning Data Mining, Inference, and Prediction, Second Edition by Trevor Hastie, Robert Tibshirani, Jerome Friedman
- Dive into Deep Learning
papers
I found this REPO more usefuel for papers The roadmap is constructed in accordance with the following four guidelines:
- From outline to detail
- From old to state-of-the-art
- from generic to specific areas
- focus on state-of-the-art
Conferences
- CVPR - IEEE Conference on Computer Vision and Pattern Recognition
- AAMAS - International Joint Conference on Autonomous Agents and Multiagent Systems
- IJCAI - International Joint Conference on Artificial Intelligence
- ICML - International Conference on Machine Learning
- ECML - European Conference on Machine Learning
- KDD - Knowledge Discovery and Data Mining
- NIPS - Neural Information Processing Systems
- O'Reilly AI Conference - O'Reilly Artificial Intelligence Conference
- ICDM - International Conference on Data Mining
- ICCV - International Conference on Computer Vision
- AAAI - Association for the Advancement of Artificial Intelligence
- MAIS - Montreal AI Symposium
Tools
- Nebullvm - Easy-to-use library to boost deep learning inference leveraging multiple deep learning compilers.
- Netron - Visualizer for deep learning and machine learning models
- Jupyter Notebook - Web-based notebook environment for interactive computing
- TensorBoard - TensorFlow's Visualization Toolkit
- Visual Studio Tools for AI - Develop, debug and deploy deep learning and AI solutions
- Google Colab
- Kaggle
- Azure
Educatinal websites and Helpful
- kdnuggets
- Machine Learning Mastery
- Analytics Vidhya
- AWS » Machine Learning Blog
- Google AI
- Carnegie Mellon University | Machine Learning Blog
- BigML.com | Machine Learning Made Simple
- Medium » Machine Learning
- Open Data Science » Machine Learning
- OpenAI Blog
- Letting neural networks be weird
- Neuroscience News - Deep Learning
- Paper With Code