Links to helpful resources
- Self Learning
- Introduction
- Data Analysis
- Machine Learning
- Deep Learning
- Backers
- Sponsors
- Acknowledgment
Books
- Python Machine Learning by Sebastian Raschka and Vahid Mirjalili.
Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition. Online version of the book can be found @GitHub
Concepts, Tools, and Techniques to Build Intelligent Systems
- Deep Learning with Python by Francois Chollet (the founder of Keras language)
Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples.
- Deep Learning Book by Ian Goodfellow, Aaron Courville and Yoshua Bengio
- Reinforcement Learning, Second Edition by Richard S. Sutton and Andrew G. Barto.
This second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Papers
If you are a newcomer to the Deep Learning area, the first question you may have is "Which paper should I start reading from?" Above is a reading roadmap of Deep Learning papers!
Open access to 1,489,618 e-prints in Physics, Mathematics, Computer Science, Quantitative Biology, Quantitative Finance, Statistics, Electrical Engineering and Systems Science, and Economics. Maintain by Cornell University
Built in spare time by @karpathy to accelerate research.
Machine Learning & Data Analysis
This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decision-theoretic modeling paradigm.
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).
Teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community.
Deep Learning
- fast.ai | Practical Deep Learning For Coders, Part 1
- fast.ai | Cutting Edge Deep Learning For Coders, Part 2
Free courses from fast.ai
- Stanford University Computer Vision (cs231n) (2016)
- Stanford University Natural Language Processing (cs224n)
- Deep Reinforcement Learning @ UCL by Google Deep Mind team.
Mathematics
Another course offered by fast.ai
Animated math. centers around presenting math with a visuals-first approach.
Forums
Blogging
Competitions
Opportunities
- Pi School, School of AI
- CIFAR amil | DLRL Summer School
- Google AI Residency Program
- List of AI Residency Programs
Self Learn People Profiles/Projects
- 60 Day Deep Reinforcement Learning Challenge by Andrea Lonza.
- 1 Year ML Journey an Andrea Lonza.
View repository and user information, control your notifications and even manage your issues and pull requests. Built with React Native, GitPoint is one of the most feature-rich unofficial GitHub clients that is 100% free.
Available for both iOS and Android.
A few of the things you can do with GitPoint:
- View user activity feed
- Communicate on your issue and pull request conversations
- Close or lock issues
- Apply labels and assignees
- Review and merge pull requests
- Create new issues
- Star, watch and fork repositories
- Control your unread and participating notifications
- Easily search for any user or repository
Feel free to send us feedback on Twitter or file an issue. Feature requests are always welcome. If you wish to contribute, please take a quick look at the guidelines!
If there's anything you'd like to chat about, please feel free to join our Gitter chat!
This project follows the all-contributors specification and is brought to you by these awesome contributors.
- Follow the React Native Guide for getting started building a project with native code. A Mac is required if you wish to develop for iOS.
- Clone or download the repo
yarn
to install dependenciesyarn run link
to link react-native dependenciesyarn start:ios
to start the packager and run the app in the iOS simulator (yarn start:ios:logger
will boot the application with redux-logger)yarn start:android
to start the packager and run the app in the the Android device/emulator (yarn start:android:logger
will boot the application with redux-logger)
Please take a look at the contributing guidelines for a detailed process on how to build your application as well as troubleshooting information.
Development Keys: The CLIENT_ID
and CLIENT_SECRET
in api/index.js
are for development purposes and do not represent the actual application keys. Feel free to use them or use a new set of keys by creating an OAuth application of your own. Set the "Authorization callback URL" to gitpoint://welcome
.
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Thanks to NTU COOL ASIA and Professor Theng Yin Leng for giving the opportunity.