Difference between revisions of "Resources"
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*** [https://sites.ualberta.ca/~szepesva/RLBook.html Csaba Szepesvári. Algorithms for Reinforcement Learning] | *** [https://sites.ualberta.ca/~szepesva/RLBook.html Csaba Szepesvári. Algorithms for Reinforcement Learning] | ||
*** [http://dl.acm.org/citation.cfm?id=2670001 Marco Wiering and Martijn van Otterlo. Reinforcement Learning: State-of-the-Art] | *** [http://dl.acm.org/citation.cfm?id=2670001 Marco Wiering and Martijn van Otterlo. Reinforcement Learning: State-of-the-Art] | ||
| + | ** [http://www.kdnuggets.com/2017/04/10-free-must-read-books-machine-learning-data-science.html KDNuggets "10 Free Must-Read Books for Machine Learning and Data Science"] | ||
* Online resources: | * Online resources: | ||
** [https://www.coursera.org/learn/machine-learning Andrew Ng's free online Coursera Machine Learning course] | ** [https://www.coursera.org/learn/machine-learning Andrew Ng's free online Coursera Machine Learning course] | ||
| Line 46: | Line 47: | ||
** [http://playground.tensorflow.org/ TensorFlow Playground] | ** [http://playground.tensorflow.org/ TensorFlow Playground] | ||
** [https://distill.pub/2017/momentum/ Why Momentum Really Works] | ** [https://distill.pub/2017/momentum/ Why Momentum Really Works] | ||
| + | ** [http://lamda.nju.edu.cn/weixs/project/CNNTricks/CNNTricks.html Xiu-Shen Wei's Must Know Tips/Tricks in Deep Neural Networks] | ||
| + | ** [https://medium.com/towards-data-science/secret-sauce-behind-the-beauty-of-deep-learning-beginners-guide-to-activation-functions-a8e23a57d046 Medium.com guide: Understanding Activation Functions] | ||
| + | ** [https://github.com/williamFalcon/DeepRLHacks John Schulman's Deep RL Hacks (summarized by William Falcon)] | ||
*Online Courses | *Online Courses | ||
** [https://www.coursera.org/learn/machine-learning Andrew Ng's Machine Learning course] and [https://www.coursera.org/specializations/deep-learning Coursera Deep Learning specialization] | ** [https://www.coursera.org/learn/machine-learning Andrew Ng's Machine Learning course] and [https://www.coursera.org/specializations/deep-learning Coursera Deep Learning specialization] | ||
Revision as of 11:45, 13 September 2017
Starting Places
General
Machine Learning
- Texts:
- Christopher Bishop. Pattern Recognition and Machine Learning
- Kevin Murphy. Machine Learning: A Probabilistic Perspective
- David Barber. Probabilistic Reasoning and Machine Learning (Barber's free PDF version)
- Tom Mitchell. Machine Learning
- Ethem Alpaydin. Introduction to Machine Learning
- Statistical Learning:
- Reinforcement Learning:
- KDNuggets "10 Free Must-Read Books for Machine Learning and Data Science"
- Online resources:
- Andrew Ng's free online Coursera Machine Learning course
- UC Irvine Machine Learning Repository
- Kaggle datasets
- RStudio software for labs
- Weka Java-based Data Mining software and Ian Witten and Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques
- Model AI Assignments
- Berkeley student crash course on ML
- Recommendations:
Neural Network Learning
- Textbooks
- Websites
- Waikit Lau and Arthur Chan's Artificial Intelligence and Deep Learning (AIDL) Facebook group and FAQ
- A Guide to Deep Learning by YerevaNN Labs
- Piotr Migdał's Learning Deep Learning with Keras
- a16z team's reference links
- Stanford's CS 231n Convolutional Networks course website
- various Wikipedia pages concerning artificial neural networks
- TensorFlow Playground
- Why Momentum Really Works
- Xiu-Shen Wei's Must Know Tips/Tricks in Deep Neural Networks
- Medium.com guide: Understanding Activation Functions
- John Schulman's Deep RL Hacks (summarized by William Falcon)
- Online Courses
- Software
- Hardware
