Difference between revisions of "Resources"
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ToddNeller (Talk | contribs) (Added NN resources) |
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** [http://www.cs.waikato.ac.nz/ml/weka/ Weka Java-based Data Mining software] and [http://dl.acm.org/citation.cfm?id=1205860 Ian Witten and Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques] | ** [http://www.cs.waikato.ac.nz/ml/weka/ Weka Java-based Data Mining software] and [http://dl.acm.org/citation.cfm?id=1205860 Ian Witten and Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques] | ||
** [http://modelai.gettysburg.edu/ Model AI Assignments] | ** [http://modelai.gettysburg.edu/ Model AI Assignments] | ||
+ | ** [https://ml.berkeley.edu/blog/tutorials/ Berkeley student crash course on ML] | ||
* Recommendations: | * Recommendations: | ||
** [https://www.quora.com/How-do-I-learn-machine-learning-1 Quora "How do I learn machine learning?" answers] | ** [https://www.quora.com/How-do-I-learn-machine-learning-1 Quora "How do I learn machine learning?" answers] |
Revision as of 15:10, 24 July 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:
- 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
- Online Courses
- Software
- Hardware