https://github.com/ecelis/ai6notebook
A.I. Notes
https://github.com/ecelis/ai6notebook
Last synced: about 1 year ago
JSON representation
A.I. Notes
- Host: GitHub
- URL: https://github.com/ecelis/ai6notebook
- Owner: ecelis
- Created: 2018-05-07T06:51:42.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2023-05-22T00:51:52.000Z (about 3 years ago)
- Last Synced: 2025-02-16T04:44:16.001Z (over 1 year ago)
- Language: Jupyter Notebook
- Homepage: https://ecelis.github.io/ai6notebook/
- Size: 227 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# AI Saturdays Notebook
## Basic knowledge
* [Basic Algebra](https://en.wikibooks.org/wiki/Basic_Algebra)
* [Linear Algebra](https://www.youtube.com/watch?v=kjBOesZCoqc&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)
* [Python + Numpy](https://github.com/kuleshov/cs228-material)
## FastAI
### [Fast AI course part 1](https://course.fast.ai/)
* [Lectures videos](https://www.youtube.com/playlist?list=PLfYUBJiXbdtS2UQRzyrxmyVHoGW0gmLSM)
#### Notes
- [Lesson 1 - Introduction to Image
Recognition](dl1/dl1_lesson1)
- [Lesson 2 - Convolutional Neural Networks](dl1/dl1_lesson2)
- [Lesson 3 - Overfitting](dl1/dl1_lesson3)
## UCL/Deep Reinforcement Learning
* [Lectures videos](https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PLMenL_w8ROqkPwMqbDlYkFC0Ely1Sb6AE)
* [Slides](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html)
* [Web site](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html)
## CS231n: Convolutional Neural Networks for Visual Recognition
* [Lectures videos](https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv)
* [Syllabus and slides](http://cs231n.stanford.edu/syllabus.html)
* [Web site](http://cs231n.stanford.edu/)
## CS224n: Natural Language Processing with Deep Learning
* [Lectures videos](https://www.youtube.com/playlist?list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6)
* [Syllabus and slides](http://web.stanford.edu/class/cs224n/syllabus.html)
* [Web site](http://web.stanford.edu/class/cs224n/)
### NLP Links
* [A primer on Neural Network Models for NLP](http://u.cs.biu.ac.il/~yogo/nnlp.pdf)
* [Speech and language processing 3rd ed. draft](https://web.stanford.edu/~jurafsky/slp3/)
* [Word2Vec tutorial - The skip-gram model](http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/)
* [Distributed Representations of Words and Phrases and their Compositionality](http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf)
* [Efficient Estimation of Word Representations in Vector Space](https://arxiv.org/pdf/1301.3781.pdf)
* [Adversarial examples for evaulating Reading Comprenhension Systems](https://arxiv.org/pdf/1707.07328.pdf)
- [CodaLab worksheet](https://worksheets.codalab.org/worksheets/0xc86d3ebe69a3427d91f9aaa63f7d1e7d/)
- [Github repository](https://github.com/robinjia/adversarial-squad)
* [Google's trained Word2Vec model in Python](http://mccormickml.com/2016/04/12/googles-pretrained-word2vec-model-in-python/)
* [Word2Vec tutorial](https://rare-technologies.com/word2vec-tutorial/)
* [Deep learning with word2vec](https://radimrehurek.com/gensim/models/word2vec.html)
* [The Stanford NLP Group](https://nlp.stanford.edu/)
* [NLP for hackers](https://nlpforhackers.io/)
### NLP Datasets
* [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/)
* [Google Word2Vec](https://code.google.com/archive/p/word2vec/)
* [Spanish Billion Words Corpus](http://crscardellino.me/SBWCE/)
* [Catalan, Spanish, and English portions of the Wikipedia](http://www.lsi.upc.edu/~nlp/wikicorpus/)
## STATS385 Theories of Deep Learning?
* [Lectures videos](https://www.youtube.com/playlist?list=PLog56cvzJcj5nSZqhe-WzImVT9FiSSne3)
* [Slides](https://stats385.github.io/lecture_slides)
* [Website](https://www.researchgate.net/project/Theories-of-Deep-Learning)
## (Jupyter) Notebooks
* [Simple Transfer Functions (TFs)](https://github.com/ecelis/ai6notebook/blob/master/notebooks/Simple%20Transfer%20Functions.ipynb)
* [Simple Back Propagation Neural Network with Numpy (BPNN)](https://github.com/ecelis/ai6notebook/blob/master/notebooks/Simple%20Neural%20Network%20with%20Numpy.ipynb)
* [Enhanced BPNN with added
momentum](https://github.com/ecelis/ai6notebook/blob/master/notebooks/Simple%20Neural%20Network2%20with%20Numpy.ipynb)
* [BPNN with configurable TFs](https://github.com/ecelis/ai6notebook/blob/master/notebooks/Simple%20Neural%20Network3%20with%20Numpy.ipynb)
## AI in the Cloud
Resources to access GPU computing power in the cloud (usually for a
fee).
* [Google Colaboratory](https://colab.research.google.com/)
* [crestle.com](https://www.crestle.com/)
* [paperspace.com](https://www.paperspace.com)
## Links
* [Linear Algebra cheat sheet for Deep Learning](https://towardsdatascience.com/linear-algebra-cheat-sheet-for-deep-learning-cd67aba4526c)
* [Deep Learning, Goodfellow et al. 2016 MIT Press](http://www.deeplearningbook.org/)
* [AI6 Forums](https://ai6forums.nurture.ai)
* [AI6 Slack](https://aisaturdays.slack.com)
* [AI6](https://nurture.ai/ai-saturdays)
* [FastAI's Intro to Machine Learning](http://forums.fast.ai/t/another-treat-early-access-to-intro-to-machine-learning-videos/6826)
* [MLNotebook](https://mlnotebook.github.io/)
* [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/)
## Utils
* [Draw network diagrams with matplotlib](https://gist.github.com/dvgodoy/0db802cfb8edd488dfbd524302ca4be7)