butterfly_detector

This is a repository which has tutorials, scripts and notes for teaching machine learning and deep learning. This material can be used to teach machine learning to a general audience and/or working professionals.

This repository also has materials for outreach and teaching AI to the general public.

• Material

  • https://sites.google.com/site/neelsoumya/research-resources/machine-learning

  • https://github.com/neelsoumya/butterfly_detector

• Prerequisities (for learning basic statistics)

  • https://sites.google.com/site/neelsoumya/research-resources/basic-statistics

  • https://github.com/neelsoumya/basic_statistics

• Deep learning animation

  • https://playground.tensorflow.org/

• Deep learning tutorial by Michael Nielsen

  • http://neuralnetworksanddeeplearning.com/

• Deep learning book where each chapter is an executable notebook

  • http://d2l.ai/

• Tutorial and courses on tensorflow and Google colab

  • https://www.coursera.org/learn/introduction-tensorflow/

  • https://github.com/lmoroney/dlaicourse

  • https://colab.research.google.com/github/lmoroney/dlaicourse/blob/master/Course%201%20-%20Part%206%20-%20Lesson%202%20-%20Notebook.ipynb#scrollTo=9FGsHhv6JvDx

  • https://www.coursera.org/learn/convolutional-neural-networks-tensorflow/

• Course on AI by Prof. Patrick Winston

  • https://www.youtube.com/watch?v=TjZBTDzGeGg

• Review paper on machine learning

  • https://www.sciencedirect.com/science/article/pii/S0370157319300766#sec9

• Machine learning course for developers by Google Education

  • https://developers.google.com/machine-learning/crash-course/ml-intro

• Picture of butterfly taken by my mother Kalyani Banerjee

  • https://www.deviantart.com/kalyanibanerjee/art/Broken-wings-776029085

• Tensorflow in the browser

  • https://github.com/tensorflow/tfjs/blob/master/GALLERY.md

  • https://coconet.glitch.me/

  • http://cabreraalex.com/interactive-classification/

  • https://github.com/poloclub/ganlab/

  • https://www.tensorflow.org/js/demos/

  • https://experiments.withgoogle.com/collection/creatability

• More AI in the browser and outreach materials

  • http://projector.tensorflow.org/

  • https://experiments.withgoogle.com/collection/ai

  • https://teachablemachine.withgoogle.com/

  • https://quickdraw.withgoogle.com/

  • https://magenta.tensorflow.org/assets/sketch_rnn_demo/index.html

  • https://pair-code.github.io/what-if-tool/uci.html

  • https://www.climbproject.org.uk/big-data

  • https://www.climbproject.org.uk/dance-mat

• Materials for AI outreach for general public

  • https://www.coursera.org/learn/ai-for-everyone/lecture/9n83j/more-examples-of-what-machine-learning-can-and-cannot-do​

  • https://teachablemachine.withgoogle.com/

  • https://playground.tensorflow.org

  • http://projector.tensorflow.org/

• Here are also some other teaching resources I have compiled/designed

  • https://github.com/neelsoumya/butterfly_detector

  • https://ncase.me/neurons/

• Tensorflow hub

  • http://tfhub.dev/

• Art classification, generation and visualization

  • https://artsexperiments.withgoogle.com/tsnemap/#2611.40,171.15,4110.41,2681.01,0.00,4057.88

  • https://artsexperiments.withgoogle.com/artpalette/colors/f1e3e5-3b614a-d0a468-f45d53-64a67e

• More teaching resources for machine learning

  • https://osf.io/25gnz/

  • https://sites.google.com/site/neelsoumya/teaching

  • https://sites.google.com/site/neelsoumya/research-resources/machine-learning

• Teaching resources for hierarchical Bayesian models and Bayesian linear regression

  • https://osf.io/ujydr/

• Another deep learning book

  • http://d2l.ai/?fbclid=IwAR3gOYDbWBpldmwExcZLakejfQsF6Ixo6BmKcspz4eqVMuTRkVv89i-etak

• More tensorflow resources

  • https://www.youtube.com/watch?v=oXj6ew5ymhM

• Rules of machine learning and data science

  • https://www.youtube.com/watch?v=VfcY0edoSLU

  • https://developers.google.com/machine-learning/guides/rules-of-ml/

  • https://dl.acm.org/citation.cfm?id=2347755

  Structuring machine learning projects

  • https://www.coursera.org/learn/machine-learning-projects

  Data science in a company

  • https://cultivating-algos.stitchfix.com/

• Backpropagation lectures by Andrej Karpathy and Andrew Ng

  • https://www.youtube.com/watch?v=i94OvYb6noo

  • https://www.coursera.org/learn/machine-learning/home/week/5

• Beautiful explanation, game and video on neural networks

  • https://ncase.me/neurons/

• Communication skills

  • Business skills

  • Case studies

  • Business processes

  • Trans-disciplinarity

    • https://medium.com/@miekevanderbijl/transdisciplinary-innovation-and-design-d19d1520ddca

  • How to speak by Prof. Patrick Winston

    • https://www.youtube.com/watch?v=Unzc731iCUY

• Bayesian methods and great tutorial on logistic regression

  • http://cbl.eng.cam.ac.uk/pub/Public/Turner/News/slides.pdf

• Reinforcement learning

  • https://www.coursera.org/learn/practical-rl/home/welcome

  • gym_interface.ipynb

  • gym_interface.py

• Graph neural networks

  • Introduction to graph neural networks

    • https://www.youtube.com/watch?v=uF53xsT7mjc

  • https://github.com/neelsoumya/butterfly_detector/blob/master/graph_neural_networks_tutorial_shortest_path.ipynb

  • DGL library

    • https://github.com/dmlc/dgl

    • https://docs.dgl.ai/tutorials/blitz/index.html

• Very good tutorial on neural networks, autoencoder, softmax

  • https://www.youtube.com/watch?v=VrMHA3yX_QI

• Natural language processing

  • https://www.coursera.org/learn/natural-language-processing-tensorflow/home/welcome

  • https://github.com/neelsoumya/nlp_resources

  • gpt2_playground.ipynb

• Basics of statistical learning from the Introduction to Statistical Learning (ISLR) text (videos and text)

  • https://www.statlearning.com/s/ISLR-Seventh-Printing.pdf

  • https://www.youtube.com/playlist?list=PLOg0ngHtcqbPTlZzRHA2ocQZqB1D_qZ5V

  • http://www.statlearning.com/

• Basics of statistics

  • https://www.openintro.org/stat/textbook.php?stat_book=aps

• AI podcast by Lex Freidman

  • https://www.youtube.com/watch?v=vNOTDn3D_RI&list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4

• Link for citation (if you like this work, please cite it as)

  • Soumya Banerjee. (2020, January 22). neelsoumya/butterfly_detector: Open source teaching materials for machine learning (Version v1.0). Zenodo. http://doi.org/10.5281/zenodo.3621363

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