Data

Tools

Indexes

Applications

Experiments

Awesome

An open API service indexing awesome lists of open source software.

https://github.com/neelsoumya/butterfly_detector

Basic tutorials and code for teaching deep learning and machine learning
https://github.com/neelsoumya/butterfly_detector

data-science deep deep-learning deep-learning-tutorial deep-neural-networks ethical-artificial-intelligence learning machine-learning neural-networks open open-data-science outreach outreach-activities public-outreach statistical-learning teaching teaching-materials teaching-resources tutorial tutorials

Last synced: 2 months ago
JSON representation

Basic tutorials and code for teaching deep learning and machine learning

Awesome Lists containing this project

README 

          
# 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



    * [![DOI](https://zenodo.org/badge/180774639.svg)](https://zenodo.org/badge/latestdoi/180774639)