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Awesome-resources

Awesome Resource for ML and Deep Learning

Python

  1. https://runestone.academy/runestone/books/published/pythonds/index.html

Notes of courses by other people.

  1. http://chrismaxwell.com/ai

Machine Learning

  1. http://www.fast.ai/2017/11/13/validation-sets/ Calibration of Machine Learning Model.
  2. https://www.cs.cornell.edu/~alexn/papers/calibration.icml05.crc.rev3.pdf

NLP

  1. Tf-idf = http://www.tfidf.com/
  2. LSTM in Keras = https://machinelearningmastery.com/sequence-classification-lstm-recurrent-neural-networks-python-keras/
  3. BLEU Score = https://machinelearningmastery.com/calculate-bleu-score-for-text-python/
  4. Sentence similarity using wordnet = https://nlpforhackers.io/wordnet-sentence-similarity/
  5. https://github.com/Smerity/keras_snli 6)https://github.com/salesforce/pytorch-qrnn/
  6. Zero shot learning in moern NLP - https://joeddav.github.io/blog/2020/05/29/ZSL.html
  7. Microsoft best practices for NLP - https://github.com/microsoft/nlp-recipes

Deep Learning

  1. Learning_Rate - https://techburst.io/improving-the-way-we-work-with-learning-rate-5e99554f163b?gi=7645cd50d98
  2. Practical Deep Learning for Coders-: http://www.fast.ai/
  3. Deep Learning Andrew Ng from ground up -: https://www.deeplearning.ai/
  4. Cyclical learning Rate -: http://teleported.in/posts/cyclic-learning-rate/
  5. Differential Learning Rates -: https://towardsdatascience.com/transfer-learning-using-differential-learning-rates-638455797f00
  6. https://www.kaggle.com/kanncaa1/deep-learning-tutorial-for-beginners

Kaggle Discussions

  1. https://terrytao.wordpress.com/2016/06/01/how-to-assign-partial-credit-on-an-exam-of-true-false-questions/
  2. https://www.kaggle.com/c/quora-question-pairs/discussion/31179
  3. https://www.linkedin.com/pulse/duplicate-quora-question-abhishek-thakur/

System Design and Deployment

  1. https://towardsdatascience.com/architecting-a-machine-learning-pipeline-a847f094d1c7
  2. https://github.com/EthicalML/awesome-production-machine-learning
  3. https://www.reddit.com/r/MachineLearning/comments/fvfeps/d_what_does_your_modern_mlinproduction/
  4. https://hackernoon.com/a-guide-to-scaling-machine-learning-models-in-production-aa8831163846
  5. https://medium.com/bettercode/how-to-build-a-modern-ci-cd-pipeline-5faa01891a5b
  6. ML system design -: https://becominghuman.ai/machine-learning-system-design-f2f4018f2f8

#Books

  1. Think stats -: http://greenteapress.com/thinkstats2/thinkstats2.pdf
  2. https://www.thedatasciencehandbook.com/

Misc

  1. https://amitness.com/2020/06/google-colaboratory-tips/

Tensorflow/Keras

  1. https://stanford.edu/~shervine/blog/keras-how-to-generate-data-on-the-fly

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