README.md

ML_DL_Research_collab_base:

This is a repository of where we are trying to have all the knowledge base collected.

Conferences:

• http://www.wikicfp.com/cfp/

Literature Review:

• https://www.linkedin.com/posts/valeriovelardo_how-to-conduct-literature-review-effectively-activity-6749316909717606400-B2bc/

CV Notes (Dr.Rob fergus):

• https://cs.nyu.edu/~fergus/teaching/vision/index.html

Reinforcement Learning/ Cognitive Science/ Probabilistic graphical models (Dr. Brenden Lake):

• https://www.youtube.com/playlist?list=PLqYmG7hTraZBKeNJ-JE_eyJHZ7XgBoAyb
• https://brendenlake.github.io/CCM-site/

High Performance Machine Learning:

• HPML, fall 2019 (Drive), Dr. Ulrich Finkler.

Deep Learning (Dr. Yann Le Cunn):

• https://atcold.github.io/pytorch-Deep-Learning/
• https://www.youtube.com/playlist?list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq
• https://d2l.ai/. (DL)
• https://course.fast.ai/videos/?lesson=1 (FAST AI)

Websites:

• https://github.com/gowriaddepalli/DeepLearning2022

Notes:

• ML: (https://github.com/gowriaddepalli/100-Days-Of-ML-Code)
  : https://github.com/Shivanshu-Gupta/foundations-of-machine-learning-david-rosenberg/blob/master/notes.pdf
  : https://bloomberg.github.io/foml/#lectures
• DL (https://github.com/gowriaddepalli/DeepLearning.ai-Summary)
• https://github.com/gowriaddepalli/CV_DL_Research
• NLP - cs224n - (https://github.com/gowriaddepalli/cs224n-winter-2017)
• CV - cs231n - (https://github.com/gowriaddepalli/CS231n-2017-Summary)
• Math - : https://vaibhavgupta.io/notes/
  : https://nickgreenquist.github.io/blog/

Probability and statistics

• https://machinelearninginterview.com/topics/probability-and-statistics/dartboard-paradox-probability-density-function-vs-probability/

Model implementation code:

• https://github.com/gowriaddepalli/deeplearning-models

Applied ML and Industry Research:

• https://github.com/gowriaddepalli/applied-ml
• https://madewithml.com/courses/applied-ml-in-production/solution/ (MLops)

DL CV NLP Tensorflow implementation:

• https://www.educative.io/courses/natural-language-processing-ml/7nz7ynvqVA8
• https://www.educative.io/courses/image-recognition-ml
• https://www.educative.io/courses/deep-learning-for-industry

Papers:

• https://github.com/karanchahal/papers
• https://github.com/sksq96/dl-papernotes
• https://arxiv.org/pdf/1704.02532.pdf
• https://www.kdnuggets.com/2017/04/top-20-papers-machine-learning.html
• https://medium.com/@rupak.thakur/23-deep-learning-papers-to-get-you-started-part-1-308f80d7bba2
• https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap
• https://distill.pub/

Examples:

• https://github.com/sksq96/examples
• https://github.com/sksq96/deep-learning
• https://github.com/sksq96/ML-From-Scratch
• https://github.com/Niranjankumar-c/DeepLearning-PadhAI/tree/master/DeepLearning_Materials
• https://github.com/eriklindernoren/ML-From-Scratch

Blogs:

• https://towardsdatascience.com/@niranjankumarc
• https://distill.pub/2019/visual-exploration-gaussian-processes/
• https://medium.com/@karpathy/yes-you-should-understand-backprop-e2f06eab496b
• http://karpathy.github.io/neuralnets/
• https://im.perhapsbay.es/
• https://im.perhapsbay.es/db
• https://machinelearnings.co/a-humans-guide-to-machine-learning-e179f43b67a0#a817
• https://p555s.github.io/
• https://spraphul.github.io/blog/MyCollections

Courses:

• https://classroom.udacity.com/courses/ud188
• https://www.deeplearning.ai/deep-learning-specialization/
• https://www.fast.ai/

Videos:

• https://www.youtube.com/watch?v=D3fnGG7cdjY
• https://www.youtube.com/watch?v=vTY58-51XZA
• https://www.youtube.com/playlist?list=PLZbbT5o_s2xq7LwI2y8_QtvuXZedL6tQU

Interview Prep:

• https://medium.com/subhrajit-roy/cracking-the-machine-learning-interview-1d8c5bb752d8
• https://www.analyticsvidhya.com/blog/2016/09/40-interview-questions-asked-at-startups-in-machine-learning-data-science/
• https://github.com/gowriaddepalli/Interview_preparation

cheatsheat:

• https://becominghuman.ai/cheat-sheets-for-ai-neural-networks-machine-learning-deep-learning-big-data-678c51b4b463
• https://ml-cheatsheet.readthedocs.io/en/latest/optimizers.html
• https://medium.com/machine-learning-in-practice/cheat-sheet-of-machine-learning-and-python-and-math-cheat-sheets-a4afe4e791b6
• https://ml-cheatsheet.readthedocs.io/en/latest/index.html
• https://stanford.edu/~shervine/teaching/cs-229/cheatsheet-machine-learning-tips-and-tricks
• https://github.com/gowriaddepalli/stanford-cs-229-machine-learning
• https://github.com/gowriaddepalli/stanford-cs-230-deep-learning
• https://github.com/gowriaddepalli/stanford-cs-221-artificial-intelligence
• https://github.com/gowriaddepalli/Cheatsheets

ML Production/Engineering guidelines:

• https://developers.google.com/machine-learning/guides/rules-of-ml
• http://franciscouture.com/wordpress/
• https://towardsdatascience.com/quest-to-understand-machine-learning-in-production-notes-part-i-c9364eb4616
• https://towardsdatascience.com/quest-to-understand-machine-learning-in-production-notes-part-ii-a72bdde60f4c
• https://www.educative.io/courses/data-science-in-production-building-scalable-model-pipeline
• https://madewithml.com/courses/applied-ml-in-production/
• https://medium.com/dair-ai/my-recommendations-to-learn-machine-learning-in-production-d3c5b8e42635
• https://mlinproduction.com/

NLP Research:

• https://ruder.io/deep-learning-nlp-best-practices/index.html
• https://jalammar.github.io/ = https://learningturtle.github.io/Blog/ (attention)
• https://towardsdatascience.com/transformers-explained-visually-part-1-overview-of-functionality-95a6dd460452 (Transformers)

Mathematics: (3Blue1Brown series)

• https://www.youtube.com/channel/UCYO_jab_esuFRV4b17AJtAw
• https://vaibhavgupta.io/notes/
• https://www.linkedin.com/learning/essential-math-for-machine-learning-python-edition

paper explanations(YouTube):

• https://www.youtube.com/channel/UCZHmQk67mSJgfCCTn7xBfew

SOTA code:

• https://paperswithcode.com/
• https://modelzoo.co/
• https://pub.towardsai.net/state-of-the-art-models-in-every-machine-learning-field-2021-c7cf074da8b2

Stanford Courses:

• https://online.stanford.edu/programs/artificial-intelligence-graduate-certificate

floating point deep learning:

• https://eigenfoo.xyz/floating-point-deep-learning/

Metrics:

• https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)
• https://abacus.ai/blog/2020/11/30/evaluating-deep-learning-models-classification/

Interpretability, biasness and fairness in ML:

• https://towardsdatascience.com/interpretability-in-machine-learning-ab0cf2e66e1

ML + Systems:

• https://www.youtube.com/watch?v=OEiNnfdxBRE&list=PLSrTvUm384I9PV10koj_cqit9OfbJXEkq
• https://github.com/gowriaddepalli/mlsystems2022
• https://github.com/gowriaddepalli/ML_Systems

ML:

• https://github.com/madewithml/basics

AutoEncoders

• https://towardsdatascience.com/variational-autoencoder-demystified-with-pytorch-implementation-3a06bee395ed

Graph Convolutional Networks:

• https://towardsdatascience.com/how-to-do-deep-learning-on-graphs-with-graph-convolutional-networks-7d2250723780

Evaluating Model:

• https://www.linkedin.com/posts/bindureddy_evaluating-deep-learning-models-with-abacusai-activity-6739970775232708609-Q_W2/

GANS:

• https://www.linkedin.com/posts/akshat57_recitation-11-generative-adversarial-networks-activity-6745635131828039680-Z7LM/

Energy based models:

• https://thesequence.substack.com/p/edge47

Bayesian Machine Learning:

• https://medium.com/acing-ai/what-is-bayesian-machine-learning-e040ffefbe3b

Model Monitoring:

• https://towardsdatascience.com/the-playbook-to-monitor-your-models-performance-in-production-ec06c1cc3245

Probability Distributions:

• https://towardsdatascience.com/getting-to-know-probability-distributions-cc1dd1e2f22b

Out of Distribution:

• https://medium.com/analytics-vidhya/out-of-distribution-detection-in-deep-neural-networks-450da9ed7044

JOBS PROFILE ANALYSIS

• https://eugeneyan.com/writing/data-science-roles/

Contrastive learning:

• https://www.linkedin.com/posts/danfu09_advances-in-understanding-improving-and-activity-6922385602126499841-01-i/?utm_source=linkedin_share&utm_medium=android_app

ML Codebase techniques:

• https://theaisummer.com/best-practices-deep-learning-code/
• https://theaisummer.com/deep-learning-production/

Pytorch:

• https://github.com/gowriaddepalli/Pytorch

AI SUMMER SCHOOL: Deep Learning Production.

• https://theaisummer.com/deep-learning-production/
• https://github.com/The-AI-Summer/Deep-Learning-In-Production

DeepLearning:

• https://deeplizard.com/

Experiences from the field:

• https://kartikay.substack.com/p/lessons-from-a-decade-of-working