https://github.com/deehzee/ml-bookmarks

My bookmarks related to Machine Learning
https://github.com/deehzee/ml-bookmarks
data-science deep-learning machine-learning nlp
Last synced: 6 months ago
JSON representation
My bookmarks related to Machine Learning
Host: GitHub
URL: https://github.com/deehzee/ml-bookmarks
Owner: deehzee
License: cc0-1.0
Created: 2021-02-16T14:24:20.000Z (over 5 years ago)
Default Branch: main
Last Pushed: 2024-04-15T15:24:25.000Z (about 2 years ago)
Last Synced: 2024-04-15T16:49:27.733Z (about 2 years ago)
Topics: data-science, deep-learning, machine-learning, nlp
Homepage:
Size: 165 KB
Stars: 3
Watchers: 2
Forks: 1
Open Issues: 1
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project

README

          # ML Bookmarks

My bookmarks (as well as, wishlist and advice) for free (for most parts -- some may be non-free)

machine learning resources on the web -- personally biased.

## Table of Contents

* [Machine Learning / Deep Learning](#machine-learning--deep-learning)

* [Computer Vision](#computer-vision)

* [Natural Language Processing / Understanding](#natural-language-processing--understanding)

* [Math / Stat / Foundations](#math--stat--foundations)

* [Computer Science](#computer-science)

* [System Design](#system-design)

* [Useful Resources](#useful-resources)

* [Books](#books)

* [Articles / Blogs / Talks](#articles--blogs--talks)

## Machine Learning / Deep Learning

* [ML] Stanford CS229 Fall 2018 by Andrew Ng

  + http://cs229.stanford.edu/syllabus-autumn2018.html

  + [(YouTube) Stanford CS229 | Machine Learning | Autumn 2018](https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU)

* [ML] Learning from Data by Abu Mostafa, Caltech

  + https://work.caltech.edu/telecourse

* [ML] [Foundations of Data Science, Microsoft Research (YouTube)](https://www.youtube.com/playlist?list=PLD7HFcN7LXRcvobbHq_8zMyWq_tKwtebc)

* [ML] Mining Massive Datasets

  + http://mmds.org/

  + http://web.stanford.edu/class/cs246/index.html

  + http://infolab.stanford.edu/~ullman/mining/2009/index.html

  + [(YouTube) Mining Massive Datasets Stanford University [Full Course]](https://www.youtube.com/playlist?list=PLLssT5z_DsK9JDLcT8T62VtzwyW9LNepV)

  + [(YouTube) CS246, Mining Massive Data Sets](https://www.youtube.com/playlist?list=PLoCMsyE1cvdVnCgHk43vRy7PVTVWJ6WVR)

* [ML] [Cornell CS5787: Applied Machine Learning, Fall 2020 (YouTube)](https://www.youtube.com/playlist?list=PL2UML_KCiC0UlY7iCQDSiGDMovaupqc83)

  + https://github.com/kuleshov/cornell-cs5785-applied-ml

* [ML] Machine Learning by Google Developer

  + https://developers.google.com/machine-learning/crash-course

* [ML] Tom Michell's courses

  + Machine Learning 10-601 @CMU in Spring 2015 by Mitchell and Balcan with video (http://www.cs.cmu.edu/~ninamf/courses/601sp15/)

  + Tom Mitchell's university page (http://www.cs.cmu.edu/~tom/)

  + Maria-Florina Balcan's university page (http://www.cs.cmu.edu/~ninamf/)

* [ML] Harvard CS181: Machine Learning (https://harvard-ml-courses.github.io/cs181-web/)

* [ML] Machine Learning Crash Course by Google (https://developers.google.com/machine-learning/crash-course)

* [ML.Prob] [Probabilistic Machine Learning - Philipp Hennig, 2021, Uni-Tuebingen (YouTube)](https://www.youtube.com/playlist?list=PL05umP7R6ij1tHaOFY96m5uX3J21a6yNd)

  + [Course contents from Universität Tübingen, Summer 2020](https://uni-tuebingen.de/en/180804)

* [ML] Scalable Machine Learning by Alex Smola at Berkeley in 2012

  + http://alex.smola.org/teaching/berkeley2012/

* [MLD] Full Stack Deep Learning

  + Spring 2021 (https://fullstackdeeplearning.com/spring2021/)

  + [(2021-Spring) Fullstack Deep Learning (YouTube)](https://www.youtube.com/playlist?list=PL1T8fO7ArWlcWg04OgNiJy91PywMKT2lv)

  + Fall 2019 (https://fall2019.fullstackdeeplearning.com/)

* [DL] MIT 6.S191 Introduction to Deep Learning (Spring 2021)

  + http://introtodeeplearning.com/

* [DL] Deep Learning by Fast.ai

  + https://course.fast.ai/

  + [YouTube Video Lectures by FreeCodeCamp.org](https://www.youtube.com/watch?v=0oyCUWLL_fU)

* [DL] [UC Berkeley CS182 Spring 2021: Deep Learning on YouTube](https://www.youtube.com/playlist?list=PL_iWQOsE6TfVmKkQHucjPAoRtIJYt8a5A)

  + [Course Mateirials: https://cs182sp21.github.io/](https://cs182sp21.github.io/)

* [DL] NYU DS-GA 1008 Deep Learning by LeCun and Canziani

  + [(YouTube) NYU Deep Learning, Spring 2021](https://www.youtube.com/playlist?list=PLLHTzKZzVU9e6xUfG10TkTWApKSZCzuBI)

  + https://github.com/Atcold/NYU-DLSP21 Spring 2021

  + https://atcold.github.io/pytorch-Deep-Learning/ Soring 2020

  + [(YouTube) Deep Learning with Pytorch, Spring 2020](https://www.youtube.com/playlist?list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq)

* [DL] T81 558: Applications of Deep Neural Networks by Jeff Heaton, Washington University at St Louis

  + https://sites.wustl.edu/jeffheaton/t81-558/

  + [Applications of Deep Learning Networks for Tensorflow and Keras (2021) [YouTube]](https://www.youtube.com/playlist?list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN)

  + https://github.com/jeffheaton/t81_558_deep_learning

* [DL] [Deep Learning Crash Course 2021 by Alex Smola](https://www.youtube.com/playlist?list=PLZSO_6-bSqHQsDaBNtcFwMQuJw_djFnbd)

* [DL] Deep Learning Course by Fleuret at UNIGE/EPFL with Pytorch (https://fleuret.org/dlc/)

* [DL] UVA Deep Learning Course (https://uvadlc.github.io/)

  + [YouTube Playlist - UVA Deep Learning Lectures 2020](https://www.youtube.com/playlist?list=PLdlPlO1QhMiDlES3Vck6oQwO3TMYbdZDk)

  + [YouTube Playlist - Tutorial Notebooks](https://www.youtube.com/playlist?list=PLdlPlO1QhMiAkedeu0aJixfkknLRxk1nA)

  + GitHub Repo for Jupyter Notebooks (https://github.com/phlippe/uvadlc_notebooks) includes GNN

  + Website for Jupyter Notebooks (https://uvadlc-notebooks.readthedocs.io/en/latest/) includes GNN

* [DL.UL] [Berkeley CS294-158-SP20: Deep Unsupervised Learning (Spring 2020)](https://sites.google.com/view/berkeley-cs294-158-sp20/home)

  + [YouTube Playlist - Lectures](https://www.youtube.com/playlist?list=PLwRJQ4m4UJjPiJP3691u-qWwPGVKzSlNP)

  + [HWs and Notebooks](https://github.com/rll/deepul)

* [ML.GNN] Stanford CS224W: Machine Learning with Graphs (https://cs224w.stanford.edu)

  + [Stanford CS224W: Machine Learning with Graphs by Jure Leskovec 2021-Spring (YouTube)](https://www.youtube.com/playlist?list=PLoROMvodv4rPLKxIpqhjhPgdQy7imNkDn)

  + Winter 2021 by Jure Leskovec (http://web.stanford.edu/class/cs224w/)

  + [Autumn 2019 on YouTube by Jure Leskovec](https://www.youtube.com/playlist?list=PLUjDWbHzLn6NOha7_RnC5LOXurenpy-QE)

* [DL.GDL/GNN] AMMI 2021 GDL100 Geometric Deep Learning Course (https://geometricdeeplearning.com/lectures/)

* [RL.DL] [(2020-Fall) UC Berkeley CS285: Deep Reinforcement Learning (YouTube)](https://www.youtube.com/playlist?list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc)

[[back to top](#ml-bookmarks)]

## Computer Vision

* [CV] Introduction to Computer Vision | Georgia Tech CS 4476 Fall 2019 edition (https://dellaert.github.io/19F-4476/schedule.html)

* [CV.DL] [(2020-Fall) UMich EECS 498-007 / 598-005: Deep Learning for Computer Vision (YouTube)](https://www.youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r)

* [ML.GNN] Stanford CS224W: Machine Learning with Graphs (https://cs224w.stanford.edu)

  + [Stanford CS224W: Machine Learning with Graphs by Jure Leskovec 2021-Spring (YouTube)](https://www.youtube.com/playlist?list=PLoROMvodv4rPLKxIpqhjhPgdQy7imNkDn)

  + Winter 2021 by Jure Leskovec (http://web.stanford.edu/class/cs224w/)

  + [Autumn 2019 on YouTube by Jure Leskovec](https://www.youtube.com/playlist?list=PLUjDWbHzLn6NOha7_RnC5LOXurenpy-QE)

* [DL.GDL/GNN] AMMI 2021 GDL100 Geometric Deep Learning Course (https://geometricdeeplearning.com/lectures/)

* [RL.DL] [(2020-Fall) UC Berkeley CS285: Deep Reinforcement Learning (YouTube)](https://www.youtube.com/playlist?list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc)

[[back to top](#ml-bookmarks)]

## Natural Language Processing / Understanding

* [NLP.DL] [(YouTube) Stanford CS224N: Natural Language Processing with Deep Learning](https://www.youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z)

* [NLP.DL] [(YouTube) CMU CS11-747: Neural Nets for NLP 2021](https://www.youtube.com/playlist?list=PL8PYTP1V4I8AkaHEJ7lOOrlex-pcxS-XV)

* [NLP.DL] [(2020-Fall) CMU CS11-737: Multilingual NLP (YouTube)](https://www.youtube.com/playlist?list=PL8PYTP1V4I8CHhppU6n1Q9-04m96D9gt5)

* [NLP.DL] [(YouTube) UMass CS685: Advanced NLP, Fall 2020](https://www.youtube.com/playlist?list=PLWnsVgP6CzadmQX6qevbar3_vDBioWHJL)

[[back to top](#ml-bookmarks)]

## Math / Stat / Foundations

* [Advice] Data Science Career Advice of College Students (https://www.springboard.com/blog/data-scientist-training-college/)

* [DS.ML] [52 Week Curriculum for Data Science in 2021 by Terrence Shin](https://towardsdatascience.com/a-complete-52-week-curriculum-to-become-a-data-scientist-in-2021-2b5fc77bd160)

* [Stat] StatQuest (https://statquest.org/video-index/)

* [Math] Mathematics for Machine Learning (https://mml-book.github.io/)

* [Math.DS.ML] [Great contents from 3Blue1Brown on YouTube](https://www.youtube.com/channel/UCYO_jab_esuFRV4b17AJtAw)

* [LinAlg] Linear Algebra Video Lectures by Strang at MIT in 2010 (https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/)

* [LinAlg] Computational Linear Algebra for Coders (https://github.com/fastai/numerical-linear-algebra) ([YouTube](https://www.youtube.com/playlist?list=PLtmWHNX-gukIc92m1K0P6bIOnZb-mg0hY))

* [DS.ML] Python Data Science Handbook (https://jakevdp.github.io/PythonDataScienceHandbook/)

* [PhD] Do I Need to Go to University? (http://colah.github.io/posts/2020-05-University/)

* [PhD] A Survival Guide to a PhD (http://karpathy.github.io/2016/09/07/phd/)

* [ML] Probabilistic Programming and Bayesian Methods for Hackers by Davidson-Pilon (with PyMC3 and TFP)

  + http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/

  + https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers

* [ML] Introduction to Statistical Learning (ISLR) by James, Witten, Hastie and Tibshirani (https://www.statlearning.com/)

* [DS] Data Science @OSSU (https://github.com/ossu/data-science)

* [CS] Computer Science @OSSU (https://github.com/ossu/computer-science)

* [Prob.Stat] Seeing Theory: A Visual Introduction to Probability and Statistics (https://seeing-theory.brown.edu/)

* [ML] [(2018-Fall) Staford CS229: Machine Learning by Andrew Ng (YouTube)](https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU)

* [Git] Git from the Bottom up (https://jwiegley.github.io/git-from-the-bottom-up/)

[[back to top](#ml-bookmarks)]

## Computer Science

* [CS.Prog] Stanford CS106B: Programming Abstractions in C++ 

   ([Course](https://online.stanford.edu/courses/cs106b-programming-abstractions),

    [SEE](https://see.stanford.edu/course/cs106b),

    [YouTube](https://www.youtube.com/playlist?list=PLoCMsyE1cvdWiqgyzwAz_uGLSHsuYZlMX))

* [CS.Prog] Stanford CS106X: Programming Abstractoins in C++ (Accelerated)

   ([Course](https://online.stanford.edu/courses/cs106x-programming-abstractions-accelerated),

    [YouTube](https://www.youtube.com/playlist?list=PLoCMsyE1cvdVmbGH6Jp-9twXPbi5J_IBT))

* [CS.Sys] Stanford CS107: Computer Organization and Systems (C)

   ([Course](https://online.stanford.edu/courses/cs107-computer-organization-and-systems),

    [YouTube](https://www.youtube.com/playlist?list=PLoCMsyE1cvdWivlV-39KKsBKUX-4DvraN))

* [CS.Sys] Stanford CS108: Object-Oriented Systems Design

   ([Course](https://online.stanford.edu/courses/cs108-object-oriented-systems-design))

* [CS.Sys] Stanford CS110: Principles of Computer Systems

   ([Course](https://online.stanford.edu/courses/cs110-principles-computer-systems),

    [YouTube](https://www.youtube.com/playlist?list=PLai-xIlqf4JmTNR9aPCwIAOySs1GOm8sQ))

* [CS.Network] Stanford CS144: Introduction to Computer Networking

   ([Course](https://online.stanford.edu/courses/cs144-introduction-computer-networking),

    [YouTube](https://www.youtube.com/playlist?list=PLoCMsyE1cvdWKsLVyf6cPwCLDIZnOj0NS))

* [CS.Sys] Stanford CS212: Operating Systems and Systems Programming

   ([Course](https://online.stanford.edu/courses/cs212-operating-systems-and-systems-programming))

* [CS.Web] Stanford CS142: Web Applications

   ([Course](https://online.stanford.edu/courses/cs142-web-applications))

* [CS.Web] Harvard CS50 and CS75

  - Harvard CS50x: Intro CS & Web (https://cs50.harvard.edu/x/2021/)

  - Harvard CS50: Intro CS & Web (https://cs50.harvard.edu/college/2021/spring/)

  - Harvard CS50: Web Programming with Python and JavaScript

     (https://cs50.harvard.edu/web/2020/,

      [YouTube](https://www.youtube.com/playlist?list=PLhQjrBD2T382_R182iC2gNZI9HzWFMC_8),

      [YouTube](https://www.youtube.com/playlist?list=PLhQjrBD2T380xvFSUmToMMzERZ3qB5Ueu))

  - Harvard CS75: Building Dynamic Website by David J Malan

     ([CS75.TV](http://cs75.tv/2012/summer/),

      [YouTube](https://www.youtube.com/playlist?list=PLSlcu3sMjsc9QoiHpdEWF246A4eVCjBUQ))

  - [Web] Deep Dive into Modern Web Development (https://fullstackopen.com/en/)

  - [Web] https://javascript.info/

  - [Web] (MDN) Mozilla Developer Network (https://developer.mozilla.org/en-US/docs/Learn)

* [CS.DB] Stanford CS145: Data Management and Data Systems

   ([Course](https://online.stanford.edu/courses/cs145-data-management-and-data-systems),

    [YouTube](https://www.youtube.com/playlist?list=PLroEs25KGvwzmvIxYHRhoGTz9w8LeXek0),

    [YouTube](https://www.youtube.com/playlist?list=PL9ysvtVnryGpnIj9rcIqNDxakUn6v72Hm))

* [CS.DB] CMU 15-445/645: Database Systems

  - Fall 2019 ([Course](https://15445.courses.cs.cmu.edu/fall2019/), [YouTube](https://www.youtube.com/playlist?list=PLSE8ODhjZXjbohkNBWQs_otTrBTrjyohi))

  - Fall 2021 ([Course](https://15445.courses.cs.cmu.edu/fall2021/), [YouTube](https://www.youtube.com/playlist?list=PLSE8ODhjZXjZaHA6QcxDfJ0SIWBzQFKEG))

* [CS.DB] CMU 15-721: Advanced Database Systems (Spring 2020)

   ([Course](https://15721.courses.cs.cmu.edu/spring2020/), [YouTube](https://www.youtube.com/playlist?list=PLSE8ODhjZXjasmrEd2_Yi1deeE360zv5O))

[[back to top](#ml-bookmarks)]

## System Design

* [SystemDesign] https://github.com/donnemartin/system-design-primer

* [SystemDesign] ByteByteGo Blog by Alex Xu (https://blog.bytebytego.com/)

* [MLD] Stanford CS329S: Machine Learning System Design, Spring 2021

  + https://stanford-cs329s.github.io/syllabus.html

* [DistSys] An Introduction to Distributed Systems (https://github.com/aphyr/distsys-class)

* [DistSys] Distributed Systems for Fun and Profit Book (http://book.mixu.net/distsys/index.html)

[[back to top](#ml-bookmarks)]

## Useful Resources

* [DS] [52 Week Curriculum for Data Science in 2021 by Terrence Shin](https://towardsdatascience.com/a-complete-52-week-curriculum-to-become-a-data-scientist-in-2021-2b5fc77bd160)

* [Stat] StatQuest (https://statquest.org/video-index/)

* [ML/DL/NLP/GNN/GDL/RL] YouTube ML Courses (https://github.com/dair-ai/ML-YouTube-Courses)

* [ML] Awesome ML Courses (https://github.com/luspr/awesome-ml-courses)

* [MLD] Machine Learning Systems Design (https://github.com/chiphuyen/machine-learning-systems-design)

* [MLD] Applied ML (https://github.com/eugeneyan/applied-ml)

* [ML.DL.NLP] ML Surveys (https://github.com/eugeneyan/ml-surveys)

* [NLP] The NLP Pandect (https://github.com/ivan-bilan/The-NLP-Pandect)

* [SystemDesign] https://github.com/donnemartin/system-design-primer

* [DS.ML.Interview] Data Science Interviews

  + https://github.com/alexeygrigorev/data-science-interviews

  + https://ds-interviews.org/awesome.html

* [ML.Interview] [4 Types of Interview Questions for DS & ML](https://pub.towardsai.net/4-types-of-machine-learning-interview-questions-for-data-scientists-and-machine-learning-engineers-b8135805ce1b)

* [ML.DL.Code] Keras Code Examples (https://keras.io/examples/)

* [GraphNN] Must-Read Papers on Graph Neural Networks (https://github.com/thunlp/GNNPapers)

* [DL.Prod] Deep Learning in Production (https://github.com/The-AI-Summer/Deep-Learning-In-Production)

[[back to top](#ml-bookmarks)]

## Books

* [DS.ML] Python Data Science Handbook (https://jakevdp.github.io/PythonDataScienceHandbook/)

* [ML] Interpretable Machine Learning (https://christophm.github.io/interpretable-ml-book/)

* [ML] Probabilistic Programming and Bayesian Methods for Hackers by Davidson-Pilon (with PyMC3 and TFP)

  + http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/

  + https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers

* [ML] Bayesian Reasoning and Machine Learning by David Barber

   ([BRML](http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage))

* [ML] Pattern Recognition and Machine Learning by Christopher Bishop

   ([PRML](https://www.microsoft.com/en-us/research/people/cmbishop/prml-book/))

* [ML] Probabilistic Machine Learning by Kevin Murphy

   ([PML](https://probml.github.io/pml-book/))

* [ML] Elements of Statistical Learning by Hastie, Tibshirani and Friedman

   ([ESL](https://web.stanford.edu/~hastie/ElemStatLearn/))

* [ML] Introduction to Statistical Learning by James, Witten, Hastie and Tibshirani

   ([ISLR](https://www.statlearning.com/))

* [ML/BigData] Mining of Massive Datasets by Leskovec, Rajaraman, Ullman

   ([MMDS](http://mmds.org/))

* [ML] Foundations of Data Science by Blum, Hopcroft, Kannan

   ([FDS:pdf](https://www.cs.cornell.edu/jeh/book%20no%20so;utions%20March%202019.pdf))

* [DL] Deep Learning Book by Goodfellow, Bengio and Courville (https://www.deeplearningbook.org/)

* [DL] Dive into Deep Learning by Zhang, Lipton, Li and Smola (https://d2l.ai/)

* [Graph/Network] Network Science Book by Barabási (http://networksciencebook.com/)

* [Grap/Network/GameTh] Networks, Crowds, and Markets by Easley and Kleinberg (http://www.cs.cornell.edu/home/kleinber/networks-book/)

* [Graph.Kernel] Graph Kernels: State-of-the-Art and Future Challenges (https://arxiv.org/abs/2011.03854)

* [GeomDL/Graph] Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges (https://arxiv.org/abs/2104.13478)

* [DL.GNN] Graph Representation Learning Book by Hamilton (https://www.cs.mcgill.ca/~wlh/grl_book/)

* [ML.MLE.MLD] Machine Learning Engineering by Andriy Burkov (http://www.mlebook.com/wiki/doku.php)

* [ML.MLE.MLD] Machine Learning Yearnings by Andrew Ng (Downloadable from https://www.deeplearning.ai/programs/)

* [ML] Approacing Almost Any Machine Learning Problem by Abhishek Thakur (https://github.com/abhishekkrthakur/approachingalmost)

* [DistSys] Distributed Systems for Fun and Profit Book (http://book.mixu.net/distsys/index.html)

[[back to top](#ml-bookmarks)]

## Articles / Blogs / Talks

* [SystemDesign] ByteByteGo Blog by Alex Xu (https://blog.bytebytego.com/)

* [NLP] NLP Overview (https://nlpoverview.com/)

* Lilian Weng's Blog (https://lilianweng.github.io/lil-log/)

  + [DL] An Overview for Deep Learning for Curious People (https://lilianweng.github.io/lil-log/2017/06/21/an-overview-of-deep-learning.html)

  + [DL] Predict Stock Prices Using RNN (https://lilianweng.github.io/lil-log/2017/07/08/predict-stock-prices-using-RNN-part-1.html and https://lilianweng.github.io/lil-log/2017/07/22/predict-stock-prices-using-RNN-part-2.html)

  + [NLP] Learning Word Embeddings (https://lilianweng.github.io/lil-log/2017/10/15/learning-word-embedding.html)

  + [RL] A Peek into Reinforcement Learning (https://lilianweng.github.io/lil-log/2018/02/19/a-long-peek-into-reinforcement-learning.html)

  + [NLP] Attention (https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)

  + [NLP] Generalized Language Models (https://lilianweng.github.io/lil-log/2019/01/31/generalized-language-models.html)

  + [AI] Evolution Strategies (https://lilianweng.github.io/lil-log/2019/09/05/evolution-strategies.html)

  + [NLP] Transformer Family (https://lilianweng.github.io/lil-log/2020/04/07/the-transformer-family.html)

  + [NLP] Open-Domain Question Answering System (https://lilianweng.github.io/lil-log/2020/10/29/open-domain-question-answering.html)

* Andrej Karpathy's Blog (http://karpathy.github.io/)

  + [DL] Hacker's Guide to Neural Networks (http://karpathy.github.io/neuralnets/)

  + [DL] A Recipe for Training Neural Networks (http://karpathy.github.io/2019/04/25/recipe/)

  + [DL] The Unreasonable Effectiveness of RNNs (http://karpathy.github.io/2015/05/21/rnn-effectiveness/)

  + [PhD] A Survival Guide to a PhD (http://karpathy.github.io/2016/09/07/phd/)

* Christopher Colah's Blog (http://colah.github.io/)

  + [DL] Understanding LSTM Networks (http://colah.github.io/posts/2015-08-Understanding-LSTMs/)

  + [DL] Attention and Augmented RNNs (https://distill.pub/2016/augmented-rnns/)

  + [NLP] Deep Learning, NLP, Representations (http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/)

  + [DL] Neural Networks, Manifolds, Topology (http://colah.github.io/posts/2014-03-NN-Manifolds-Topology/)

  + [DL] Neural Networks and Functional Programming (http://colah.github.io/posts/2015-09-NN-Types-FP/)

  + [DL] Understanding Convolutions (http://colah.github.io/posts/2014-07-Understanding-Convolutions/)

  + [DL] Group Convolutions (http://colah.github.io/posts/2014-12-Groups-Convolution/)

  + [Info] Visual Information Theory (http://colah.github.io/posts/2015-09-Visual-Information/)

  + [PhD] Do I Need to Go to University? (http://colah.github.io/posts/2020-05-University/)

* [NLP] Sebastian Ruder's Blog (https://ruder.io/)

  + [DL] https://ruder.io/optimizing-gradient-descent/

* [ML] Sebastian Raschka's Blog (https://sebastianraschka.com/blog/)

  + [DL] A Short Chronology of Deep Learning for Tabular Data (https://sebastianraschka.com/blog/2022/deep-learning-for-tabular-data.html)

* [DL] [Understanding GRU Networks](https://towardsdatascience.com/understanding-gru-networks-2ef37df6c9be)

* [ML.Interview] [4 Types of Interview Questions for DS & ML](https://pub.towardsai.net/4-types-of-machine-learning-interview-questions-for-data-scientists-and-machine-learning-engineers-b8135805ce1b)

* [ML] [Feature Engineering Deep Diving: Binning & Encoding](https://towardsdatascience.com/feature-engineering-deep-dive-into-encoding-and-binning-techniques-5618d55a6b38)

* [NLP] [Understanding BERT Transformer: Attention isn't all you need](https://medium.com/synapse-dev/understanding-bert-transformer-attention-isnt-all-you-need-5839ebd396db)

* [NLP] [Some Examples of Applying BERT in Specific Domain](https://towardsdatascience.com/how-to-apply-bert-in-scientific-domain-2d9db0480bd9)

* [NLP] Berkeley Neural Parser (https://github.com/nikitakit/self-attentive-parser)

* [NLP.Address] [AddressNet: Robust Street Address Parser](https://towardsdatascience.com/addressnet-how-to-build-a-robust-street-address-parser-using-a-recurrent-neural-network-518d97b9aebd)

* Netflix Tech Blog (https://netflixtechblog.com)

* Facebook Blog

  + Engineering @FB (https://engineering.fb.com)

  + ML Applications @FB (https://engineering.fb.com/category/ml-applications/)

  + [[How ML Powers FB's News Feed Ranking Algorithm](https://engineering.fb.com/2021/01/26/ml-applications/news-feed-ranking/)

  + [Self-supervised Learning](https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence)

* [ML] Elvis's Blog (https://elvissaravia.substack.com/archive)

* [ML.NLP.DL] Eugene Yan's Blog (https://eugeneyan.com/)

  - [ML] On RecSys (https://eugeneyan.com/tag/recsys/)

  - [ML] RecSys 2020 - Takeaways and Notable Papers (https://eugeneyan.com/writing/recsys2020/)

  - [Ld] On Leadership (https://eugeneyan.com/tag/leadership/)

* [ML.GraphNN] [Theoretical Foundations of Graph Neural Networks by Petar Veličković on YouTube](https://www.youtube.com/watch?v=uF53xsT7mjc)

* [GraphML] GML in-depth: three forms of self-supervised learning (https://graphml.substack.com/p/self-supervised-learning)

* [GeoDL,GraphDL] Geometric Deep Learning - paper, blog, keynotes, lectures (https://geometricdeeplearning.com/)

* [GraphDL] [Introducton to Graph Deep Learning and Where It May Be Heading](https://medium.com/syncedreview/introduction-to-deep-learning-for-graphs-and-where-it-may-be-heading-75d48f42a322)

* [GraphML] [How to Get Started with Graph Machine Learning](https://gordicaleksa.medium.com/how-to-get-started-with-graph-machine-learning-afa53f6f963a)

* [SystemDesign] (Tiktok's Recommendation System) Monolith: Real Time Recommendation System with Collisionless Embedding Table (https://arxiv.org/abs/2209.07663)

[[back to top](#ml-bookmarks)]
ecosyste.ms

Data

Tools

Indexes

Applications

Experiments

Awesome

https://github.com/deehzee/ml-bookmarks

Awesome Lists containing this project

README
