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LearningFromTheBest

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This project is to list the best books, courses, tutorial, methods on learning certain knowledge

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# LearningFromTheBest
This project is to list the best books, courses, tutorial, methods on learning certain knowledge, for free

<h2>Machine Learning Courses and Books:</h2>
Introduction Course: Coursera's "Machine Learning" by Andrew Ng. 
 -- 2008 youtube version is not introducionary
 -- Do NOT spend time on Octave/Matlab unless you already knew it
Introduction Course2(I think this is the best one): Andrew Ng's deeplearing.ai course: https://www.youtube.com/channel/UCcIXc5mJsHVYTZR1maL5l9w/playlists 
Introduction Course 3: Caltech "Learning from Data" by Yasar
Introduction Course 4: Fast.ai "Practical Deep Learning for Coders" by Jeremy Howard. It uses PyTorch 
Tutorials on different topics: http://bit.ly/36skFE7 
Advanced Courses: Stanford cs231n(CNN) and cs224n(NLP) 
Book on DeepLearning: www.deeplearningbook.com (by now, it's the only one and it's free)
Book on Reinforcement Learning: Reinforcement Learning: An Introduction 
Course on Reinforcement Learning: David Silver's course 
Course on Advanced Reinforcement Learning: UCB CS294 fall 2017/2018, UCB CS285 2019 
How to read ML papers: https://deeps.site/blog/2019/10/14/reading-research-papers-career-advice/ 
Deep Bayesian Network: https://github.com/bayesgroup/deepbayes-2019 
Data Driver Algorithm Design: https://sites.google.com/view/cs-159-spring-2020/

<h2>Machine Learning Papers, blogs, conferences:</h2>
Paper trend: http://www.arxiv-sanity.com/ 
Paper with cite/references/categorize: https://www.semanticscholar.org/paper/ 
Paper with review: https://openreview.net/ 
Paper Explained: https://towardsdatascience.com/ w
Paper with Code: https://paperswithcode.com/ 
Berkeley: https://bair.berkeley.edu/blog/ 
OpenAI: https://spinningup.openai.com/en/latest/index.html 
DeepMind Research/Blog: https://deepmind.com/research 
CrossMind: https://crossminds.ai/ 
ICML(RL): https://slideslive.com/38930488/modelbased-methods-in-reinforcement-learning-part-1-introduction-learning-models

<h2>Personal Blogs </h2>
RL blog(Onwer is working on bitcoin and stopped updating): http://www.wildml.com/ 
Lex podcast: https://lexfridman.com/ai/ 
LiLian's RL blog: https://lilianweng.github.io/lil-log/ 
Import AI(recommended by quite some) https://jack-clark.net/ 
Best papers collection: https://jeffhuang.com/best_paper_awards.html 
Colah's blog(famous for LSTM): https://colah.github.io/ 
Someone's paper collection (I'm doing similar): https://github.com/number9473/nn-algorithm/issues 
Pinterest Practical MultiTask AutoML: https://medium.com/pinterest-engineering/how-we-use-automl-multi-task-learning-and-multi-tower-models-for-pinterest-ads-db966c3dc99e

<h2>RL Framework</h2>
openAI gym (most widely used)
openAI procGen: https://openai.com/blog/procgen-benchmark/ 
facebook ELF 
deepmind pysc2 
deepmind opensiel(C++ and Python, both env and algo): https://github.com/deepmind/open_spiel 
Card: https://github.com/datamllab/rlcard 
Portfolio Management(based on Gym): https://github.com/MRYingLEE/Portfolio_GYM 
Portfolio Management(no git repo): https://www.slideshare.net/KamerAliYuksel/deep-reinforcement-learning-portfolio-management 
Benchmark site: https://app.wandb.ai/cleanrl/cleanRL?workspace=user-

<h2>ML Tools, Playground, and Framework</h2>
Googl OR tools: https://developers.google.com/optimization 
Google Colab: https://colab.research.google.com/notebooks/welcome.ipynb

<h2>Github repo for ML </h2>
OpenAI Baseline(TF1.x): 
Stable Baseline(better Baseline, with dis/cont comments): https://github.com/hill-a/stable-baselines 
TensorLayer RLZoo(TF2.0): https://github.com/tensorlayer/RLzoo 
ML in IC: https://github.com/kouroshHakha/bag_deep_ckt 
PathFinding: https://github.com/cair/deep-maze 
Alpha-Star: https://github.com/imagry/aleph_star 
VIN: https://github.com/kentsommer/pytorch-value-iteration-networks 
GNN: https://github.com/thunlp/GNNPapers 
GNN(NTU): https://graphdeeplearning.github.io/ 
NLP progress: https://github.com/sebastianruder/NLP-progress 
PyTorch RL: http://github.com/shangtongzhang/DeepRL
PyTorch Baseline(with c++ implementation too): https://github.com/navneet-nmk/pytorch-rl 
Alpha-Zero: https://github.com/suragnair/alpha-zero-general 
Replay Buffers: https://ymd_h.gitlab.io/cpprb/examples/ 
MultiAgent: https://github.com/ChenglongChen/pytorch-MADRL
DGL(from aws): https://github.com/dmlc/dgl 
Karpathy(minGpt is interesting): https://github.com/karpathy

<h2>ML Contest</h2>
House Price Predicting(hybrid input): https://www.kaggle.com/serigne/stacked-regressions-top-4-on-leaderboard 
Power Grid Management: https://l2rpn.chalearn.org/power-grid-in-action 
Kaggle 2-Sigma Financial Modeling: https://www.kaggle.com/c/two-sigma-financial-modeling 
Kaggle 2-Sigma Financial News: https://www.kaggle.com/c/two-sigma-financial-news

Path Finding 
Combining Q-Learning and Search with Amortized Value Estimates https://arxiv.org/abs/1912.02807 
Reinforcement Learning with A* and a Deep Heuristic https://arxiv.org/abs/1811.07745

Progressive tracking 
 Object Detection: https://github.com/yehengchen/Object-Detection-and-Tracking 
 RL: http://louiskirsch.com/maps/reinforcement-learning 
 GNN: https://github.com/thunlp/GNNPapers#natural-language-processing

PyTorch Explained: https://arxiv.org/abs/1912.01703v1 </> 
 Hardware-Software Co-Optimization: https://tvm.apache.org/

<h2>General Algorithm:</h2>
Course: MIT "Introduction to Algorithms"
Book: MIT "Introduction to Algorithms"
Site: Leetcode.com 
Algorithm with Python: http://interactivepython.org/runestone/static/pythonds/Introduction/toctree.html

<h2>Linear Algebra:</h2>
Course: MIT "Linear Algebra" by Strang
Introduction Book: "Linear Algebra Done Right"
Book on Nemeric: Matrix

<h2>Statistics:</h2>
Course: MIT https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-041sc-probabilistic-systems-analysis-and-applied-probability-fall-2013/ 
Packages: pymc3 https://docs.pymc.io/

<h2>Python:</h2>
Introduction Book: Learning Python
Advanced Book: Python document
Advanced Tutorial: Stackoverflow
Packages: NumPy, SciPy, Pandas
<h3>Python GUI package: </h3>
Eel:A little Python library for making simple Electron-like HTML/JS GUI apps.(See Electron) https://github.com/samuelhwilliams/Eel 
pywebview: Build GUI for your Python program with JavaScript, HTML, and CSS (wrapper around webview, display it in gui) 
flaskwebgui: Create desktop applications with Flask (light weight and simple webview wrapper) https://github.com/ClimenteA/flaskwebgui
PySimpleGUI: Create custom layout GUI's simply(convert tkinter, Qt, Remi, WxPython): https://github.com/PySimpleGUI/PySimpleGUI 
Tips on profit: https://cryptolens.io/2019/11/tips-on-monetizing-python-applications/

<h2>CPP:</h2>
C++ FAQ: http://yosefk.com/c++fqa/templates.html

<h2>JavaScript:</h2>
Advanced Book: Oreilly "Javascript the Good Parts" by Douglas Crockford

<h2>DataVisualization(D3):</h2>
Introduction Book: Oreilly "Interactive Data Visualization for the web" by Scott Murphy
Web Package: D3
Python Package: vispy, bokeh, dash(plotly)
Frameworks: grafana, superset

<h2>Quant and ML:</h2>
Introduction Book: "Algorithmic Trading" by Ernie Chan
Course: Coursera "Quantization Financial" (only one really covers this topic?) 
Paper Site: qwafafew.org 
Resource Site: https://www.quantopian.com/tutorials
Another Quant Site: https://quantocracy.com/ 
Packages: zipline, pyalgotrade, vnpy, easytrade
2-sigma: https://www.twosigma.com/insights/article/25-of-our-favorite-papers-talks-presentations-and-workshops-from-nips-2017/ 
Financial Sentiment Analysis: github.com/ProsusAI/finBERT 
Sentiment Analysis in Action: http://www.alternative-analytics.eu/dashboard/sentiment.html 
Advances in Financial Machine Learning: https://github.com/hudson-and-thames/research 
NewsCatcher(w/o NLP process): https://github.com/kotartemiy/newscatcher 
Cointegration: https://github.com/daehkim/pair-trading/blob/master/pairSelection.ipynb 
Pair Trading 1: https://israeldi.github.io/coursework/EECS545/545_Final_Project.pdf

<h3>Quant and ML -- Papers </h3>
 Universal features of price formation in financial markets: perspectives from Deep Learning https://arxiv.org/abs/1803.06917

<h2>DataBase:</h2>
Introduction Book:
Course:
TimeSeries DataBase: InfluxDB (becuase of IoT)
DB: mysql/mariadb (easy available); postgresql (solid)

---- Not Necessary Best Resource ----
<h2>Bioinformatics:</h2>
Deep Learning Usage Summary: "Deep learning in bioinformatics"(https://academic.oup.com/bib/article/18/5/851/2562808)
Alternative Deep Learning usage Analysis: "Opportunities and obstacles for deep learning in biology and medicine" (http://rsif.royalsocietypublishing.org/content/15/141/20170387#sec-15) 
Online Course: "Deep Learning in Genomics and Biomedicine" (https://canvas.stanford.edu/courses/51037) 
Bio Paper Site: bioarxiv.org

Gene Sequencing Read:
 Nanopore tech: "The evolution of nanopore sequencing" (https://www.frontiersin.org/articles/10.3389/fgene.2014.00449/full) 
 Tool Comparison: "Comprehensive comparison of Pacific Biosciences and Oxford Nanopore Technologies and their applications to transcriptome analysis" (https://f1000research.com/articles/6-100/v1) 
 NanoPore practice: DeepNano: Deep Recurrent Neural Networks for Base Calling in MinION Nanopore Reads http://compbio.fmph.uniba.sk/deepnano/ 
 Machine Learning in Gene: https://www.biorxiv.org/content/biorxiv/early/2015/11/16/031906.full.pdf 
 RNN in sequencing: An Empirical Evaluation of Generic Convolutional and Recurrent Networks
for Sequence Modeling https://arxiv.org/pdf/1803.01271.pdf

<h2>Cancer Imaging:</h2>
Cone Beam Imaging: "https://sinews.siam.org/Details-Page/achieving-real-time-cone-beam-ct-reconstruction-1" 
Image Reconstruct: "A Deep Learning Architecture for Limited-Angle Computed Tomography Reconstruction" (https://pdfs.semanticscholar.org/ae41/8b253c235138ef1671fa1053be2f17ef2aa8.pdf) 
Throax Disease Classification: https://arxiv.org/pdf/1801.09927.pdf

<h2>Education:</h2>
How to Get Into Harvard and the Ivy League: https://blog.prepscholar.com/how-to-get-into-harvard-and-the-ivy-league-by-a-harvard-alum#part2

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