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https://github.com/ahmedbelgacem/awesome-datascience

A curated list of awesome python, machine learning, computer vision and data science resources, articles, guides, courses and books.
https://github.com/ahmedbelgacem/awesome-datascience

List: awesome-datascience

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A curated list of awesome python, machine learning, computer vision and data science resources, articles, guides, courses and books.

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# Awesome-Datascience

This is mainly a curated list of awesome python, machine learning, computer vision and data science resources, articles, guides, courses and books. Since there's a lot of awesome lists for frameworks and libraries out there, I may add libraries, software and frameworks I find awesome but that's not the purpose of this list.  

This list will follow me along my journey and I will be updating it as I progress. I'm making this list for my personal use mainly but I'm sharing it here if it may help anyone.

Some of the courses are courses I had at my university that are available freely online.



- [Awesome Data Science](#awesome-datascience)

  - [AI and games](#ai-and-games)

  - [Computer Vision](#computer-vision)

  - [Data Engineering](#data-engineering)

  - [Deep Learning](#deep-learning)

  - [Edge Computing](#edge-computing)

  - [Exploratory Data Analysis](#exploratory-data-analysis)

  - [Fundamentals](#fundamentals)

  - [Graph Neural Networks](#graph-neural-networks)

  - [Generative Learning](#generative-learning)

  - [Information Retireval](#information-retrieval)

  - [Machine Learning](#machine-learning)

  - [Mathematics](#mathematics)

  - [Natural Language Processing](#natural-language-processing)

  - [Python](#python)

  - [Pytorch](#pytorch)

  - [Reinforcement Learning](#reinforcement-learning)

  - [Statistics](#statistics)

# AI and games:

  - [Intelligence artificielle, une approche ludique - Tristan Cazenave](https://basepub.dauphine.fr/handle/123456789/9340) (French Book)

  - ## Monte Carlo Search

    - [Monte Carlo Search - Tristan Cazenave](https://www.lamsade.dauphine.fr/~cazenave/MonteCarlo.pdf)

# Computer Vision:

- ## Classification: 

  - [An intuitive guide to deep network architectures](https://towardsdatascience.com/an-intuitive-guide-to-deep-network-architectures-65fdc477db41)

- ## Edge Detection:

    - [Canny Edge Detection - Step by Step in Python](https://towardsdatascience.com/canny-edge-detection-step-by-step-in-python-computer-vision-b49c3a2d8123)

    - [Quality Assessment Methods to Evaluate the Performance of Edge Detection Algorithms](https://ieeexplore.ieee.org/document/9454489)

- ## Object Detection:

    - [Deep Learning Bible 4- Object Detection](https://wikidocs.net/book/8119)

- ## Semantic Segmentation:

    - [A survey of loss functions for semantic segmentation](https://arxiv.org/pdf/2006.14822.pdf)

- ## Yolo:

    - [Understand yolov8 structure](https://github.com/akashAD98/yolov8_in_depth)

    - [YOLOv8: One Concept You Must Know in Object Detection — Letterbox](https://medium.com/mlearning-ai/letterbox-in-object-detection-77ee14e5ac46)

  - [The dumb reason your fancy Computer Vision app isn’t working: Exif Orientation](https://medium.com/@ageitgey/the-dumb-reason-your-fancy-computer-vision-app-isnt-working-exif-orientation-73166c7d39da)

# Data Engineering:

  - [Hadoop: The Definitive Guide](https://grut-computing.com/HadoopBook.pdf)

  - [Data-Intensive Text Processing with MapReduce](http://www.iro.umontreal.ca/~nie/IFT6255/Books/MapReduce.pdf)

# Deep Learning:

  - [Deep Learning Specialization - Andrew NG](https://www.coursera.org/specializations/deep-learning)

  - [A Recipe for Training Neural Networks - Andrej Karpathy](http://karpathy.github.io/2019/04/25/recipe/?fbclid=IwAR14qzU0WPypUSd2cJDn8_3GVDh6VjIcHBHcVJsLN9t7HtUkUfxzrluaaYY)

  - ## LSTM:

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

    - [Illustrated Guide to LSTM’s and GRU’s: A step by step explanation](https://towardsdatascience.com/illustrated-guide-to-lstms-and-gru-s-a-step-by-step-explanation-44e9eb85bf21)

  - ## Multi-Task Learning:

    - [Deep Multi-Task Learning — 3 Lessons Learned](https://towardsdatascience.com/deep-multi-task-learning-3-lessons-learned-7d0193d71fd6)

    - [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://arxiv.org/pdf/1705.07115.pdf)

    - [Multi-Task Learning with Pytorch and FastAI](https://towardsdatascience.com/multi-task-learning-with-pytorch-and-fastai-6d10dc7ce855)

    - [Multi Task Learning with Homoscedastic Uncertainty Implementation](https://github.com/ranandalon/mtl)

  - ## RNN:

    - [Illustrated Guide to Recurrent Neural Networks](https://towardsdatascience.com/illustrated-guide-to-recurrent-neural-networks-79e5eb8049c9)

  - ## SPP:

    - [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://arxiv.org/pdf/1406.4729v4.pdf)

# Edge computing:

  - ## Machine Learning Compilers, optimizers and Gpu accelerated deep learning:

    - [A friendly introduction to machine learning compilers and optimizers](https://huyenchip.com/2021/09/07/a-friendly-introduction-to-machine-learning-compilers-and-optimizers.html)

    - [The Correct Way to Measure Inference Time of Deep Neural Networks](https://deci.ai/blog/measure-inference-time-deep-neural-networks/)

    - ## TensorRT:

      - [Profiling Deep Learning Networks And Automatic Mixed Precision For Optimization](https://on-demand.gputechconf.com/gtc-cn/2019/pdf/CN9620/presentation.pdf)

# Exploratory Data Analysis:

  - ## Principal component analysis, Correspondence Analysis, Multiple Correspondence Analysis:

    - [Analyse des données - Patrice Bertrand et Denis Pasquignon](https://www.ceremade.dauphine.fr/~pasquignon/analyse-des-donnees-M1.pdf) (French Course)

# Fundamentals:

- ## Docker:

  - [Simplified guide to using Docker for local development environment](https://blog.atulr.com/docker-local-environment/)

  - [Use the same Dockerfile for both local development and production with multi-stage builds](https://blog.atulr.com/docker-local-production-image/)

  - [Introduction to docker - Datacamp](https://www.datacamp.com/courses/introduction-to-docker)

- ## Git:

  - [Introduction to Git for Data Science](https://www.datacamp.com/courses/introduction-to-git)

- ## Web Scraping:

  - [Web Scraping with Python](https://www.datacamp.com/courses/web-scraping-with-python)



# Graph Neural Networks:

  - [Machine Learning with Graphs - Stanford University](http://web.stanford.edu/class/cs224w/index.html#content)

  - [LiteratureDL4Graph, A comprehensive collection of recent papers on graph deep learning](https://github.com/DeepGraphLearning/LiteratureDL4Graph)

# Generative Learning:

  - ## Variational Autoencoders:

    - [The theory behind Latent Variable Models: formulating a Variational Autoencoder](https://theaisummer.com/latent-variable-models/#variational-autoencoders)

    - [How to Generate Images using Autoencoders](https://theaisummer.com/Autoencoder/)

  - ## Diffusion Models:

    - [Diffusion Theory](https://github.com/mikonvergence/DiffusionFastForward/blob/master/notes/01-Diffusion-Theory.md)

    - [How diffusion models work: the math from scratch](https://theaisummer.com/diffusion-models/?fbclid=IwAR1BIeNHqa3NtC8SL0sKXHATHklJYphNH-8IGNoO3xZhSKM_GYcvrrQgB0o)

# Information Retrieval:

  - [Introduction to Information Retrieval - Cambridge University, Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze](https://nlp.stanford.edu/IR-book/information-retrieval-book.html)

  - ## Semantic Search:

    - [Semantic Search - Sentence Transformers Documentation](https://www.sbert.net/examples/applications/semantic-search/README.html) - A guide on using sbert for semantic search

# Machine Learning:

  - [Machine Learning - Stanford University, Andrew NG](https://www.coursera.org/learn/machine-learning)

  - [Python Machine Learning, 3rd Edition - Sebastian Raschka , Vahid Mirjalili](https://sebastianraschka.com/books/#python-machine-learning-3rd-edition)

  - ## Boosting Algorithms:

    - [Boosting algorithms explained](https://towardsdatascience.com/boosting-algorithms-explained-d38f56ef3f30)

    - [A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning](https://machinelearningmastery.com/gentle-introduction-gradient-boosting-algorithm-machine-learning/)

  - ## Preparing for a machine learning interview:

    - [Introduction to Machine Learning Interviews](https://huyenchip.com/ml-interviews-book/)

    - ### Machine Learning System Design:

        - [Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications - Chip Huyen](https://www.amazon.com/Designing-Machine-Learning-Systems-Production-Ready/dp/1098107969)

  - ## Support Vector Machines:

    - [Support Vector Machines (SVM) - An overview](https://towardsdatascience.com/https-medium-com-pupalerushikesh-svm-f4b42800e989)

# Mathematics:

  - ## Optimization for Machine Learning:

    - [Optimization for Machine Learning - Clément Royer](https://www.lamsade.dauphine.fr/%7Ecroyer/ensdocs/OML/PolyOML.pdf)

# Natural Language Processing:

  - ## Transformers:

    - [Attention is all you need](https://arxiv.org/abs/1706.03762)

    - [Hugging Face Course](https://huggingface.co/course/chapter1/1)

# Python:

- [Learning Python: Powerful Object-Oriented Programming - Mark Lutz](https://www.amazon.com/Learning-Python-Powerful-Object-Oriented-Programming-ebook/dp/B00DDZPC9S/ref=as_li_ss_tl?crid=2OZ9IA8BKEZKO&dchild=1&keywords=python+programming+language&qid=1588165545&sprefix=python+progra,aps,315&sr=8-3&linkCode=sl1&tag=adilet-20&linkId=bbe99ec15d04e43dd44966d937725cad&language=en_US)

- ## Clean Code:

  - [How to write beautiful python code with PEP 8](https://realpython.com/python-pep8/)  

  - [5 Different Meanings of Underscore in Python](https://towardsdatascience.com/5-different-meanings-of-underscore-in-python-3fafa6cd0379)

  - [f-Strings: A New and Improved Way to Format Strings in Python](https://realpython.com/python-f-strings/#f-strings-a-new-and-improved-way-to-format-strings-in-python)

  - [The Zen of Python](https://zen-of-python.info/)

  - [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html)

  - [PEP8 online checker](http://pep8online.com)

- ## Data Structures:

  - [Binary Tree](https://emre.me/data-structures/binary-tree/)

  - [Binary Search Tree](https://emre.me/data-structures/binary-search-trees/)

  - [Queue in Python](https://www.geeksforgeeks.org/queue-in-python/)

  - [Sets in Python](https://realpython.com/python-sets/)

- ## Distribution:

  - [How to create a Python library](https://medium.com/analytics-vidhya/how-to-create-a-python-library-7d5aea80cc3f)

  - [How to upload your python package to PyPi](https://medium.com/@joel.barmettler/how-to-upload-your-python-package-to-pypi-65edc5fe9c56)

- ## Documentation:

  - [Python Docstrings](https://www.datacamp.com/community/tutorials/docstrings-python)

  - [Auto-documenting a python project using Sphinx](https://betterprogramming.pub/auto-documenting-a-python-project-using-sphinx-8878f9ddc6e9)

- ## Efficient Code:

  - [Code Profiling](https://towardsdatascience.com/a-quick-and-easy-guide-to-code-profiling-in-python-58c0ed7e602b)

  - [Python's Counter: The Pythonic Way to Count Objects](https://realpython.com/python-counter/)

  - [Unpacking in Python](https://stackabuse.com/unpacking-in-python-beyond-parallel-assignment/)

  - [Using the Python zip() Function for Parallel Iteration](https://realpython.com/python-zip-function/)

- ## Generators:

  - [Python yield, Generators and Generator Expressions](https://www.programiz.com/python-programming/generator?fbclid=IwAR2aGKoKpvMJy2R-jINBQMM7qcOeBczS192k1c2TZYwiV7YAKc1XKhboS4k)

  - [How to Use Generators and yield in Python](https://realpython.com/introduction-to-python-generators/)

- ## Metaclasses:

  - [Python Metaclasses](https://www.godaddy.com/engineering/2018/12/20/python-metaclasses/)

- ## Tools and development environements: 

  - ### Jupyter Notebook

    - [7 essential tips for writing with jupyter notebook](https://towardsdatascience.com/7-essential-tips-for-writing-with-jupyter-notebook-60972a1a8901)

- ## Preparing for a coding interview:

  - [CP-Algorithms.com](https://cp-algorithms.com/)

  - [Competitive Programmer’s Handbook - Antti Laaksonen (2018)](https://cses.fi/book/book.pdf)

  - [Grokking LeetCode: A Smarter Way to Prepare for Coding Interviews](https://medium.com/interviewnoodle/grokking-leetcode-a-smarter-way-to-prepare-for-coding-interviews-e86d5c9fe4e1)

  - [Interview School](https://interviews.school)

  - [Introduction to Algorithms, 3rd Edition](https://www.amazon.com/dp/0262033844/ref=as_li_ss_tl?ie=UTF8&linkCode=sl1&tag=adilet-20&linkId=925f749322dc9e4485887dce6cbc8248&language=en_US)

  - [Pramp - Mock interviews with peers](https://www.pramp.com/#/)

  - ### Algorithms:

      - [Dynamic Programming](https://emre.me/algorithms/dynamic-programming/) 

      - [Greedy Algorithms](https://emre.me/algorithms/greedy-algorithms/)

  - ### Python implementation for different coding problem patterns:

      - [Coding Patterns: In-place Reversal of a Linked List](https://emre.me/coding-patterns/in-place-reversal-of-a-linked-list/)

      - [4 types of tree traversal algorithms (Java implementation)](https://towardsdatascience.com/4-types-of-tree-traversal-algorithms-d56328450846)

      - [Coding Patterns: Depth First Search (DFS)](https://emre.me/coding-patterns/depth-first-search/)

      - [Coding Patterns: Breadth First Search (BFS)](https://emre.me/coding-patterns/breadth-first-search/)

# Pytorch: 

- [Pytorch 101: An applied tutorial](https://www.youtube.com/watch?v=_R-mvKBD5U8&list=PL98nY_tJQXZln8spB5uTZdKN08mYGkOf2&index=1)

- [Faster Deep Learning Training with PyTorch – a 2021 Guide](https://efficientdl.com/faster-deep-learning-in-pytorch-a-guide/)

# Reinforcement Learning:

- [Reinforcement Learning An Introduction - Richard S.Sutton, Andrew G. Barto](https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf)

- [Reinforcement Learning - Stéphane Airiau](https://www.lamsade.dauphine.fr/~airiau/Teaching/M2-IASDapp-RL/) (French Course)

# Statistics:

- ## Estimation, Confidence Interval, Hypothesis Testing:

  - [Statistique mathématique - Vincent Rivoirard](https://www.ceremade.dauphine.fr/~rivoirar/Poly-L3-StatMath.pdf) (French Course)

  - [The Bootstrap Method for Standard Errors and Confidence Intervals](https://www.dummies.com/article/academics-the-arts/science/biology/the-bootstrap-method-for-standard-errors-and-confidence-intervals-164614/)

- ## Monte Carlo Method:

  - [Méthodes de Monte Carlo - Julien Stoehr](https://www.ceremade.dauphine.fr/~stoehr/data/medias/001/m1_monte_carlo/cm_monte_carlo.pdf) (French Course)