awesome-ai-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-ai-datascience
Last synced: 8 days ago
JSON representation
-
Uncategorized
-
Uncategorized
- Introduction to Machine Learning Interviews
- Google Python Style Guide
- Very Deep Convolutional Networks for Large-Scale Image Recognition (VGG Paper)
- Attention is all you need
- Machine Learning - Stanford University, Andrew NG
- Pramp - Mock interviews with peers
- A Few Useful Things to Know About Machine Learning
- The Illustrated Transformer
- Tips for Writing Technical Papers
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- Deep Learning Specialization - Andrew NG
- CP-Algorithms.com
- DiffusionFastForward - Diffusion Theory
- How diffusion models work: the math from scratch
- The Annotated Diffusion Model
- Understanding Diffusion Models: A Unified Perspective
- Diffusion models from scratch in PyTorch - Deep Findr
- Machine Learning with Graphs - Stanford University
- Mask R-CNN
- Transformers from scratch
- Semantic Search - Sentence Transformers Documentation - A guide on using sbert for semantic search
- Multimodal Neurons in Artificial Neural Networks
- U-Net: Convolutional Networks for Biomedical Image Segmentation
- Learn in public - swyx
- Diffusion Models Beat GANs on Image Synthesis
- Denoising Diffusion Implicit Models
- Denoising Diffusion Probabilistic Models
- Deep Unsupervised Learning using Nonequilibrium Thermodynamics
- YOLOv4: Optimal Speed and Accuracy of Object Detection
- Mixed-precision training
- Bag of Tricks for Image Classification with Convolutional Neural Networks
- Competitive Programmer’s Handbook - Antti Laaksonen (2018)
- Introduction to Information Retrieval - Cambridge University, Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze
- Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
- Python Machine Learning, 3rd Edition - Sebastian Raschka , Vahid Mirjalili
- Understanding LSTM Networks
- How to upload your python package to PyPi
- How to write beautiful python code with PEP 8
- What is YOLOv5? A Guide for Beginners.
- What are Diffusion Models?
- Python Metaclasses
- Auto-Encoding Variational Bayes
- The Zen of Python
- Fast R-CNN
- A friendly introduction to machine learning compilers and optimizers
- Going Deeper with Convolutions (Inception Paper)
- Reinforcement Learning An Introduction - Richard S.Sutton, Andrew G. Barto
- Deep Residual Learning for Image Recognition (ResNet Paper)
- Xception: Deep Learning with Depthwise Separable Convolutions (Xception Paper)
- MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (MobileNet Paper)
- PEP8 online checker
- LiteratureDL4Graph, A comprehensive collection of recent papers on graph deep learning
- Intelligence artificielle, une approche ludique - Tristan Cazenave
- Monte Carlo Search - Tristan Cazenave
- An intuitive guide to deep network architectures
- Canny Edge Detection - Step by Step in Python
- Quality Assessment Methods to Evaluate the Performance of Edge Detection Algorithms for Digital Image: A Systematic Literature Review
- Deep Learning Bible - 4. Object Detection
- A survey of loss functions for semantic segmentation
- Understand yolov8 structure
- YOLOv11: One Concept You Must Know in Object Detection — Letterbox
- The dumb reason your fancy Computer Vision app isn’t working: Exif Orientation
- Hadoop: The Definitive Guide
- Data-Intensive Text Processing with MapReduce
- A Recipe for Training Neural Networks - Andrej Karpathy
- Illustrated Guide to LSTM’s and GRU’s: A step by step explanation
- Deep Multi-Task Learning — 3 Lessons Learned
- Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
- Multi-Task Learning with Pytorch and FastAI
- Multi Task Learning with Homoscedastic Uncertainty Implementation
- Illustrated Guide to Recurrent Neural Networks
- Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
- Analyse des données - Patrice Bertrand et Denis Pasquignon (French Course)
- Simplified guide to using Docker for local development environment
- Use the same Dockerfile for both local development and production with multi-stage builds
- Introduction to docker - Datacamp
- Introduction to Git for Data Science
- Web Scraping with Python
- The theory behind Latent Variable Models: formulating a Variational Autoencoder
- How to Generate Images using Autoencoders
- Boosting algorithms explained
- A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning
- Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications - Chip Huyen
- Optimization for Machine Learning - Clément Royer
- Learning Python: Powerful Object-Oriented Programming - Mark Lutz
- 5 Different Meanings of Underscore in Python
- f-Strings: A New and Improved Way to Format Strings in Python
- Binary Tree
- Binary Search Tree
- Queue in Python
- Sets in Python
- How to create a Python library
- Python Docstrings
- Auto-documenting a python project using Sphinx
- Code Profiling
- Python's Counter: The Pythonic Way to Count Objects
- Unpacking in Python
- Using the Python zip() Function for Parallel Iteration
- Python yield, Generators and Generator Expressions
- How to Use Generators and yield in Python
- 7 essential tips for writing with jupyter notebook
- Grokking LeetCode: A Smarter Way to Prepare for Coding Interviews
- Interview School
- Introduction to Algorithms, 3rd Edition
- Dynamic Programming
- Greedy Algorithms
- Coding Patterns: In-place Reversal of a Linked List
- 4 types of tree traversal algorithms (Java implementation)
- Coding Patterns: Depth First Search (DFS)
- Coding Patterns: Breadth First Search (BFS)
- Pytorch 101: An applied tutorial
- Faster Deep Learning Training with PyTorch – a 2021 Guide
- Reinforcement Learning - Stéphane Airiau
- Statistique mathématique - Vincent Rivoirard
- The Bootstrap Method for Standard Errors and Confidence Intervals
- Méthodes de Monte Carlo - Julien Stoehr
- An intuitive guide to deep network architectures
- Canny Edge Detection - Step by Step in Python
- An intuitive guide to deep network architectures
- Canny Edge Detection - Step by Step in Python
- An intuitive guide to deep network architectures
- Canny Edge Detection - Step by Step in Python
- An intuitive guide to deep network architectures
- Canny Edge Detection - Step by Step in Python
- An intuitive guide to deep network architectures
- Canny Edge Detection - Step by Step in Python
- An intuitive guide to deep network architectures
- Canny Edge Detection - Step by Step in Python
- An intuitive guide to deep network architectures
- Canny Edge Detection - Step by Step in Python
- An intuitive guide to deep network architectures
- Canny Edge Detection - Step by Step in Python
- An intuitive guide to deep network architectures
- Canny Edge Detection - Step by Step in Python
- An intuitive guide to deep network architectures
- Canny Edge Detection - Step by Step in Python
- An intuitive guide to deep network architectures
- Canny Edge Detection - Step by Step in Python
- An intuitive guide to deep network architectures
- Canny Edge Detection - Step by Step in Python
- Long Short-Term Memory
- An intuitive guide to deep network architectures
- Canny Edge Detection - Step by Step in Python
- An intuitive guide to deep network architectures
- Canny Edge Detection - Step by Step in Python
- An intuitive guide to deep network architectures
- Canny Edge Detection - Step by Step in Python
- An intuitive guide to deep network architectures
- Canny Edge Detection - Step by Step in Python
- An intuitive guide to deep network architectures
- Canny Edge Detection - Step by Step in Python
- Intelligence artificielle, une approche ludique - Tristan Cazenave
- Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network
- ResNet strikes back: An improved training procedure in timm
- How to Train State-Of-The-Art Models Using TorchVision’s Latest Primitives
- Deep Learning Bible - 2. Classification
- Fine-Grained Image Analysis with Deep Learning: A Survey
- SSD: Single Shot MultiBox Detector
- YOLOv3: An Incremental Improvement
- YOLOv10: Real-Time End-to-End Object Detection
- Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression
- Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression
- yolov8_in_depth
- Hadoop: The Definitive Guide
- The Turing Way handbook
- Canny Edge Detection Step by Step in Python
- Deep Reinforcement Learning: Pong from Pixels
- Machine Learning with Graphs - Stanford University
- The 2025 AI Engineer Reading List
- A Recipe for Training Neural Networks
- GroupNorm,BatchNorm,InstanceNorm,LayerNorm
- Illustrated Guide to LSTM’s and GRU’s: A step by step explanation
- Illustrated Guide to Recurrent Neural Networks
- Profiling Deep Learning Networks And Automatic Mixed Precision For Optimization
- A Data Scientist’s Guide to Docker Containers
- MIT 6.S191 Introduction to Deep Learning Lecture 4: Deep Generative Modeling
- Tutorial on Diffusion Models for Imaging and Vision
- Training with Diffusers - Example
- Making a Class-Conditioned Diffusion Model
- Speeding Up Diffusion Sampling
- An Introduction to Support Vector Machine (SVM) in Python
- Optimization for Machine Learning - Clément Royer
- Large Language Models: A Survey
- Positional Encoding in Transformer
- Sinusoidal Positional Encoding
- Why Generative-AI Apps’ Quality Often Sucks and What to Do About It
- Python Cookbook
- Python Formatter Beautifier
- 7 ways to implement DTOs in Python and what to keep in mind
- How to Use Python Data Classes in 2023
- Code Profiling
- How to Practice LeetCode Problems (The Right Way)
- Grokking LeetCode: A Smarter Way to Prepare for Coding Interviews
- Pytorch 101: An applied tutorial
- PyTorch Training Performance Guide
- How To Train Your PyTorch Models (Much) Faster
- How To Train Your PyTorch Models With Less Memory
- Pytorch autograd explained
- Faster Deep Learning Training with PyTorch – a 2021 Guide
- Here are 17 ways of making PyTorch training faster
- Performance Tuning Guide
- Mixed Precision - PyTorch Training Performance Guide
- Do not end the week with nothing - Patio11
- You Are Not Too Old (To Pivot Into AI)
- History of Deep Learning
- A Survival Guide to a PhD
- Career advice for recent Computer Science graduates
- What’s the Right Way to Find a Mentor?
- Comprehensive Guide to Crafting a Perfect CV in Data Science
- Sayak Paul FAQ
- Improving as an ML Practitioner
- Practicing AI Research
- Why you (yes, you) should blog
- "advice" for aspiring tech bloggers
- Your Tech Resume is Garbage: Here’s How To Fix It
- Ten Tips for a (Slightly) Less Awful Resume
- My Learning to Be Hired Again After a Year… Part 2
- Auto-documenting a python project using Sphinx
- Grokking LeetCode: A Smarter Way to Prepare for Coding Interviews
- How To Train Your PyTorch Models (Much) Faster
- How To Train Your PyTorch Models With Less Memory
- A Survival Guide to a PhD
- Queue in Python
- Learning Python: Powerful Object-Oriented Programming - Mark Lutz
- An Intuitive Guide to Deep Network Architectures - Towards Data Science
- Canny Edge Detection Step by Step in Python
- Illustrated Guide to LSTM’s and GRU’s: A step by step explanation
- Multi-Task Learning with Pytorch and FastAI
- Illustrated Guide to Recurrent Neural Networks
- MIT 6.S191 Introduction to Deep Learning Lecture 4: Deep Generative Modeling
- Quantifying Surprise – A Data Scientist’s Intro To Information Theory
- Boosting algorithms explained
- 5 Different Meanings of Underscore in Python
- Python Docstrings
- Code Profiling
- 7 essential tips for writing with jupyter notebook
- Introduction to Algorithms, 3rd Edition
- Interview School
- 4 types of tree traversal algorithms (Java implementation)
- Performance Tuning Guide
- My Personal Formula for a Winning Resume
-
Programming Languages
Categories
Sub Categories