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: 2 days ago
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- 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
- A survey of loss functions for semantic segmentation
- 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 - Towards Data Science
- 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
- A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning
- Introduction to Machine Learning Interviews
- Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications - Chip Huyen
- 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
- Introduction to Algorithms, 3rd Edition
- Pramp - Mock interviews with peers
- Dynamic Programming
- Data-Intensive Text Processing with MapReduce
- Deep Learning Specialization - Andrew NG
- A Recipe for Training Neural Networks - Andrej Karpathy
- Understanding LSTM Networks
- Multi-Task Learning with Pytorch and FastAI
- Multi Task Learning with Homoscedastic Uncertainty Implementation
- Illustrated Guide to Recurrent Neural Networks
- Introduction to docker - Datacamp
- Introduction to Git for Data Science
- 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
- Learning Python: Powerful Object-Oriented Programming - Mark Lutz
- How to write beautiful python code with PEP 8
- 5 Different Meanings of Underscore in Python
- f-Strings: A New and Improved Way to Format Strings in Python
- The Zen of Python
- Google Python Style Guide
- PEP8 online checker
- Binary Tree
- Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
- A friendly introduction to machine learning compilers and optimizers
- Analyse des données - Patrice Bertrand et Denis Pasquignon
- Simplified guide to using Docker for local development environment
- Use the same Dockerfile for both local development and production with multi-stage builds
- The theory behind Latent Variable Models: formulating a Variational Autoencoder
- How to Generate Images using Autoencoders
- Diffusion Theory
- How diffusion models work: the math from scratch
- Introduction to Information Retrieval - Cambridge University, Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze
- Semantic Search - Sentence Transformers Documentation - A guide on using sbert for semantic search
- Machine Learning - Stanford University, Andrew NG
- Web Scraping with Python
- Machine Learning with Graphs - Stanford University
- LiteratureDL4Graph, A comprehensive collection of recent papers on graph deep learning
- Python Machine Learning, 3rd Edition - Sebastian Raschka , Vahid Mirjalili
- Boosting algorithms explained
- Queue in Python
- How to create a Python library
- How to upload your python package to PyPi
- Binary Search Tree
- Support Vector Machines (SVM) - An overview
- Optimization for Machine Learning - Clément Royer
- Attention is all you need
- Using the Python zip() Function for Parallel Iteration
- Python's Counter: The Pythonic Way to Count Objects
- Python yield, Generators and Generator Expressions
- Code Profiling
- Sets in Python
- Python Docstrings
- How to Use Generators and yield in Python
- Python Metaclasses
- 7 essential tips for writing with jupyter notebook
- CP-Algorithms.com
- Competitive Programmer’s Handbook - Antti Laaksonen (2018)
- Grokking LeetCode: A Smarter Way to Prepare for Coding Interviews
- Interview School
- Auto-documenting a python project using Sphinx
- Unpacking in Python
- 4 types of tree traversal algorithms (Java implementation)
- Coding Patterns: Depth First Search (DFS)
- Coding Patterns: Breadth First Search (BFS)
- Méthodes de Monte Carlo - Julien Stoehr
- Greedy Algorithms
- Coding Patterns: In-place Reversal of a Linked List
- Pytorch 101: An applied tutorial
- Faster Deep Learning Training with PyTorch – a 2021 Guide
- Reinforcement Learning An Introduction - Richard S.Sutton, Andrew G. Barto
- Reinforcement Learning - Stéphane Airiau
- Statistique mathématique - Vincent Rivoirard
- The Bootstrap Method for Standard Errors and Confidence Intervals
- 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
- What is YOLOv5? A Guide for Beginners.
- 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
- Mask R-CNN
- U-Net: Convolutional Networks for Biomedical Image Segmentation
- Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
- Canny Edge Detection Step by Step in Python
- Hadoop: The Definitive Guide
- Intelligence artificielle, une approche ludique - Tristan Cazenave
- Deep Residual Learning for Image Recognition (ResNet Paper)
- Going Deeper with Convolutions (Inception Paper)
- MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (MobileNet Paper)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (VGG Paper)
- Xception: Deep Learning with Depthwise Separable Convolutions (Xception Paper)
- Deep Learning Bible - 2. Classification
- Fine-Grained Image Analysis with Deep Learning: A Survey
- Fast R-CNN
- Bag of Tricks for Image Classification with Convolutional Neural Networks
- Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network
- ResNet strikes back: An improved training procedure in timm
- An Intuitive Guide to Deep Network Architectures - Towards Data Science
- How to Train State-Of-The-Art Models Using TorchVision’s Latest Primitives
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- SSD: Single Shot MultiBox Detector
- YOLOv3: An Incremental Improvement
- YOLOv4: Optimal Speed and Accuracy of Object Detection
- YOLOv10: Real-Time End-to-End Object Detection
- 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
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