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  • 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
  • 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
  • Deep Learning Bible 4- Object Detection
  • A survey of loss functions for semantic segmentation
  • Understand yolov8 structure
  • YOLOv8: 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