Pinned Repositories
Algorithm_Coursera_Stanford
awesome-adversarial-examples-dl
A curated list of awesome resources for adversarial examples in deep learning
awesome-deep-learning-papers
The most cited deep learning papers
awesome-Federated-Learning
federated-learning
Awesome-Federated-Learning-Papers
:closed_book: :sunglasses: Mostly a collection of research papers categorized into broad topics in federated learning.
Awesome-Federated-Machine-Learning
Everything about federated learning, including research papers, books, codes, tutorials, videos and beyond
DeepLearning.ai-Summary
This repository contains my personal notes and summaries on DeepLearning.ai specialization courses. I've enjoyed every little bit of the course hope you enjoy my notes too.
MLBD
Materials for "Machine Learning on Big Data" course
papers
:paperclip: Summaries of papers on deep learning
UCSanDiego_DataStructure
In this online course, I will use and analyze data structures such as linked lists, trees, and hashtables. I also argue and evaluate algorithmic performance by applying asymptotic Big-O analysis.
saigontrade88's Repositories
saigontrade88/awesome-Federated-Learning
federated-learning
saigontrade88/Awesome-Federated-Machine-Learning
Everything about federated learning, including research papers, books, codes, tutorials, videos and beyond
saigontrade88/annotated_deep_learning_paper_implementations
🧑🏫 50! Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
saigontrade88/attack-and-defense-methods
A curated list of papers on adversarial machine learning (adversarial examples and defense methods).
saigontrade88/awesome-ml-for-cybersecurity
:octocat: Machine Learning for Cyber Security
saigontrade88/Bag-of-Tricks-for-AT
Empirical tricks for training robust models (ICLR 2021)
saigontrade88/continuum
A clean and simple data loading library for Continual Learning
saigontrade88/DDataParallelTemplate
saigontrade88/Distributed-Machine-Learning-with-Python
Distributed Machine Learning with Python, published by Packt
saigontrade88/FedDANE
FedDANE: A Federated Newton-Type Method (Asilomar Conference on Signals, Systems, and Computers ‘19)
saigontrade88/Federated-Averaging-PyTorch
An unofficial PyTorch implementation of a federated learning algorithm, FedAvg.
saigontrade88/federated-learning-lib_DangL
A library for federated learning (a distributed machine learning process) in an enterprise environment.
saigontrade88/Federated-Learning-PyTorch
Implementation of Communication-Efficient Learning of Deep Networks from Decentralized Data
saigontrade88/FedMA
Code for Federated Learning with Matched Averaging, ICLR 2020.
saigontrade88/FedProx
Federated Optimization in Heterogeneous Networks (MLSys '20)
saigontrade88/fedvote
saigontrade88/gpu_programming_intro
saigontrade88/Hands-On-Transfer-Learning-with-Python
Hands On Transfer Learning with Python, published by Packt
saigontrade88/loss-landscape
Code for visualizing the loss landscape of neural nets
saigontrade88/nngeometry
NNGeometry is a PyTorch library for computing Fisher Information Matrices and Neural Tangent Kernels
saigontrade88/ntk-fed
saigontrade88/Practicing-Federated-Learning
saigontrade88/Predictive-Coding-FL
saigontrade88/PrivayAttack_AT_FL
A privacy attack that exploits Adversarial Training models to compromise the privacy of Federated Learning systems.
saigontrade88/prml-1
Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop
saigontrade88/pytorch-tutorial
PyTorch Tutorial for Deep Learning Researchers
saigontrade88/pytorchTutorial
PyTorch Tutorials from my YouTube channel
saigontrade88/selfstudy-adversarial-robustness
saigontrade88/uvadlc_notebooks
Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2021/Spring 2022
saigontrade88/ViTRobust
Code corresponding to the paper: "On the Robustness of Vision Transformers": https://arxiv.org/abs/2104.02610