xmk233 Goto Github PK

alphafm

Multi-thread implementation of Factorization Machines with FTRL for binary-class classification problem.

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, ... 🧠

awesome-algorithm-books

📚 awesome algorithm books I've collected 【不定期更新】 搜集整理的算法书籍（经典算法、ML/DL算法、面试算法、比赛算法等）

bear_grylls_survive

假托贝爷的野外生存而做的一个软件，当作复习一下PyQt5的基本操作

blog

Python机器学习算法技术博客，有原创干货！有code实践！

bookrack

code-of-learn-deep-learning-with-pytorch

This is code of book "Learn Deep Learning with PyTorch"

compiler-design

computer-organization-p1

creation

computer-organization-p2

computer-organization-p3

computer-organization-p4

computer-organization-p5

computer-organization-p5.2

computer-organization-p6

cs-notes

:books: 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计、Java、Python、C++

ctr_model_zoo

some ctr model, implemented by PyTorch, such as Factorization Machines, Field-aware Factorization Machines, DeepFM, xDeepFM, Deep Interest Network

deep-learning-for-image-processing

deep learning for image processing including classification and object-detection etc.

deep-learning-papers-reading-roadmap

Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!

deepctr

deepctr-pytorch

Here are the models listed in CTR. Example: FM、DeepFM、xDeepFM etc.

django_validate_code_demo

first

djangodemo_logreg

First edition

eecs3311-fall2018

eecs6432

Some code come from the EECS6432

eecs6444

Any data and code related to this course

elevator-distribution-als

a little smarter elevator distribution system

elevetor-distribution-concurrency

elevators distribution using the concurrency

emse2020_mlpackage

Replication package for EMSE 2020 ML package paper

external-attention-pytorch

🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐