Popular GitHub Repositories for Tricks

a pytorch lib with state-of-the-art architectures, pretrained models and real-time updated results

key Deep Learning engineering tricks in recsys

An open collection of implementation tips, tricks and resources for training large language models

A collection of online continual learning paper implementations and tricks for computer vision in PyTorch, including our ASER(AAAI-21), SCR(CVPR21-W) and an online continual learning survey (Neurocomputing).

Amazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks

PyTorch implementations of recent Computer Vision tricks (ReXNet, RepVGG, Unet3p, YOLOv4, CIoU loss, AdaBelief, PolyLoss, MobileOne). Other additions: AdEMAMix

Bag of Tricks for Graph Neural Networks.

Tensorflow2.3的文本分类项目，支持各种分类模型，支持相关tricks。

A collection of tricks and tools to speed up transformer models

使用torch整合两种经典的指针NER抽取范式，分别是SpanBert和苏神的GlobalPointer，简单加了些tricks，配置后一键运行

[TPAMI 2022] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen*, Kaixiong Zhou*, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang

It contains interview preparation notes provided by iNeuron, important links, MLOps resources

Wrapped k-diffuison samplers with tricks to improve the generated image quality (maybe?), extension script for AUTOMATIC1111/stable-diffusion-webui

Ultimate collection of Claude Code tips, tricks, hacks, and workflows that you can use to master Claude Code in minutes

[ICLR 2022] Code for Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation (GLNN)

Experimental playground for benchmarking language model (LM) architectures, layers, and tricks on smaller datasets. Designed for flexible experimentation and exploration.

Automate swing trading using deep reinforcement learning. The deep deterministic policy gradient-based neural network model trains to choose an action to sell, buy, or hold the stocks to maximize the gain in asset value. The paper also acknowledges the need for a system that predicts the trend in stock value to work along with the reinforcement learning algorithm. We implement a sentiment analysis model using a recurrent convolutional neural network to predict the stock trend from the financial news. The objective of this paper is not to build a better trading bot, but to prove that reinforcement learning is capable of learning the tricks of stock trading.

The official repository for our paper "The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers". We significantly improve the systematic generalization of transformer models on a variety of datasets using simple tricks and careful considerations.

Bag of Tricks for Image Classification with Convolutional Neural Networks in Keras

This is a demo project showing how to use Face API in Cognitive Services with OpenCV