Count the MACs / FLOPs of your PyTorch model.

更新2008年版本的《上海交通大学生存手册》gitbook发布于https://survivesjtu.gitbook.io/survivesjtumanual/

The calflops is designed to calculate FLOPs、MACs and Parameters in all various neural networks, such as Linear、 CNN、 RNN、 GCN、Transformer(Bert、LlaMA etc Large Language Model)

GNUPlot Examples

A Rust library for drawing plots, powered by Gnuplot.

Graphic notes on Gilbert Strang's "Linear Algebra for Everyone"

Transformer Explained Visually: Learn How LLM Transformer Models Work with Interactive Visualization

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-…

assistant tools for attention visualization in deep learning

[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.

抖音、快手、火山、皮皮虾，视频去水印程序

哔哩下载姬downkyi，哔哩哔哩网站视频下载工具，支持批量下载，支持8K、HDR、杜比视界，提供工具箱（音视频提取、去水印等）。

李宏毅2021/2022/2023春季机器学习课程课件及作业

A cross platform (Linux and Windows) user mode SDK to read data from your Azure Kinect device.

The BART Project: Benchmarking Algorithms for (data) Repairing and Translation

The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning

Hyperspectral and soil-moisture data from a field campaign based on a soil sample. Karlsruhe (Germany), 2017.

Topic Modelling for Humans

Source code of the paper: SAGED: Few-Shot Meta Learning for Tabular Data Error Detection

Bunch of examples of a "Simple but tough to beat baseline for sentence embeddings" in classification tasks

sentence embedding by Smooth Inverse Frequency weighting scheme

Character-based word embeddings model based on RNN for handling real world texts

总结梳理自然语言处理工程师(NLP)需要积累的各方面知识，包括面试题，各种基础知识，工程能力等等，提升核心竞争力