liborutgers12

llama.cpp

LLM inference in C/C++

Firefly

Firefly: 大模型训练工具，支持训练Qwen2、Yi1.5、Phi-3、Llama3、Gemma、MiniCPM、Yi、Deepseek、Orion、Xverse、Mixtral-8x7B、Zephyr、Mistral、Baichuan2、Llma2、Llama、Qwen、Baichuan、ChatGLM2、InternLM、Ziya2、Vicuna、Bloom等大模型

Llama-Chinese

Llama中文社区，Llama3在线体验和微调模型已开放，实时汇总最新Llama3学习资料，已将所有代码更新适配Llama3，构建最好的中文Llama大模型，完全开源可商用

Baichuan-13B

A 13B large language model developed by Baichuan Intelligent Technology

Baichuan-7B

A large-scale 7B pretraining language model developed by BaiChuan-Inc.

LLaMA-Factory

A WebUI for Efficient Fine-Tuning of 100+ LLMs (ACL 2024)

Kalman-and-Bayesian-Filters-in-Python

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

pyAudioAnalysis

Python Audio Analysis Library: Feature Extraction, Classification, Segmentation and Applications

speech_recognition

Speech recognition module for Python, supporting several engines and APIs, online and offline.

doamusic

Python/C implementation of MUltiple SIgnal Classification as described by Schmidt.

friture

Real-time audio visualizations (spectrum, spectrogram, etc.)

rnn-speech-denoising

Recurrent neural network training for noise reduction in robust automatic speech recognition

DRNN4ASS

Deep Recurrent Neural Network for Audio Source Separation

nnrccar

nnrccar

applied-math-for-ML

data-analysis-for-ml

LeetCode-Sol-Res

Clean, Understandable Solutions and Resources for LeetCode Online Judge Algorithm Problems.

DeepLearnToolbox

Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started.

toPPT

toPPT is a powerful tool for generating PowerPoint presentations programmatically defined in Matlab. It will use different scripts for performing exports of figures, tables and texts. For this purpose it will use scripts written by Jens Richter and in addition scripts written by others (see Acknowledgment) . All code was tested with Matlab 2012-2015 and PowerPoint 2010 and 2013

tech-interview-handbook

💯 Curated coding interview preparation materials for busy software engineers