jannymon Goto Github PK

anchor

Code for "High-Precision Model-Agnostic Explanations" paper

audioclassification-pytorch

The Pytorch implementation of sound classification supports EcapaTdnn, PANNS, TDNN, Res2Net, ResNetSE and other models, as well as a variety of preprocessing methods.

audioset_tagging_cnn

awesome-wechat-weapp

微信小程序开发资源汇总 :100:

blog

My Blog

caffe

Caffe: a fast open framework for deep learning.

captum

Model interpretability and understanding for PyTorch

curldemo

A simple demo to show how to use libcurl

deeplift

Public facing deeplift repo

essentia

C++ library for audio and music analysis, description and synthesis, including Python bindings

exploreopencvdnn

git_book

教材对应的源码

gpt4free

The official gpt4free repository | various collection of powerful language models

headfirst

HeadFirst Sample

jannymon.github.io

lhy_machine_learning

李宏毅2021/2022/2023春季机器学习课程课件及作业

libsamplerate

An audio Sample Rate Conversion library

lime

Lime: Explaining the predictions of any machine learning classifier

miniprogram-demo

微信小程序组件 / API / 云开发示例

mobilenet-ssd

Caffe implementation of Google MobileNet SSD detection network, with pretrained weights on VOC0712 and mAP=0.727.

models

Models and examples built with TensorFlow

moneyprinter

Automate Creation of YouTube Shorts using MoviePy.

opencv_extra

OpenCV extra data

opensmile

The Munich Open-Source Large-Scale Multimedia Feature Extractor

pytorch-grad-cam

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

shap

A game theoretic approach to explain the output of any machine learning model.

shapash

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

terminal

The new Windows Terminal, and the original Windows console host -- all in the same place!

tflite-micro

Infrastructure to enable deployment of ML models to low-power resource-constrained embedded targets (including microcontrollers and digital signal processors).

tricks-of-semi-superviseddeepleanring-pytorch

PseudoLabel 2013, VAT, PI model, Tempens, MeanTeacher, ICT, MixMatch, FixMatch