wintaowang,Tao WANG,github

introneuralnetworks

Introducing neural networks to predict stock prices

irs

IRS: A Large Synthetic Indoor Robotics Stereo Dataset for Disparity and Surface Normal Estimation

LabelImg is now part of the Label Studio community. The popular image annotation tool created by Tzutalin is no longer actively being developed, but you can check out Label Studio, the open source data labeling tool for images, text, hypertext, audio, video and time-series data.

labelmetoolbox

MATLAB Toolbox for the LabelMe Image Database

llama_index

LlamaIndex is a data framework for your LLM applications

llm-and-genai-in-engineering

A Collection of Papers on the Use of LLM and GenAI in Engineering

llm-course

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

llm-finetuning

Guide for fine-tuning Llama/Mistral/CodeLlama models and more

llm-finetuning-large-language-models

LLM (Large Language Model) FineTuning

lstmdemo

LSTM demo

machine-learning-by-scikit-learn-algorithms-and-practices

scikit-learn机器学习常用算法原理及编程实战黄永昌编著

machine_learning

机器学习算法的实现和应用

make_a_neural_network

This is the code for the "Make a Neural Network" - Intro to Deep Learning #2 by Siraj Raval on Youtube

mapbi3-single-crystal-growth-simulation-by-mcmc-sampling

This simulation use the Monte Carlo Markov Chain (MCMC) sampling to simulate the growth of PbI2 in solution.

matgan

Generative deep learning model for inorganic materials

matlab-figure-templates

matrcwa

An electromagnetic simulation tool programmed in Matlab by using the method Rigorous Coupled-Wave analysis (RCWA)

mcml_simulatespectroscopy

simulate Diffuse Reflectance Spectroscopy(DRS) of human skin tissue via Monte Carlo for Multi-Layered media(MCML)

memetic_thin_film_py

Now in Python! This software designs thin-film optical structures to match target spectral properties using the memetic algorithm

miepython

Mie scattering of light off perfect spheres in python

ml

此仓库将介绍Deep Learning 所需要的基础知识以及NLP方面的模型原理到项目实操 : )

ml-assignments

about Regression, Classification, CNN, RNN, Explainable AI, Adversarial Attack, Network Compression, Seq2Seq, GAN, Transfer Learning, Meta Learning, Life-long Learning, Reforcement Learning.