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Ran Tan's starred repositories

hello-algo

《Hello 算法》：动画图解、一键运行的数据结构与算法教程。支持 Python, Java, C++, C, C#, JS, Go, Swift, Rust, Ruby, Kotlin, TS, Dart 代码。简体版和繁体版同步更新，English version ongoing

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LLMSurvey

The official GitHub page for the survey paper "A Survey of Large Language Models".

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LLMBox

A comprehensive library for implementing LLMs, including a unified training pipeline and comprehensive model evaluation.

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MiniCPM-V

MiniCPM-V 2.6: A GPT-4V Level MLLM for Single Image, Multi Image and Video on Your Phone

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d2l-en

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

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awesome-recommend-system-pretraining-papers

Paper List for Recommend-system PreTrained Models

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annotated_deep_learning_paper_implementations

🧑‍🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

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leetcode

My leetcode solutions.

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Awesome-LLM

Awesome-LLM: a curated list of Large Language Model

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dalai

The simplest way to run LLaMA on your local machine

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unlocking-the-power-of-llms

使用 Prompts 和 Chains 让 ChatGPT 成为神奇的生产力工具！Unlocking the power of LLMs.

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tsai

Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai

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UniTS-Sensory-Time-Series-Classification

Source code for paper: UniTS: Short-Time Fourier Inspired Neural Networks for Sensory Time Series Classification

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Modern-CPP-Programming

Modern C++ Programming Course (C++03/11/14/17/20/23/26)

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StockSharp

Algorithmic trading and quantitative trading open source platform to develop trading robots (stock markets, forex, crypto, bitcoins, and options).

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stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

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