houzl3416 Goto Github PK

6036datagroup

预测招商银行股票涨跌幅度

ai-learn

人工智能学习路线图，整理近200个实战案例与项目，免费提供配套教材，零基础入门，就业实战！包括：Python，数学，机器学习，数据分析，深度学习，计算机视觉，自然语言处理，PyTorch tensorflow machine-learning,deep-learning data-analysis data-mining mathematics data-science artificial-intelligence python tensorflow tensorflow2 caffe keras pytorch algorithm numpy pandas matplotlib seaborn nlp cv等热门领域

autogen

Enable Next-Gen Large Language Model Applications. Join our Discord: https://discord.gg/pAbnFJrkgZ

awesome_chinese_medical_nlp

中文医学NLP公开资源整理：术语集/语料库/词向量/预训练模型/知识图谱/命名实体识别/QA/信息抽取/模型/论文/etc

bert-as-service

Mapping a variable-length sentence to a fixed-length vector using BERT model

bert-protein

chatgpt-on-wechat

concise

Concise: Keras extension for regulatory genomics

deepcircode

R package for deep learning of the back-splicing code for circular RNA formation

deeplncloc

A deep learning-based lncRNA subcellular localization predictor

developer

with 100k context windows on the way, it's now feasible for every dev to have their own smol developer

dimotif

Alignment-free Protein Motif Miner

dmctop

DMCTOP: Topology Prediction of Alpha-Helical Transmembrane Protein Based on Deep Multi-Scale Convolutional Neural Network

eden_examples

IPython notebooks describing the use of the EDeN framework

edlmppi

generative-ai-for-beginners

12 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/

gpts

leaked prompts of GPTs

houzl3416.github.io

icircrbp-dhn

ideepsubmito

javaguide

「Java学习+面试指南」一份涵盖大部分 Java 程序员所需要掌握的核心知识。准备 Java 面试，首选 JavaGuide！

leetcode

C++

leetcode-1

😏 LeetCode solutions in any programming language | 多种编程语言实现 LeetCode、《剑指 Offer（第 2 版）》、《程序员面试金典（第 6 版）》题解

lstm-

LSTM长短期记忆模型预测股票涨跌

mallproject

movielens-recommender

A pure Python implement of Collaborative Filtering based on MovieLens' dataset.

qqq

quivr

Dump all your files and thoughts into your GenerativeAI Second Brain and chat with it

recommanded_system

This is a source code of a new algorithm by myself. The algorithm is a collaborative filtering recommendation algorithm based on ALS that combines the "time forgetting curve" and "item type characteristics". The result showed that the accuracy of this algorithm is greatly improved. The experimental data are 100,000 records of movielens users' movie-watching feedback.本算法是结合“时间遗忘曲线”和“物品类型特征”的基于ALS的协同过滤推荐算法，算法基于前人的基础上进行了改革，实现算法优化. 该算法相较于原始的ALS推荐算法准确率有大幅度的提升。实验数据为movielens曾给出的用户观影反馈的十万条记录。

recommendation-system

《推荐系统实践》学习笔记