Popular GitHub Repositories for Tesla

国内首个占据栅格网络全栈课程《从BEV到Occupancy Network，算法原理与工程实践》，包含端侧部署。Surrounding Semantic Occupancy Perception Course for Autonomous Driving (docs, ppt and source code) 在线课程主页：http://111.229.117.200:8100/ (作者独立搭建)

Simulation of path planning for self-driving vehicles in Unity. This is also an implementation of the Hybrid A* pathfinding algorithm which is useful if you are interested in pathfinding for vehicles.

This repository hosts a stock market prediction model for Tesla and Apple using Liquid Neural Networks. It showcases data-driven forecasting techniques, feature engineering, and machine learning to enhance the accuracy of financial predictions.

Deep Learning Applications (Darknet - YOLOv3, YOLOv4 | DeOldify - Image Colorization, Video Colorization | Face-Recognition) with Google Colaboratory - on the free Tesla K80/Tesla T4/Tesla P100 GPU - using Keras, Tensorflow and PyTorch.

Tesla Autopilot Racing Simuator

Tesla Car Range, Base Price and Precise Price Prediction - Machine Learning

YOLO-Based Real-Time Object Detection for Autonomous Driving in CARLA

Prediction of Stock price using Recurrent Neural Network (RNN) models. Contains GRU, LSTM, Bidirection LSTM & LSTM combinations with GRU units. The models were deveoped using the keras module from Tensorlfow.

A clean and simple implementation of Retrieval Augmented Generation (RAG) to enhanced LLaMA chat model to answer questions from a private knowledge base. We use Tesla user manuals to build the knowledge base, and use open-source embedding and Cross-Encoders reranking models from Sentence Transformers in this project.

This a project which predicts the stock price of Tesla for a given time period & based upon the previous 10 years of historical data. Here, numerical and sentimental analysis is performed with the help of natural language toolkit (NLKT), Textblob, sklearn etc. By observing the previous trends of the market stock price and sentiments of the news about the market, this model predicted the future variation of the stock market price of TELSA. Basically, four models were trained with the same dataset like: Random Forest, AdaBoost, LGBM & xgboost out of these model LGBM model predicted with the least mean squared error value.

The stock price forecasting results for 5 leading technology companies: Meta (META), Tesla (TSLA), NVIDIA (NVDA), Microsoft (MSFT), and Apple (AAPL). This analysis uses three popular prediction models: LSTM (Long Short-Term Memory), ARIMA (AutoRegressive Integrated Moving Average), and FBProphet.

A Model Context Protocol Server for AI models to interface with the Tesla Fleet API.

This repository features notebooks and datasets for predicting Tesla (TSLA) stock prices using LSTM models. Explore historical data, forecast trends, and gain insights into TSLA's market movements.

SynapDrive-AI simulates seamless Brain-Computer Interface integration with modular AGI for real-time, safe control of advanced physical systems like Tesla vehicles, SpaceX rockets, and Hyperloop infrastructure. It pioneers memory-driven, vision-aware, and safety-guarded cognition to accelerate human-machine synergy.

This repository aims to build a Facebook Prophet Machine learning model that can forecast the price of Tesla 30 days in advance. The effectiveness of the model is also evaluated by looking at Tesla's prior performance using data visualization methods.

A curated list of resources, projects, interviews, documentaries, articles, and communities related to Elon Musk.