Popular GitHub Repositories for Trip Energy Consumption

A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.

A fast Rust based tool to serialize text-based files in a repository or directory for LLM consumption

A deep learning package for many-body potential energy representation and molecular dynamics

Tensors, for human consumption

"Deep Generative Modeling": Introductory Examples

(CVPR 2021 Oral) Open World Object Detection

List of Molecular and Material design using Generative AI and Deep Learning

Boltzmann Machines in TensorFlow with examples

Power Tools for AI Engineers With Deadlines

PyTorch Code for Energy-Based Transformers paper -- generalizable reasoning and scalable learning

Vector Hub - Library for easy discovery, and consumption of State-of-the-art models to turn data into vectors. (text2vec, image2vec, video2vec, graph2vec, bert, inception, etc)

[ECCV 2022] Compositional Generation using Diffusion Models

Training neural network potentials

The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

The official implementation for ICLR23 spotlight paper "DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion"

User can set up destination for any agent to navigate on Google Map and learn the best route for the agent based on its current condition and the traffic. Our result is 10% less energy consumption than the route provided by Google map

? EcoLogits tracks the energy consumption and environmental footprint of using generative AI models through APIs.

Explore the energy-efficient dataflow scheduling for neural networks.

OpenModelica Microgrid Gym (OMG): An OpenAI Gym Environment for Microgrids