Image restoration with neural networks but without learning.
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A curated list of applied machine learning and data science notebooks and libraries across different industries (by @firmai)
🔥 LeetCode for PyTorch — practice implementing softmax, attention, GPT-2 and more from scratch with instant auto-grading. Jupyter-based, self-hosted or try online.
MongoDB's Generative AI Showcase: an exhaustive collection of examples and sample applications covering Retrieval-Augmented Generation (RAG), AI agents, and industry-specific use cases.
An unsupervised learning framework for depth and ego-motion estimation from monocular videos
- deep-learning
- depth-prediction
- visual-odometry
- self-supervised-learning
WebGL accelerated JavaScript molecular graphics library
- molecular-graphics
- molecular-modeling
- computational-biology
- computational-chemistry
bert-loves-chemistry: a repository of HuggingFace models applied on chemical SMILES data for drug design, chemical modelling, etc.
- huggingface
- bert
- chemical-modelling
- chemical-smiles-data
Every day, millions of traders around the world are trying to make money by trading stocks. These days, physical traders are also being replaced by automated trading robots. Algorithmic trading market has experienced significant growth rate and large number of firms are using it. I have tried to build a Deep Q-learning reinforcement agent model to do automated stock trading.
Wrapper for RDKit's RunReactants to improve stereochemistry handling
Optimising chemical reactions using machine learning
- chemistry
- machine-learning
- optimization
- bayesian-optimization
UV-first benchmark for protein-ligand docking with reproducible annotation, pocket similarity, and HPC workflows.
- benchmark
- bioinformatics
- cheminformatics
- computational-chemistry