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NaturalAntibody
- Poland, Cracow
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Repo for ML Models built from scratch such as Self-Attention, Linear +Logistic Regression, PCA, LDA. CNN, LSTM, Neural Networks using Numpy only
LLMs build upon Evol Insturct: WizardLM, WizardCoder, WizardMath
Curated tutorials and resources for Large Language Models, Text2SQL, Text2DSLγText2APIγText2Vis and more.
Problems can be a nuisance, but they can also be an opportunity for growth.π
An unnecessarily tiny implementation of GPT-2 in NumPy.
ποΈ + π¬ + π§ = π€ Curated list of top foundation and multimodal models! [Paper + Code + Examples + Tutorials]
Code and Documentation for the first place solution in 2023 Abdominal Trauma Detection Competition hosted by RSNA on Kaggle.
2nd Place Solution for the RSNA 2023 Abdominal Trauma Detection Kaggle Competition
An introduction to bioimage analysis - https://bioimagebook.github.io
The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
A Generative Adversarial Network (GAN) trained on the MNIST dataset, capable of creating fake but realistic looking MNIST digit images that appear to be drawn from the original dataset.
Hypothesis is a powerful, flexible, and easy to use library for property-based testing.
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
Images to inference with no labeling (use foundation models to train supervised models).
Statistical Rethinking course winter 2022
Machine Learning Resources, Practice and Research
This is reimplementation of "Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness" in Pytorch.
The most cited deep learning papers
An opinionated list of awesome Python frameworks, libraries, software and resources.
A curated list of awesome Machine Learning frameworks, libraries and software.
An ultra-high-performance protein-protein docking for heterogeneous supercomputers
Code and data used in The Great Repertoire Project
Experiments with supervised contrastive learning methods with different loss functions