Repository Index

A library for debugging/inspecting machine learning classifiers and explaining their predictions

• scikit-learn
• machine-learning
• xgboost
• lightgbm

An unsupervised learning framework for depth and ego-motion estimation from monocular videos

• deep-learning
• depth-prediction
• visual-odometry
• self-supervised-learning

Natural Gradient Boosting for Probabilistic Prediction

• machine-learning
• gradient-boosting
• natural-gradients
• uncertainty-estimation

A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)

• drug-repurposing
• deep-learning
• drug-target-interactions
• toolkit

Signal forecasting with a Sequence-to-Sequence (seq2seq) Recurrent Neural Network (RNN) model in TensorFlow - Guillaume Chevalier

• seq2seq
• tensorflow
• tensorflow-tutorials
• python

This repository introduces PIXIU, an open-source resource featuring the first financial large language models (LLMs), instruction tuning data, and evaluation benchmarks to holistically assess financial LLMs. Our goal is to continually push forward the open-source development of financial artificial intelligence (AI).

• aifinance
• fintech
• llama
• machine-learning

🍇 GRAPE is a Rust/Python Graph Representation Learning library for Predictions and Evaluations

• graph
• high-performance
• machine-learning
• knowledge-graph

ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission (CHIL 2020 Workshop)

• clinicalbert
• hospital-readmission
• prediction

Genomic Pretrained Network - GPN, GPN-MSA, PhyloGPN, GPN-Star

• dna
• language-model
• variant-effect-prediction
• genomics

MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction (Bioinformatics)

This is the code for "Bitcoin Prediction" by Siraj Raval on Youtube

A collection of tasks to probe the effectiveness of protein sequence representations in modeling aspects of protein design

• protein
• protein-sequences
• protein-design
• representation-learning

PRnet is a flexible and scalable perturbation-conditioned generative model predicting transcriptional responses to unseen complex perturbations at bulk and single-cell levels.

Interpretable drug response prediction with graph neural networks over drugs, cell lines, and genes.

Comparing the performance of Stacked Ensemble Learning & machine learning algorithms like Random Forest, Decision Tree, Adaboost, Gradient Boost and XGBoost Classifier in Python for Stock Market Trend Prediction.