Repository Index

WFGY is heading toward WFGY 5.0 Polaris Protocol, a major open-source release for AI reasoning, RAG, agents, and real-world workflows. Includes Problem Map, Global Debug Card, WFGY 4.0, and the CFV Easter Egg.

• rag
• ai-agents
• alignment
• debugging

Notebooks for financial economics. Keywords: Jupyter notebook pandas Federal Reserve FRED Ferbus GDP CPI PCE inflation unemployment wage income debt Case-Shiller housing asset portfolio equities SPX bonds TIPS rates currency FX euro EUR USD JPY yen XAU gold Brent WTI oil Holt-Winters time-series forecasting statistics econometrics

• jupyter-notebook
• pandas
• federal-reserve
• gdp

TorchXRayVision: A library of chest X-ray datasets and models. Classifiers, segmentation, and autoencoders.

• medical-imaging
• medical-ai
• deep-learning
• machine-learning

CatBoost tutorials repository

• catboost
• tutorials
• ipython-notebook
• ipython

TeachOpenCADD: a teaching platform for computer-aided drug design (CADD) using open source packages and data

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

• seq2seq
• tensorflow
• tensorflow-tutorials
• python

"Neural Turing Machine" in Tensorflow

• tensorflow
• neural-turing-machines

TensorFlow Implementation of "Show, Attend and Tell"

• tensorflow
• image-captioning
• show-attend-and-tell
• attention-mechanism

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

Use evolutionary algorithms instead of gridsearch in scikit-learn

A light-weight deep reinforcement learning framework for portfolio management. This project explores the possibility of applying deep reinforcement learning algorithms to stock trading in a highly modular and scalable framework.

Move fast from data science prototype to pipeline. Capture, analyze, and transform messy notebooks into data pipelines with just two lines of code.

food image to recipe with deep convolutional neural networks.

• convolutional-neural-networks
• keras
• chef
• deep-learning

Cleora AI is a general-purpose open-source model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data. Created by Synerise.com team.

• ai
• graphs
• synerise
• embeddings

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

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

• clinicalbert
• hospital-readmission
• prediction

Tutorials, datasets, and other material associated with textbook "A First Course in Network Science" by Menczer, Fortunato & Davis

• social-network
• indiana-university
• network-science
• textbook

Solve captcha without manually labeling a training set

• keras
• gan
• neural-network
• generative-adversarial-network

Reference mapping for single-cell genomics

• single-cell
• deep-learning
• rna-seq-analysis
• data-integration

The fast Continuous Wavelet Transform (fCWT) is a library for fast calculation of CWT.

• cwt
• matlab
• cpp
• optimization

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

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

Tensorflow implementation of "Learning Deep Features for Discriminative Localization"

An OCaml kernel for Jupyter (IPython) notebook

• ocaml
• functional-programming
• jupyter-kernels
• machine-learning

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.