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anomaly-detection-resources

Anomaly detection related books, papers, videos, and toolboxes

anomalydetection

batteryml

biblebuddy

BibleBuddy Q&A ChatGPT API with Context

contactsmanagement

creditanalysis

creditfrauddetection

data-driven-prediction-of-battery-cycle-life-before-capacity-degradation

Code for Nature energy manuscript

dataprep

Summarize and implement data preparation methods

deepchem

Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology

diabetespred

Use CGM data to predict the Glycemic Risk. This project mainly focuses on Time in Range and the risk correlation.

dialogpt

Large-scale pretraining for dialogue

diffusers

🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch

ecommerce

etsformer

PyTorch code for ETSformer: Exponential Smoothing Transformers for Time-series Forecasting

featureengineering

funlotto

Powerball Lottery Number Generation with Machine Learning

generateexam

icme-recommendation-system-summer-2023

kubernetestutorial

Tutorial on how to deploy app on Kubernetes and monitor the service with Prometheus and grafana

llama

Inference code for LLaMA models

llm-course

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

nlp-resources

Learning NLP with hands-on coding examples

phenaki-pytorch

Implementation of Phenaki Video, which uses Mask GIT to produce text guided videos of up to 2 minutes in length, in Pytorch

powerlock.github.io

probabilistic-programming-and-bayesian-methods-for-hackers

aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

pythondatasciencehandbook

Python Data Science Handbook: full text in Jupyter Notebooks

raman-analysis

Python-code to analyse Raman spectroscopy

screenshot-to-code

Drop in a screenshot and convert it to clean code (HTML/Tailwind/React/Vue)

sklearn_tutorial

Materials for my scikit-learn tutorial