Code and documentation to train Stanford's Alpaca models, and generate the data.
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Ambrosia helps you clean up your LLM datasets using other LLMs.
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler.
Machine Learning and Data Mining for Astronomy.
Automated machine learning for image, text, tabular, time-series, and multi-modal data.
Curated list of Turkish AI models, datasets, papers.
CLIP Interrogator supports machine learning models, deployment, inspection, datasets, or AI development workflows.
A curated list of language modeling researches for code and related datasets.
Track your datasets, code changes, experimentation history, and models.
A comparative framework for multimodal recommender systems with a focus on models leveraging auxiliary data.
Curate and annotate vision, audio, and LLM datasets, track experiments, and manage models on a single platform.
A data orchestrator for machine learning, analytics, and ETL.
Very simple implementation of neural networks for dummies in python without using any libraries, with detailed comments.
Enjoy unlimited API calls with Serverless AI Workers/LLMs for just $25 per month. No rate or concurrency limits.
Machine learning development environment for data science and AI/ML engineering teams.
Visualizations for understanding and analyzing machine learning datasets.
FutureTools supports machine learning models, deployment, inspection, datasets, or AI development workflows.
Pandas AI is a Python library that integrates LLMs capabilities into Pandas, making dataframes conversational.
Hugging Face supports machine learning models, deployment, inspection, datasets, or AI development workflows.
Illusion Diffusion supports machine learning models, deployment, inspection, datasets, or AI development workflows.
IUMB supports machine learning models, deployment, inspection, datasets, or AI development workflows.
Hong Jing is a data scientist & Alibaba PhD student, write about Machine Learning & Neuroscience.
Kokoro-82M supports machine learning models, deployment, inspection, datasets, or AI development workflows.
Introduction to machine learning covering basic theory, algorithms and applications.