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🦜🔗 Build context-aware reasoning applications
A latent text-to-image diffusion model
Learn how to design, develop, deploy and iterate on production-grade ML applications.
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
Python programs, usually short, of considerable difficulty, to perfect particular skills.
Data and code behind the articles and graphics at FiveThirtyEight
Welcome to the Llama Cookbook! This is your go to guide for Building with Llama: Getting started with Inference, Fine-Tuning, RAG. We also show you how to solve end to end problems using Llama mode…
llama3 implementation one matrix multiplication at a time
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
High-Resolution Image Synthesis with Latent Diffusion Models
Foundational Models for State-of-the-Art Speech and Text Translation
Official inference library for Mistral models
🤖 💬 Deep learning for Text to Speech (Discussion forum: https://discourse.mozilla.org/c/tts)
A series of large language models trained from scratch by developers @01-ai
Visualizations for machine learning datasets
CoreNet: A library for training deep neural networks
Flax is a neural network library for JAX that is designed for flexibility.
The image prompt adapter is designed to enable a pretrained text-to-image diffusion model to generate images with image prompt.
Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
Think DSP: Digital Signal Processing in Python, by Allen B. Downey.
A computer science textbook
line drawing colorization using chainer
Bringing stable diffusion models to web browsers. Everything runs inside the browser with no server support.
A Haskell kernel for the Jupyter project.
Deep Reinforcement Learning: Zero to Hero!
Koç University deep learning framework.
Measures the latency between CPU cores