Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation.
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Very simple implementation of neural networks for dummies in python without using any libraries, with detailed comments.
Data Science at Home is a podcast about machine learning, artificial intelligence, and algorithms.
Data Skeptic is a podcast hosted by Kyle Polich, featuring interviews with experts on data science, machine learning, AI, and statistics.
Enjoy unlimited API calls with Serverless AI Workers/LLMs for just $25 per month. No rate or concurrency limits.
Machine Learning framework for rapid development of Machine Learning and Statistical applications.
Upload a photo of any room, kitchen, exterior, or garden and get a photorealistic AI redesign in seconds. No design experience needed.
A python library for accurate and scalable fuzzy matching, record deduplication and entity-resolution.
A fast Clojure Tensor & Deep Learning library.
Open-source package for validating ML models & data, with various checks and suites.
Scalable deep learning for industry with parallel GPUs.
Conversational AI library with many pre-trained Russian NLP models.
Org profile for DeepSeek on Hugging Face, the AI community building the future.
Cheaper V4 model, supports thinking and non-thinking modes.
Deep learning optimization library that makes distributed training easy, efficient, and effective.
An open source machine learning framework in Rust Δ.
Track real-time GPU and LLM pricing across all cloud and inference providers.
AI-generated summaries of provided legal docs.
Train and run a computer vision model with 5-10 lines of code.
Scalable deep learning training platform with integrated hyperparameter tuning support; includes Hyperband, PBT, and other search methods.
A Python library to remove unwanted pseudo-text from images generated by your favorite generative AI models (Stable Diffusion, Midjourney, DALL·E).
Authors: Cal.com core team,, Ted Spare.
Deep learning in Rust, with shape checked tensors and neural networks.
. Practical AI engineering and generative AI explained simply: RAG, agents, and LLM application patterns for builders.