Data Science at Home is a podcast about machine learning, artificial intelligence, and algorithms.
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Data Skeptic is a podcast hosted by Kyle Polich, featuring interviews with experts on data science, machine learning, AI, and statistics.
Machine Learning framework for rapid development of Machine Learning and Statistical applications.
Deconstructing the AI Myth: Fallacies and Harms of Algorithmification provides machine-learning models, research, training resources, or evaluation…
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 text-to-image model with a high degree of photorealism and language understanding by Stability.AI.
Open-source package for validating ML models & data, with various checks and suites.
DeepCourse catalogs AI services and resources for browsing by feature or use case.
Translate texts & full document files instantly. Accurate translations for individuals and Teams. Millions translate with DeepL every day.
DeepLearning.ai provides AI-assisted learning, courses, tutorials, or study support.
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.
Deep learning optimization library that makes distributed training easy, efficient, and effective.
An open source machine learning framework in Rust Δ.
DesignArena catalogs AI services and resources for browsing by feature or use case.
Designarena uses AI to create, edit, transform, or animate video content.
AI-generated summaries of provided legal docs.
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.