Library to build personalized AI powered by what you've seen, said, or heard. Works with Ollama. Alternative to Rewind.ai. Open. Secure. You own your data. Rust.
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Updated
Sep 21, 2024 - Rust
Machine learning is the practice of teaching a computer to learn. The concept uses pattern recognition, as well as other forms of predictive algorithms, to make judgments on incoming data. This field is closely related to artificial intelligence and computational statistics.
Library to build personalized AI powered by what you've seen, said, or heard. Works with Ollama. Alternative to Rewind.ai. Open. Secure. You own your data. Rust.
🤘 TT-NN operator library, and TT-Metalium low level kernel programming model.
An Open Source Machine Learning Framework for Everyone
UAV Object Tracking using FMCW-Radar and YoloV8
Competitive Programming, System Design, Kaggle, LeetCode, AnalyticsVidhya, HackerRank, Project Euler, Interview Query
Machine learning library, Distributed training, Deep learning, Reinforcement learning, Models, TensorFlow, PyTorch
Data Analysis and Data Science courses projects
This repository demonstrates browser based implementation of DepthAnything and DepthAnythingV2 models. It is powered by Onnx and does not require any web servers.
🍁 Sycamore is an LLM-powered search and analytics platform for unstructured data.
Visualizer for neural network, deep learning and machine learning models
tickr-agent is an enterprise-ready, scalable Python library for building swarms of financial agents that conduct comprehensive stock analysis and produce insights.
A compute framework for building Search, RAG, Recommendations and Analytics over complex (structured+unstructured) data, with ultra-modal vector embeddings.
🤖 Apps that utilize the astounding power of ChatGPT or enhance its UX
NodeJS Bindings for Whisper - the CPU version of OpenAI's Whisper, as initially crafted in C++ by ggerganov.
A public dataset of over 4 million passwords, with assigned strength levels.
This repository uses machine learning to map pipeline anomalies, predict future depths, and fill missing data to improve pipeline integrity management.
data & learning flywheel for LLM systems