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emailThe Annual Conference on Neural Information Processing Systems.
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A course on neural networks that starts all the way at the basics. The course is a series of YouTube videos where we code and train neural networks together. The Jupyter notebooks we build in the videos are then captured here inside the lectures directory. Every lecture also has a set of exercises included in the video description. (This may grow into something more respectable).
Fly through your shell history. Great Scott!.
McFly replaces your default ctrl-r shell history search with an intelligent search engine that takes into account your working directory and the context of recently executed commands. McFly's suggestions are prioritized in real time with a small neural network.
Triton is a language and compiler for parallel programming. It aims to provide a Python-based programming environment for productively writing custom DNN compute kernels capable of running at maximal throughput on modern GPU hardware.
A hyperparameter optimization framework. Optimize Your Optimization.
An open source hyperparameter optimization framework to automate hyperparameter search
fastai is a deep learning library which provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains, and provides researchers with low-level components that can be mixed and matched to build new approaches. It aims to do both things without substantial compromises in ease of use, flexibility, or performance. This is possible thanks to a carefully layered architecture, which expresses common underlying patterns of many deep learning and data processing techniques in terms of decoupled abstractions. These abstractions can be expressed concisely and clearly by leveraging the dynamism of the underlying Python language and the flexibility of the PyTorch library. fastai includes:
Qdrant (read: quadrant ) is a vector similarity search engine and vector database. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support. It makes it useful for all sorts of neural-network or semantic-based matching, faceted search, and other applications.
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ONNX is an open format built to represent machine learning models. ONNX defines a common set of operators - the building blocks of machine learning and deep learning models - and a common file format to enable AI developers to use models with a variety of frameworks, tools, runtimes, and compilers.
GUI for a Vocal Remover that uses Deep Neural Networks.
This application uses state-of-the-art source separation models to remove vocals from audio files. UVR's core developers trained all of the models provided in this package (except for the Demucs v3 4-stem models).
GPU accelerated Neural networks in JavaScript for Browsers and Node.js
Neural Network Libraries NNabla is a deep learning framework that is intended to be used for research, development and production. We aim it running everywhere like desktop PCs, HPC clusters, embedded devices and production servers.
A CNN model visualizer
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. You can find the source on GitHub or you can read more about what Darknet can do right here:
OpenFace is a Python and Torch implementation of face recognition with deep neural networks and is based on the CVPR 2015 paper FaceNet: A Unified Embedding for Face Recognition and Clustering by Florian Schroff, Dmitry Kalenichenko, and James Philbin at Google. Torch allows the network to be executed on a CPU or with CUDA.