Linking pages
- GitHub - mikeroyal/Neuromorphic-Computing-Guide: Learn about the Neumorphic engineering process of creating large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures. https://github.com/mikeroyal/Neuromorphic-Computing-Guide 7 comments
- GitHub - higgsfield-ai/higgsfield: Fault-tolerant, highly scalable cluster management, and a machine learning framework designed for training models with billions to trillions of parameters https://github.com/higgsfield-ai/higgsfield 6 comments
- GitHub - pennpolygons/cv-boilerplate: Open-source boilerplate for computer vision research https://github.com/pennpolygons/cv-boilerplate 5 comments
- GitHub - noah-hein/mazeGPT: AI model for making mazes that extends OpenAIs GPT2 model https://github.com/noah-hein/mazeGPT 5 comments
- GitHub - mikeroyal/Machine-Learning-Guide: Machine learning Guide. Learn all about Machine Learning Tools, Libraries, Frameworks, and Training Models. https://github.com/mikeroyal/Machine-Learning-Guide 2 comments
- GitHub - nicolas-chaulet/torch-points3d: Pytorch framework for doing deep learning on point clouds. https://github.com/nicolas-chaulet/torch-points3d 1 comment
- Welcome to hydra-zen’s documentation! — hydra-zen documentation https://mit-ll-responsible-ai.github.io/hydra-zen/ 1 comment
- GitHub - facebookresearch/denoiser: Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)We provide a PyTorch implementation of the paper Real Time Speech Enhancement in the Waveform Domain. In which, we present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is optimized on both time and frequency domains, using multiple loss functions. Empirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb. Additionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities. https://github.com/facebookresearch/denoiser 1 comment
- Building a Machine Learning Platform [Definitive Guide] https://neptune.ai/blog/ml-platform-guide 1 comment
- Real-World MLOps Examples: Model Development in Hypefactors - neptune.ai https://neptune.ai/blog/mlops-examples-model-development-in-hypefactors 0 comments
- GitHub - facebookresearch/rlmeta: RLMeta is a light-weight flexible framework for Distributed Reinforcement Learning Research. https://github.com/facebookresearch/rlmeta 0 comments
- GitHub - facebookresearch/hydra: Hydra is a framework for elegantly configuring complex applications https://github.com/facebookresearch/hydra 0 comments
- GitHub - alshedivat/meta-blocks: A modular toolbox for meta-learning research with a focus on speed and reproducibility. https://github.com/alshedivat/meta-blocks 0 comments
- GitHub - facebookresearch/svoice: We provide a PyTorch implementation of the paper Voice Separation with an Unknown Number of Multiple Speakers In which, we present a new method for separating a mixed audio sequence, in which multiple voices speak simultaneously. The new method employs gated neural networks that are trained to separate the voices at multiple processing steps, while maintaining the speaker in each output channel fixed. A different model is trained for every number of possible speakers, and the model with the largest number of speakers is employed to select the actual number of speakers in a given sample. Our method greatly outperforms the current state of the art, which, as we show, is not competitive for more than two speakers. https://github.com/facebookresearch/svoice 0 comments
- GitHub - higgsfield/higgsfield: Fault-tolerant, highly scalable cluster management, and a machine learning framework designed for training models with billions to trillions of parameters https://github.com/higgsfield/higgsfield 0 comments
- GitHub - SalesforceAIResearch/uni2ts: Unified Training of Universal Time Series Forecasting Transformers https://github.com/SalesforceAIResearch/uni2ts 0 comments