Open Source Deep Learning Projects
Deep learning uses neural networks with many layers to learn patterns from data. It supports tasks that are difficult to capture with hand-written rules, including image recognition, language processing, forecasting, and speech recognition or synthesis. These methods can help automate analysis and generate content, but they often require substantial data and computing resources, and their results may be difficult to interpret.
2 repositories · updated July 12, 2026
NVIDIA NeMo Speech: Scalable Generative AI for Speech Models
NVIDIA NeMo Speech is a powerful, scalable generative AI framework designed for researchers and developers focused on Large Language Models, Multimodal, and Speech AI. It provides tools for Automatic Speech Recognition (ASR) and Text-to-Speech (TTS), enabling efficient creation, customization, and deployment of new AI models using existing code and pre-trained checkpoints. This framework supports a wide range of applications, from real-time streaming ASR to high-quality multilingual TTS.
PETSA: Parameter-Efficient Test-Time Adaptation for Time Series Forecasting
PETSA offers a parameter-efficient solution for Test-Time Adaptation (TTA) in time series forecasting, addressing the performance degradation caused by non-stationary data. It adapts pre-trained models during inference by updating small calibration modules, reducing memory and compute costs. This method, which includes low-rank adapters, dynamic gating, and a specialized loss, improves forecasting accuracy across diverse backbones and datasets.