Mobile app for medical solutions: Skin Cancer - store, analise, predict, remind for update. Blood Work - analise, question with LLM, insight, reminder for update
-
Updated
Jun 11, 2024 - JavaScript
Deep neural networks (DNNs) are a class of artificial neural networks (ANNs) that are deep in the sense that they have many layers of hidden units between the input and output layers. Deep neural networks are a type of deep learning, which is a type of machine learning. Deep neural networks are used in a variety of applications, including speech recognition, computer vision, and natural language processing. Deep neural networks are used in a variety of applications, including speech recognition, computer vision, and natural language processing.
Mobile app for medical solutions: Skin Cancer - store, analise, predict, remind for update. Blood Work - analise, question with LLM, insight, reminder for update
Minerva project includes the minerva package that aids in the fitting and testing of neural network models. Includes pre and post-processing of land cover data. Designed for use with torchgeo datasets.
An Open Source Machine Learning Framework for Everyone
A Deep Learning Classification Framework with Spectral and Spatial Feature Fusion Layers for Hyperspectral and Lidar Sensor Data
Automated discovery and classification of websites content through unsupervised learning approach
oneAPI Deep Neural Network Library (oneDNN)
AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.
Fast inference engine for Transformer models
This are the Machine Learning notes by leading AI website named Deeplearning.AI. This notes will help you to be a machine learner from beginner to advanced level. Welcome Everyone!!
Examples and tutorials on using SOTA computer vision models and techniques. Learn everything from old-school ResNet, through YOLO and object-detection transformers like DETR, to the latest models like Grounding DINO and SAM.
Model Compression Toolkit (MCT) is an open source project for neural network model optimization under efficient, constrained hardware. This project provides researchers, developers, and engineers advanced quantization and compression tools for deploying state-of-the-art neural networks.
Open standard for machine learning interoperability
Godpeny Github Page :)
functions to estimate the Conditional Average Treatment Effects (CATE) and Population Average Treatment Effects on the Treated (PATT)
[ACIIDS 2024] A Deep Learning Approach to Diabetes Diagnosis
Inpactor2: LTR retrotransposon detector and classificator using Deep Learning
This repository contains implementation of how we can build AI Agents from scratch and assign specific tasks to them.
⏰ AI conference deadline countdowns
Deep Learning Courses
JAX-based neural network library