MIT Technology Review

Using unstructured data to fuel enterprise AI success

Organizations have a wealth of unstructured data that most AI models can’t yet read. Preparing and contextualizing this data ...
Deep Learning with Yacine on MSN

Visualizing high-dimensional data using PCA in Scikit-Learn

Simplify complex datasets using Principal Component Analysis (PCA) in Python. Great for dimensionality reduction and ...

Language shapes visual processing in both human brains and AI models, study finds

Neuroscientists have been trying to understand how the brain processes visual information for over a century. The development ...

Artificial Analysis overhauls its AI Intelligence Index, replacing popular benchmarks with 'real-world' tests

Artificial Analysis overhauls its AI Intelligence Index, replacing saturated benchmarks with real-world tests measuring ...
GitHub

18x Standardized Datasets for 2D and 3D Biomedical Image Classification

Update 2024-01-17: We are thrilled to release MedMNIST+ with larger sizes: 64x64, 128x128, and 224x224 for 2D, and 64x64x64 for 3D. As a complement to the previous 28-size MedMNIST, the large-size ...
Scientific Research Publishing

Cross-Dataset Transcriptomic Analysis Reveals Distinct Immune Regulatory Networks in Non-Tuberculous Mycobacterial Disease ()

1 Department of Critical Care Medicine, The Brain Hospital of Guangxi Zhuang Autonomous Region, Liuzhou, China. 2 Department of Respiratory Medicine, The Chest Hospital of Guangxi Zhuang Autonomous ...
IEEE

A Rigorous Comparative Analysis on the MNIST Dataset: Proposing an Elaborate Neural Network Model for Handwritten Digit Classification using Deep Learning Methodologies

Abstract: The present research examines the efficacy of various neural network methodologies for digit classification using the MNIST dataset, encompassing a fundamental Neural Network (Sequential API ...
GitHub

Pull requests: mugkul/Linear-Discriminant-Analysis-on-MNIST-dataset

Pull requests help you collaborate on code with other people. As pull requests are created, they’ll appear here in a searchable and filterable list. To get started, you should create a pull request.
IEEE

Differential Privacy in Federated Learning Using Noise Multipliers: An Analysis on MNIST Dataset

Abstract: Federated learning enables decentralized learning to run on multiple clients by training locally on each client’s data and sharing new instances only on a central server. However, if privacy ...