Viso.ai

MAY 1, 2024

Stanford University and panel researchers P. Deep learning and Convolutional Neural Networks (CNNs) have enabled speech understanding and computer vision on our phones, cars, and homes. Natural Language Processing (NLP) and knowledge representation and reasoning have empowered the machines to perform meaningful web searches.

Robotics 52

Robotics Convolutional Neural Networks Computer Vision Deep Learning 52

Google Research AI blog

MAY 15, 2023

Monk Skin Tone (MST) Scale See more at skintone.google. In comparison to an industry standard scale like the Fitzpatrick Skin-Type Scale designed for dermatological use, the MST offers a more inclusive representation across the range of skin tones and was designed for a broad range of applications, including computer vision.

ML 109

ML Computer Vision Software Engineer Responsible AI 109

PyImageSearch

NOVEMBER 13, 2023

Home Table of Contents Faster R-CNNs Object Detection and Deep Learning Measuring Object Detector Performance From Where Do the Ground-Truth Examples Come? One of the most popular deep learning-based object detection algorithms is the family of R-CNN algorithms, originally introduced by Girshick et al. Object detection is no different.

Convolutional Neural Networks 120

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 120

Sebastian Ruder

NOVEMBER 14, 2022

This post is partially based on a keynote I gave at the Deep Learning Indaba 2022. Models that allow interaction via natural language have become ubiquitious. Research models such as BERT and T5 have become much more accessible while the latest generation of language and multi-modal models are demonstrating increasingly powerful capabilities.

Natural Language Processing 52

Natural Language Processing NLP Computational Linguistics AI 52

PyImageSearch

OCTOBER 2, 2023

Using the renowned Fashion-MNIST dataset, we’ll guide you through understanding its nuances. Using the renowned Fashion-MNIST dataset, we’ll guide you through understanding its nuances. In our previous tutorial on autoencoders , we learned that they are not inherently generative. Let’s get started!

Computer Vision 120

Computer Vision Deep Learning Neural Network Auto-complete 120

PyImageSearch

JULY 17, 2023

This lesson is the 2nd of a 4-part series on Autoencoders : Introduction to Autoencoders Implementing a Convolutional Autoencoder with PyTorch (this tutorial) Lesson 3 Lesson 4 To learn to train convolutional autoencoders in PyTorch with post-training embedding analysis on the Fashion-MNIST dataset, just keep reading. And oh, the frustration!

Computer Vision 98

Computer Vision Deep Learning Python Machine Learning 98

PyImageSearch

OCTOBER 23, 2023

By the time we conclude, you’ll have a comprehensive understanding of how to implement, train, and experiment with VAEs using PyTorch. Learning on your employer’s administratively locked system? Ready to unravel the intricacies of training and experimenting with Variational Autoencoders on the CelebA dataset using PyTorch?

Computer Vision 67

Computer Vision Deep Learning Neural Network Explainability 67