Vision Transformers for Image Classification Hands-on

Vision Transformers are advanced deep learning architectures that leverage self-attention mechanisms to selectively process crucial image components. This empowers them to achieve remarkable performance, surpassing CNN-based methods, and delivering state-of-the-art results on large image datasets.

Deploy a Computer Vision App in a Serverless Environment

Learn how to make your object detection application available to the world by deploying to a serverless environment. Focus on building your app instead of buying, installing or configuring servers.

Human Portrait Drawing with U-Squared Net and PyTorch

Have you ever played with a portrait drawing app where you can get an AI-generated portrait of yourself in seconds by uploading your photo? This guided project will demystify such an app by showing you its underlying building block, which is the state-of-the-art U-squared Network (U2-Net). Get ready for mass-producing AI-generated human portraits!

Cancer Image Detection With PyTorch (Part 3 iBest Workshop)

This project uses deep learning in PyTorch and computer vision techniques to develop an algorithm to identify metastatic cancer in small image patches obtained from larger digital pathology scans. The project's objectives are to set up the necessary environment, install and import required libraries, and perform data preparation for the model training. The project leverages pre-trained Convolutional Neural Networks (CNNs) and transfer learning to improve the model's performance.

Getting Started with Machine Learning with PyTorch

PyTorch is a leading open source framework for AI research and commercial production in machine learning. It is used to build, train, and optimize deep learning neural networks for applications such as image recognition, natural language processing, and speech recognition.