Creating anime characters using DCGANs and PyTorch

Mass production of millions of unique anime characters is nearly impossible for even the most skilled painter, but it becomes feasible with the use of machine learning methods. In this guided project, you will have the opportunity to build machine learning models and generate anime characters on your own. Furthermore, you will explore the Deep Convolutional Generative Adversarial Networks (DCGANs) method, which is specifically designed for large-scale anime production.

Object detection with Faster R-CNN and PyTorch

Faster R-CNN is a method for object detection that uses region proposal. In this project, you will use Faster R-CNN pre-trained on the COCO dataset. You will learn how to detect several objects by name and to use the likelihood of the object prediction being correct.

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!

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.

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.

Vision Transformers for Image Classification Hands-on

Up your game in Image classification by using Vision Transformers to achieve remarkable performance, surpassing CNN-based methods, and delivering state-of-the-art results on large image datasets.

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.