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.

Predict stock prices with LSTM in PyTorch

Learn to predict time series data with Long Short-Term Memory (LSTM) in PyTorch. Create a deep learning model that can predict a stock's value using daily Open, High, Low, and Close values and practice visualizing results and evaluating your model. Build foundational skills in machine learning while exploring the LSTM architecture. Develop practical knowledge with this beginner-friendly tutorial and apply it to real-world datasets using PyTorch.

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.

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.

Medical image segmentation with PyTorch and U-Net

Explore biomedical image segmentation in computer vision with PyTorch and U-Net, an architecture also used in Stable Diffusion. Learn to construct a U-Net model using PyTorch's convolutional, max-pooling, and upsampling layers. Through hands-on steps, train your model to automate image segmentation, showcasing the power of deep learning in medical imaging. Perfect for biomedical engineering or data science professionals aiming to enhance diagnostic accuracy.

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.

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.