PyTorch

Practical Computer Vision Projects and Applications Practical computer vision helps you turn ideas into usable tools. This guide shares approachable projects and the steps to make them work. You’ll learn how to choose data, pick a model, and measure what matters. The focus is on small, repeatable experiments you can reuse later. Starter projects you can complete in a weekend: Face or person detection in photos with a pre-trained model. This teaches loading models, running inference, and drawing results. People counting in video using simple detectors or background subtraction. You’ll process frames, apply a detector, and count distinct subjects. Road scene segmentation with a lightweight model to label sky, road, and vehicles. It shows data handling, visualization, and simple evaluation. For each project, set a clear objective, keep privacy in mind, and write down what you learn. Validation should be straightforward and repeatable so you can build on your results. ...

GPU-Accelerated Computing for Data Science GPU-accelerated computing has become a cornerstone for modern data science. Today’s GPUs offer thousands of cores and wide memory bandwidth, letting you run large matrix operations, train models faster, and explore data with interactive speed. This shift changes workflows from long, fixed runs to quick, iterative experiments. With thoughtful setup, a single GPU can unlock performance that previously required a cluster. Data science workloads shine on GPUs in a few areas: deep learning, large-scale linear algebra, and data preprocessing. Training neural networks benefits from parallel tensor operations; simulations and Monte Carlo methods gain from parallel sampling; data transformations like filtering and normalization can be accelerated with GPU libraries. The key is to keep data on the GPU as much as possible to minimize transfers. ...