11 docs tagged with "computer-vision"

How CNNs and deep neural networks power modern discovery engines like Netflix, YouTube, and Pinterest.

Understanding the competitive framework between Generators and Discriminators to create realistic synthetic data.

How to train neural networks to categorize images into predefined classes using CNNs.

Going beyond bounding boxes: How to classify every single pixel in an image.

Intermediate-level ML projects focusing on NLP, Computer Vision, and Time-Series forecasting.

How padding prevents data loss at the edges and controls the output size of convolutional layers.

Understanding Max Pooling, Average Pooling, and how they provide spatial invariance.

How AI learns by predicting missing parts of its own input, powering Large Language Models and Computer Vision.

Understanding how the step size of a filter influences spatial dimensions and computational efficiency.

Understanding kernels, filters, and how feature maps are created in Convolutional Neural Networks.

Handling hierarchical data in XML: parsing techniques, its role in Computer Vision annotations, and converting XML to ML-ready formats.