Deep-learning

Exploring Model Soup techniques that improve neural network performance by averaging weights from multiple fine-tuned models, offering better accuracy without increased inference cost.

Comprehensive guide to global image descriptors, exploring traditional methods like HOG and LBP alongside modern deep learning approaches for image representation and retrieval.

Understanding ConvNeXt - the systematic modernization of convolutional networks that proved CNNs could compete with Vision Transformers by adopting their best design principles.

Understanding image augmentation - why it's crucial for computer vision success, what techniques work best, and how to apply them effectively to build robust models with limited data.

Exploring PyTorch Lightning, a framework that organizes PyTorch code for scalable, reproducible, and maintainable deep learning projects.

Understanding Batch Normalization, one of the most important techniques for training deep neural networks, from theory to implementation.

Exploring the landscape of deep learning optimizers from SGD to Adam and beyond, understanding their mechanics and when to use each one.

A conceptual deep-dive into the Transformer architecture that revolutionized AI. Learn the intuition behind attention, why it works, and how it powers modern language models like GPT and BERT.

Understanding the different computer vision tasks - object detection, semantic segmentation, instance segmentation, and panoptic segmentation - their applications, and when to use each approach.