Pytorch

Complete guide to setting up a robust development environment for LLM fine-tuning, covering hardware requirements, software installation, data preparation workflows, and optimization techniques.

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

Exploring Test-Time Augmentation techniques that improve model predictions by aggregating results from multiple augmented versions of test images.

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.

Understanding ArcFace loss - the breakthrough technique that revolutionized face recognition by teaching networks to create better feature boundaries through angular margins.

Quick setup to get everything installed on your local machine for tensorflow and pytorch