Deep learning

Where Did the GPU Memory Go?

Estimating memory consumption of GPT-2.

23 hrs ago •

Transfer Learning, Fine-tuning, Multitask Learning and Federated Learning

Four critical model training paradigms that you MUST know for real-world ML modelling.

May 16 •

Introduction to CUDA Programming

Uncovering the internal workings of model.cuda().

May 11 •

Regularize Neural Networks Using Label Smoothing

Make your model less overconfident.

May 5 •

LoRA-derived Techniques for Optimal LLM Fine-tuning

LoRA-variants explained in a beginner-friendly way.

Apr 30 •

Train and Test-time Data Augmentation

More data from existing data.

Apr 12 •

and

A Beginner-friendly Guide to Multi-GPU Training

Learn how to scale models using distributed training.

Apr 6 •

Is Your Model Data Deficient?

More data may not always help.

Mar 25 •

11 Powerful Techniques to Supercharge Your ML Models

Take your ML models to the next level.

Mar 22 •

From PyTorch to PyTorch Lightning

Simplify deep learning model building and training with PyTorch Lightning.

Mar 13 •

Gradient Accumulation in Neural Networks and How it Works

An underrated technique to train neural networks in memory constrained settings.

Mar 8 •

Augmenting LLMs: Fine-Tuning or RAG?

The trade-offs between fine-tuning and RAG.

Mar 6 •

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