Classical ML and Deep Learning
15 Ways to Optimize Neural Network Training (With Implementation)
Techniques that help you become a "machine learning engineer" from a "machine learning model developer."
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Classical ML and Deep Learning
A Detailed and Beginner-Friendly Introduction to PyTorch Lightning: The Supercharged PyTorch
Immensely simplify deep learning model building with PyTorch Lightning.
LLM and Fine-tuning
Augmenting LLMs: Fine-Tuning or RAG?
Understanding the tradeoffs between RAG and Fine-tuning, and owning the model vs. using a third-party host.
LLM and Fine-tuning
Implementing DoRA (an Improved LoRA) from Scratch
A powerful LoRA-variant explained in a beginner-friendly way and implemented in PyTorch.
LLM and Fine-tuning
Understanding LoRA-derived Techniques for Optimal LLM Fine-tuning
LoRA-variants explained in a beginner-friendly way.
LLM and Fine-tuning
Implementing LoRA From Scratch for Fine-tuning LLMs
Understanding the challenges of traditional fine-tuning and addressing them with LoRA.
Engineering Best Practices
Model Compression: A Critical Step Towards Efficient Machine Learning
Four critical ways to reduce model footprint and inference time.
Engineering Best Practices
Deploy, Version Control, and Manage ML Models Right From Your Jupyter Notebook with Modelbit
Deployment has possibly never been so simple.
