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Model Interpretability (Part 2)
Classical ML and Deep Learning

Model Interpretability (Part 2)

A deep dive into interpretability methods, why they matter, along with their intuition, considerations, how to avoid being misled, and code.

· Avi Chawla

Model Interpretability (Part 1)
Classical ML and Deep Learning

Model Interpretability (Part 1)

A deep dive into PDPs and ICE plots, along with their intuition, considerations, how to avoid being misled, and code.

· Avi Chawla

Spark DataFrames and Big Data ML using PySpark on Databricks
Engineering Best Practices

Spark DataFrames and Big Data ML using PySpark on Databricks

A completely hands-on and beginner-friendly deep dive on PySpark using Databricks.

· Avi Chawla

Graph Neural Networks Part 3 (Implementation Included)
Graph ML Course

Graph Neural Networks Part 3 (Implementation Included)

A practical and beginner-friendly guide to building neural networks on graph data.

· Avi Chawla

Graph Neural Networks Part 2 (Implementation Included)
Graph ML Course

Graph Neural Networks Part 2 (Implementation Included)

A practical and beginner-friendly guide to building neural networks on graph data.

· Avi Chawla

Graph Neural Networks Part 1 (Implementation Included)
Graph ML Course

Graph Neural Networks Part 1 (Implementation Included)

A practical and beginner-friendly guide to building neural networks on graph data.

· Avi Chawla

Model Calibration (Part 2)
Classical ML and Deep Learning

Model Calibration (Part 2)

How to make ML models reflect true probabilities in their predictions?

· Avi Chawla

Federated Learning: A Critical Step Towards Privacy-Preserving ML
Classical ML and Deep Learning

Federated Learning: A Critical Step Towards Privacy-Preserving ML

Learn real-world ML model development with a primary focus on data privacy – A practical guide.

· Avi Chawla

Model Calibration (Part 1)
Classical ML and Deep Learning

Model Calibration (Part 1)

How to make ML models reflect true probabilities in their predictions?

· Avi Chawla

A Practical Guide to Scaling ML Model Training
Engineering Best Practices

A Practical Guide to Scaling ML Model Training

GPUs - GPU Clusters - Distributed Training.

· Avi Chawla

Conformal Predictions: Build Confidence in Your ML Model's Predictions
Classical ML and Deep Learning

Conformal Predictions: Build Confidence in Your ML Model's Predictions

A critical step towards building and using ML models reliably.

· Avi Chawla

Quantization: Optimize ML Models to Run Them on Tiny Hardware
Engineering Best Practices

Quantization: Optimize ML Models to Run Them on Tiny Hardware

A must-know skill for ML engineers to reduce model footprint and inference time.

· Avi Chawla

How to Structure Your Code for Machine Learning Development
Engineering Best Practices

How to Structure Your Code for Machine Learning Development

A highly overlooked yet critical skill for data scientists.

· Avi Chawla

Develop an Elegant Testing Framework For Data Science Projects Using Pytest
Engineering Best Practices

Develop an Elegant Testing Framework For Data Science Projects Using Pytest

A Comprehensive Guide to Pytest for data scientists.

· Avi Chawla

A Beginner-friendly Guide to Multi-GPU Model Training
Engineering Best Practices

A Beginner-friendly Guide to Multi-GPU Model Training

Models are becoming bigger and bigger. Learn how to scale models using distributed training.

· Avi Chawla

Object-Oriented Programming with Python for Data Scientists
Engineering Best Practices

Object-Oriented Programming with Python for Data Scientists

A beginner to advanced guide for Python OOP.

· Avi Chawla

11 Powerful Techniques To Supercharge Your ML Models
Engineering Best Practices

11 Powerful Techniques To Supercharge Your ML Models

Take your ML models to the next level with 11 lesser-known techniques.

· Avi Chawla

Implementing KANs From Scratch Using PyTorch
Classical ML and Deep Learning

Implementing KANs From Scratch Using PyTorch

A step-by-step demonstration of an emerging neural network architecture — KANs.

· Avi Chawla

A Beginner-friendly Introduction to Kolmogorov Arnold Networks (KAN)
Classical ML and Deep Learning

A Beginner-friendly Introduction to Kolmogorov Arnold Networks (KAN)

What are KANs, how are they trained, and what makes them so powerful?

· Avi Chawla

Implementing Parallelized CUDA Programs From Scratch Using CUDA Programming
Engineering Best Practices

Implementing Parallelized CUDA Programs From Scratch Using CUDA Programming

A beginner-friendly guide for curious minds who don't know the internal workings of model.cuda().

· Avi Chawla

8 Fatal (Yet Non-obvious) Pitfalls and Cautionary Measures in Data Science
Engineering Best Practices

8 Fatal (Yet Non-obvious) Pitfalls and Cautionary Measures in Data Science

8 data science lesson I wish I had known earlier.

· Avi Chawla

15 Ways to Optimize Neural Network Training (With Implementation)
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."

· Avi Chawla

A Detailed and Beginner-Friendly Introduction to PyTorch Lightning: The Supercharged PyTorch
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.

· Avi Chawla

Augmenting LLMs: Fine-Tuning or RAG?
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.

· Avi Chawla

Implementing DoRA (an Improved LoRA) from Scratch
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.

· Avi Chawla

Understanding LoRA-derived Techniques for Optimal LLM Fine-tuning
LLM and Fine-tuning

Understanding LoRA-derived Techniques for Optimal LLM Fine-tuning

LoRA-variants explained in a beginner-friendly way.

· Avi Chawla

Implementing LoRA From Scratch for Fine-tuning LLMs
LLM and Fine-tuning

Implementing LoRA From Scratch for Fine-tuning LLMs

Understanding the challenges of traditional fine-tuning and addressing them with LoRA.

· Avi Chawla

Model Compression: A Critical Step Towards Efficient Machine Learning
Engineering Best Practices

Model Compression: A Critical Step Towards Efficient Machine Learning

Four critical ways to reduce model footprint and inference time.

· Avi Chawla

Deploy, Version Control, and Manage ML Models Right From Your Jupyter Notebook with Modelbit
Engineering Best Practices

Deploy, Version Control, and Manage ML Models Right From Your Jupyter Notebook with Modelbit

Deployment has possibly never been so simple.

· Avi Chawla

PyTorch Models Are Not Deployment-Friendly! Supercharge Them With TorchScript.
Engineering Best Practices

PyTorch Models Are Not Deployment-Friendly! Supercharge Them With TorchScript.

Eliminating the dependence of PyTorch models on Python.

· Avi Chawla