A Crash Course on Building RAG Systems – Part 5 (With Implementation)
A deep dive into key components of multimodal systems—CLIP embeddings, multimodal prompting, and tool calling.
· Avi Chawla
A Crash Course on Building RAG Systems – Part 4 (With Implementation)
A deep dive into handling multiple data types in RAG systems (with implementations).
· Avi Chawla
A Crash Course on Building RAG Systems – Part 3 (With Implementation)
A deep dive into making RAG systems faster (with implementations).
· Avi Chawla
Causality (Part 2)
A guide to building robust decision-making systems in businesses with causal inference.
· Avi Chawla
A Crash Course on Building RAG Systems – Part 2 (With Implementation)
A deep dive into evaluating RAG systems (with implementations).
· Avi Chawla
Foundations of RAG systems
A practical and beginner-friendly crash course on building RAG apps (with implementations).
· Akshay Pachaar
Causality (Part 1)
A guide to building robust decision-making systems in businesses with causal inference.
· Avi Chawla
AugSBERT: Bi-encoders + Cross-encoders for Sentence Pair Similarity Scoring – Part 2
A deep dive into extensions of cross-encoders and bi-encoders for sentence pair similarity.
· Avi Chawla
Bi-encoders and Cross-encoders for Sentence Pair Similarity Scoring (Part 1)
A deep dive into why BERT isn't effective for sentence similarity and advancements that shaped this task forever.
· Avi Chawla
Model Interpretability (Part 3)
A deep dive into interpretability methods, why they matter, along with their intuition, considerations, how to avoid being misled, and code.
· Avi Chawla
5 Must-Know Ways to Test ML Models in Production (Implementation Included)
A beginner-friendly guide to model testing.
· Avi Chawla
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)
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
A completely hands-on and beginner-friendly deep dive on PySpark using Databricks.
· Avi Chawla
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)
A practical and beginner-friendly guide to building neural networks on graph data.
· Avi Chawla
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)
How to make ML models reflect true probabilities in their predictions?
· Avi Chawla
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)
How to make ML models reflect true probabilities in their predictions?
· Avi Chawla
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
A critical step towards building and using ML models reliably.
· Avi Chawla
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
A highly overlooked yet critical skill for data scientists.
· Avi Chawla
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
Models are becoming bigger and bigger. Learn how to scale models using distributed training.
· Avi Chawla
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
Take your ML models to the next level with 11 lesser-known techniques.
· Avi Chawla
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)
What are KANs, how are they trained, and what makes them so powerful?
· Avi Chawla