Machine Learning
A Hands-on Demo on Autoencoders
...along with applications.
Avi Chawla
373 posts published
RAG Systems Course
A Crash Course on Building RAG Systems – Part 3 (With Implementation)
A deep dive into making RAG systems faster (with implementations).
Data Analysis
Use Box Plots with Caution!
They Can Be Misleading.
Correlation Analysis
How to Assess Correlation on Ordinal Data?
The limitations of Pearson correlation.
Clustering
Categorization of Clustering Algorithms
...explained in a single frame.
Machine Learning
Accuracy Can Be Deceptive
... and how to catch it.
LLMs
Build a Multi-agent Research Assistant With SwarmZero
Your own PerplexityAI (100% local).
Causal inference
Causality (Part 2)
A guide to building robust decision-making systems in businesses with causal inference.
LLMs
Run LLMs Locally with Ollama
A step-by-step hands-on guide.
RAG Systems Course
A Crash Course on Building RAG Systems – Part 2 (With Implementation)
A deep dive into evaluating RAG systems (with implementations).
RAG
A crash course on RAG systems - Part 2
Beginner-friendly and with implementation.
LLMs
Prompting vs. RAG vs. Fine-tuning
Which one is best for you?
Dimensionality Reduction
Avoid Using PCA for Visualization Unless...
...this plot says so.
LLMs
From PyTorch to Lightning Fabric
Scale model training with 4 small changes.
LLMs
Techniques to Extend Context Length of LLMs
Key techniques, explained in simple terms.
Machine Learning
KernelPCA vs. PCA
...and when to not use KernelPCA
RAG Systems Course
Foundations of RAG systems
A practical and beginner-friendly crash course on building RAG apps (with implementations).
Classical ML and Deep Learning
Causality (Part 1)
A guide to building robust decision-making systems in businesses with causal inference.
LLM and Fine-tuning
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.
LLMs
5 Chunking Strategies For RAG
...explained in a single frame.
LLM and Fine-tuning
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.
Classical ML and Deep Learning
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.
PCA
Sparse Random Projections
An alternative to PCA for highly dimensional datasets.
Engineering Best Practices
5 Must-Know Ways to Test ML Models in Production (Implementation Included)
A beginner-friendly guide to model testing.
LLMs
NumPy Cheat Sheet for Data Scientists
40 most used methods.
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.
Machine Learning
Implementing a Siamese Network
A beginner-friendly implementation guide.
Machine Learning
Contrastive Learning Using Siamese Networks
Building a face unlock system.
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.
37 min read
A Crash Course on Model Calibration
Modern neural networks being trained today are highly misleading. They appear to be heavily overconfident in their predictions. For instance, if a model predicts an event with a 70% probability, then ideally, out of 100 such predictions, approximately 70 should result in the event occurring. However, many experiments have revealed