Machine Learning
Building a Context Engineering Workflow
..explained step-by-step with code.
Machine Learning
A collection of 62 posts
LLM and Fine-tuning
A Beginner-friendly and Comprehensive Deep Dive on Vector Databases
Understanding every little detail on vector databases and their utility in LLMs, along with a hands-on demo.
Machine Learning
Discriminative vs. Generative Models
a popular interview question.
Machine Learning
A Hands-on Demo on Autoencoders
...along with applications.
Clustering
Categorization of Clustering Algorithms
...explained in a single frame.
Machine Learning
Accuracy Can Be Deceptive
... and how to catch it.
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
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.
Engineering Best Practices
5 Must-Know Ways to Test ML Models in Production (Implementation Included)
A beginner-friendly guide to model testing.
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.
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.
Graph ML Course
Graph Neural Networks Part 3 (Implementation Included)
A practical and beginner-friendly guide to building neural networks on graph data.
Machine Learning
Knowledge Distillation with Teacher Assistant for Model Compression
A better and intuitive technique to model compression.
Graph ML Course
Graph Neural Networks Part 2 (Implementation Included)
A practical and beginner-friendly guide to building neural networks on graph data.
Graph ML Course
Graph Neural Networks Part 1 (Implementation Included)
A practical and beginner-friendly guide to building neural networks on graph data.
Classical ML and Deep Learning
Model Calibration (Part 2)
How to make ML models reflect true probabilities in their predictions?
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.
Classical ML and Deep Learning
Model Calibration (Part 1)
How to make ML models reflect true probabilities in their predictions?
Engineering Best Practices
A Practical Guide to Scaling ML Model Training
GPUs - GPU Clusters - Distributed Training.
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.
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.
Engineering Best Practices
How to Structure Your Code for Machine Learning Development
A highly overlooked yet critical skill for data scientists.
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.
Engineering Best Practices
11 Powerful Techniques To Supercharge Your ML Models
Take your ML models to the next level with 11 lesser-known techniques.
Machine Learning
Shuffle Feature Importance
An intuitive and reliable technique to measure feature importance.
Classical ML and Deep Learning
Implementing KANs From Scratch Using PyTorch
A step-by-step demonstration of an emerging neural network architecture — KANs.
