10 NumPy One-Liners to Simplify Feature Engineering
When building machine learning models, most developers focus on model architectures and hyperparameter tuning.
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Your First OpenAI API Project in Python Step-By-Step
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Securing FastAPI Endpoints for MLOps: An Authentication Guide
In today's AI world, data scientists are not just focused on training and optimizing machine learning models.
Skip Connections in Transformer Models
This post is divided into three parts; they are: • Why Skip Connections are Needed in Transformers • Implementation of Skip Connections in Transformer Models • Pre-norm vs Post-norm Transformer Architectures Transformer models, like other deep learning models, stack many layers on top of each other.
5 Advanced RAG Architectures Beyond Traditional Methods
Retrieval-augmented generation (RAG) has shaken up the world of language models by combining the best of two worlds: <a href="https://www.
Mixture of Experts Architecture in Transformer Models
This post covers three main areas: • Why Mixture of Experts is Needed in Transformers • How Mixture of Experts Works • Implementation of MoE in Transformer Models The Mixture of Experts (MoE) concept was first introduced in 1991 by <a href="https://www.
Your First Local LLM API Project in Python Step-By-Step
Interested in leveraging a large language model (LLM) API locally on your machine using Python and not-too-overwhelming tools frameworks? In this step-by-step article, you will set up a local API where you'll be able to send prompts to an LLM downloaded on your machine and obtain responses back.
Linear Layers and Activation Functions in Transformer Models
This post is divided into three parts; they are: • Why Linear Layers and Activations are Needed in Transformers • Typical Design of the Feed-Forward Network • Variations of the Activation Functions The attention layer is the core function of a transformer model.
LayerNorm and RMS Norm in Transformer Models
This post is divided into five parts; they are: • Why Normalization is Needed in Transformers • LayerNorm and Its Implementation • Adaptive LayerNorm • RMS Norm and Its Implementation • Using PyTorch's Built-in Normalization Normalization layers improve model quality in deep learning.
7 AI Agent Frameworks for Machine Learning Workflows in 2025
Machine learning practitioners spend countless hours on repetitive tasks: monitoring model performance, retraining pipelines, data quality checks, and experiment tracking.
10 Must-Know Python Libraries for MLOps in 2025
MLOps, or machine learning operations, is all about managing the end-to-end process of building, training, deploying, and maintaining machine learning models.
Unlocking Performance: Accelerating Pandas Operations with Polars
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7 Concepts Behind Large Language Models Explained in 7 Minutes
If you've been using large language models like GPT-4 or Claude, you've probably wondered how they can write actually usable code, explain complex topics, or even help you debug your morning coffee routine (just kidding!).
Interpolation in Positional Encodings and Using YaRN for Larger Context Window
This post is divided into three parts; they are: • Interpolation and Extrapolation in Sinusoidal Encodings and RoPE • Interpolation in Learned Encodings • YaRN for Larger Context Window Sinusoidal encodings excel at extrapolation due to their use of continuous functions: $$ \begin{aligned} PE(p, 2i) &= \sin\left(\frac{p}{10000^{2i/d}}\right) \\ PE(p, 2i+1) &= \cos\left(\frac{p}{10000^{2i/d}}\right) \end{aligned} $$ You can simply substitute $p$ with a larger value to obtain the positiona
How to Combine Scikit-learn, CatBoost, and SHAP for Explainable Tree Models
Machine learning workflows often involve a delicate balance: you want models that perform exceptionally well, but you also need to understand and explain their predictions.
Positional Encodings in Transformer Models
This post is divided into five parts; they are: • Understanding Positional Encodings • Sinusoidal Positional Encodings • Learned Positional Encodings • Rotary Positional Encodings (RoPE) • Relative Positional Encodings Consider these two sentences: "The fox jumps over the dog" and "The dog jumps over the fox".
Advanced Feature Engineering Using Scikit-Learn Pipelines with Pandas’ ColumnTransformer and NumPy Arrays
Pandas , NumPy , and Scikit-learn .
Navigating Imbalanced Datasets with Pandas and Scikit-learn
Imbalanced datasets, where a majority of the data samples belong to one class and the remaining minority belong to others, are not that rare.
Step-by-Step Guide to Deploying Machine Learning Models with FastAPI and Docker
You've trained your machine learning model, and it's performing great on test data.
Implementing Vector Search from Scratch: A Step-by-Step Tutorial
There’s no doubt that search is one of the most fundamental problems in computing.