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Text Embedding Generation with Transformers

This post is divided into three parts; they are: • Understanding Text Embeddings • Other Techniques to Generate Embedding • How to Get a High-Quality Text Embedding? Text embeddings are to use...

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Using Auto Classes in the Transformers Library

This post is divided into three parts; they are: • What Is Auto Classes • How to Use Auto Classes • Limitations of the Auto Classes There is no class called "AutoClass" in the transformers library.

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Example Applications of Text Embedding

This post is divided into five parts; they are: • Recommendation Systems • Cross-Lingual Applications • Text Classification • Zero-Shot Classification • Visualizing Text Embeddings A simple...

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5 Reasons Why Traditional Machine Learning is Alive and Well in the Age of LLMs

Nowadays, everyone across AI and related communities talks about generative AI models, particularly the large language models (LLMs) behind widespread applications like ChatGPT, as if they have...

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How to Perform Scikit-learn Hyperparameter Optimization with Optuna

Optuna is a machine learning framework specifically designed for automating hyperparameter optimization , that is, finding an externally fixed setting of machine learning model hyperparameters that...

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Understanding RAG Part IX: Fine-Tuning LLMs for RAG

Be sure to check out the previous articles in this series: •

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Understanding RAG Part X: RAG Pipelines in Production

Be sure to check out the previous articles in this series: •

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5 Lessons Learned Building RAG Systems

Retrieval augmented generation (RAG) is one of 2025's hot topics in the AI landscape.

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Generating and Visualizing Context Vectors in Transformers

This post is divided into three parts; they are: • Understanding Context Vectors • Visualizing Context Vectors from Different Layers • Visualizing Attention Patterns Unlike traditional word embeddings...

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Applications with Context Vectors

This post is divided into two parts; they are: • Contextual Keyword Extraction • Contextual Text Summarization Contextual keyword extraction is a technique for identifying the most important words in a...

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Quantization in Machine Learning: 5 Reasons Why It Matters More Than You Think

Quantization might sound like a topic reserved for hardware engineers or AI researchers in lab coats.

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Detecting & Handling Data Drift in Production

Machine learning models are trained on historical data and deployed in real-world environments.

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Building a RAG Pipeline with llama.cpp in Python

Using llama.

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Further Applications with Context Vectors

This post is divided into three parts; they are: • Building a Semantic Search Engine • Document Clustering • Document Classification If you want to find a specific document within a collection, you...

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Understanding Text Generation Parameters in Transformers

This post is divided into seven parts; they are: - Core Text Generation Parameters - Experimenting with Temperature - Top-K and Top-P Sampling - Controlling Repetition - Greedy Decoding and Sampling -...

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10 Must-Know Python Libraries for Machine Learning in 2025

Python is one of the most popular languages for machine learning, and it’s easy to see why.

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Let’s Build a RAG-Powered Research Paper Assistant

In the era of generative AI, people have relied on LLM products such as ChatGPT to help with tasks.

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Building RAG Systems with Transformers

This post is divided into five parts: • Understanding the RAG architecture • Building the Document Indexing System • Implementing the Retrieval System • Implementing the Generator • Building the...

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10 Python One-Liners for Machine Learning Modeling 

Building machine learning models is an undertaking which is now within everyone’s reach.

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Advanced Techniques to Build Your RAG System

This post is divided into three parts; they are: • Query Expansion and Reformulation • Hybrid Retrieval: Dense and Sparse Methods • Multi-Stage Retrieval with Re-ranking One of the challenges in RAG...

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