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🧠 Machine Learning Fundamentals

What is QLoRA?: A Visual Guide to Efficient Finetuning of Quantized LLMs
Sometimes smaller is better. How QLoRA combines efficiency and performance.
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What is Retrieval Augmented Generation? A Visual Guide On RAGs in the Context of LLMs
Without Fine-tuning, Integrate Custom Information and External Data Sources To Give LLMs Relevant Context To Hallucinate Less And Be More Accurate
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What is an Eigenvector?: A Visual Guide to This Fundamental Concept From Linear Algebra
Discover how eigenvectors can simplify complex data, enhance machine learning models, and solve real-world problems
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What is LoRA?: A Visual Guide to Low-Rank Approximation for Fine-Tuning LLMs Efficiently
Why LoRA Is Essential For Model Fine-Tuning
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The Challenges of Building Effective LLM Benchmarks
Current state of LLM evaluation and gaps that need filling with comprehensive and high quality leaderboards
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"Clustering Together": A Visual Guide to the K-Means Algorithm
K-Means Clustering And How It Helps Uncover Hidden Patterns In Data
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Transformers and the Power of Positional Encoding [Transformers Series]
How Transformers Find Order In Data
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"Attention, Please!": A Visual Guide To The Attention Mechanism [Transformers Series]
Develop an intuition behind Attention: why it took over machine learning + LLMs and what it actually does
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Dropout: A Simple Solution to a Complex Problem
Learn about dropout, its variants and how to apply them in your next project
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What are 1x1 Convolutions in CNNs?
Learn what 1x1 convs are and how to build lightweight and performant CNNs
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