AI Fundamentals

An essential primer on the engine of intelligence: Vectors, Logic, and the critical difference between CPUs and GPUs.

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Neural Networks 101

A deep dive into how artificial neurons work, from perceptrons to multi-layer feed-forward networks.

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Training Data & Bias

Understanding the role of data in AI: How models learn, the danger of overfitting, and the challenge of dataset bias.

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The Turing Test & AGI

Exploring the philosophical and technical boundaries between simulated intelligence and true consciousness.

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Transformers & Attention

The architecture that changed everything: understanding the 'Attention' mechanism and the shift from RNNs to Transformers.

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Tokenization & Embeddings

How words become math: exploring the process of tokenization and the high-dimensional world of vector embeddings.

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Generative AI & LLMs

From GPT to Llama: how large language models are scaled and the mechanics of probabilistic text generation.

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Inference & Optimization

Bringing models to life: an introduction to inference, quantization, and the art of fitting huge models into limited VRAM.

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