The RAMGPT learning series
AI Foundations.
A clear starting point.
A deeper understanding with every lesson.
For readers without a computer science background, and anyone who wants to connect the ideas behind modern AI. Read in order: each lesson builds on the last.
- 01↗
- 02↗
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What Does It Mean for a Machine to Learn?
- 03↗
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Parameters, Weights, and Biases: What Are They in a Neural Network?
- 04↗
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What Is a Tensor? The Shape of Data Inside AI
- 05↗
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What Is a Forward Pass? How Data Moves Through a Neural Network
- 06↗
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How Does a Neural Network Start Learning? Training Begins With Loss
- 07↗
- 08↗
- 09↗
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Embeddings: How AI Turns Meaning Into Coordinates
- 10↗
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Tokenization: How Text Becomes the IDs an AI Model Can Read
- 11↗
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Attention: How Each Token Decides Which Other Tokens Matter
- 12↗
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Transformer: How Attention Becomes a Complete Neural Network Block
- 13↗
- 14↗