Machine Learning Explained - A Hiker Analogy
Автор: Jon Bowden
Загружено: 2026-01-05
Просмотров: 67
How Machine Learning Really Learns (The Hiker Analogy)
This video explains one of the most important ideas in machine learning and deep learning:
How models learn by making small adjustments based on error.
Using a simple “hiker in the fog” analogy, we walk through:
What a loss function really represents
How gradient descent works (without maths)
Why learning rate matters
Why training is a process of trial, feedback, and adjustment
Why models don’t “know” the right answer in advance
This video is conceptual, not a coding tutorial.
Its goal is to give you the mental model you need to help understand:
Why neural networks behave the way they do
Why training can be unstable
Why overfitting happens
Why modern AI systems (including LLMs) sometimes fail confidently
If you’re learning about machine learning, deep learning, or large language models — especially in real-world or enterprise settings — this foundation matters.
📘 This video is part of the CodeVision Academy learning path and complements our Machine Learning & Deep Learning Foundations module.
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