ML Fundamentals
Dimensionality Reduction
Simplifying complex data by keeping only the most important features, like summarizing a 50-page report into key bullet points.
Definition
Techniques for reducing the number of input variables in a dataset while retaining the most important information. Methods include PCA, t-SNE, and UMAP. Used for visualization and preprocessing.
Why it matters
Makes complex datasets manageable and helps models train faster by removing noise and redundancy.
Related terms in ML Fundamentals
Activation Functions
The "switch" inside a neural network that decides whether a neuron should fire, allowing the AI to learn complex non-linear patterns.
Active Learning
A technique where the AI asks humans to label only the most confusing examples, saving time and money on data labeling.
Anomaly Detection
Finding the "weird" stuff in a dataset, like a credit card charge in a foreign country or a broken machine part.
Artificial General Intelligence (AGI)
A hypothetical "super-AI" that can learn and do any intellectual task a human can do, not just one specific thing.
From vocabulary to outcomes
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