← Deep Learning / Neural Networks
Prerequisites & knowledge map
Everything below is a concept Deep Learning / Neural Networks leans on that ISN'T taught inside the course itself — not the whole of another course, just the specific skills.
Calculus III — Multivariable Calculus
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College Algebra
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required
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Linear Algebra — Vectors, Matrices, and the Geometry of Data
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required
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Calculus I — Differential & Integral Calculus
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Optimization
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L2 regularization (ridge)
Weight decay in a network's training loop is L2 regularization applied to its parameters.
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Stochastic gradient descent
Optimization's own general treatment of estimating the gradient from a random sample, of which this section's minibatch is one concrete case.
helpful
Discrete Mathematics I — Logic, Proof, and Structure
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required
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required
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helpful
Fourier Analysis
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required
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required
Advanced Multivariable Calculus
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required
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required
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required
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required
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Digital Signal Processing
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required