Introduction
What this guide covers
Rigorous mathematical foundations, optimization in non-convex spaces, regularization dynamics and deep representation architectures.
Overview
Topics covered
- Introduction
- Deep Learning
- Mathematical Foundations of Deep Learning
- Fundamentals of Multilinear Algebra and Tensors
- Information Theory and Probability Divergences
- The Artificial Neuron and the Multilayer Perceptron (MLP)
Related concepts
Main connections
Compatible notation
Mathematical sample in LaTeX
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F(x) = \sum_{i=1}^{k} \beta_i \sigma\left(\mathbf{w}_i^T \mathbf{x} + v_i\right)