Overview
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Learn the fundamentals of neural networks in this comprehensive lecture covering essential concepts from nonlinear and multiclass classification to advanced implementation techniques. Explore the mathematics behind softmax functions and dive deep into the architecture and training of feedforward neural networks (FNNs). Master practical implementation strategies including batch processing, network initialization, and regularization methods to optimize neural network performance. Build a strong foundation in neural network theory and practice through detailed explanations and real-world applications.
Syllabus
Lecture starts
Nonlinear classification
Multiclass classification
Softmax
Feedforward neural networks
Training FNNs
Tricks batching, init, regularization
Taught by
UofU Data Science