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.