Lecture
This lecture covers the training of deep neural networks, focusing on stochastic gradient descent, mini-batch processing, and normalization techniques. It also discusses strategies to prevent overfitting, such as dropout and L1/L2 regularization. Additionally, it explores the challenges of vanishing gradients and introduces residual networks. The presentation concludes with a look at Hebbian learning, recurrent neural networks, and different types of neural network architectures.