Neural Networks: Training and Optimization

This lecture delves into the intricacies of neural networks, focusing on training and optimization. The instructor covers topics such as forward and backward passes, stochastic gradient descent, and mini-batch stochastic gradient descent. The lecture also touches on the challenges of training neural networks, the importance of activation functions, and the use of Python code for implementing neural networks. Additionally, the instructor provides insights into the environmental concerns related to the energy consumption of training neural networks. The lecture concludes with a brief review of principal component analysis (PCA) and K-means clustering, highlighting their applications in dimensionality reduction and unsupervised learning.