Dive into the second part of an introduction to neural networks in this comprehensive lecture from the University of Central Florida's Computer Vision course. Explore computational implementations of neural activation functions, binary image classification, and multi-class neural networks. Learn about bias convenience, composition, and the introduction of non-linearities in neural networks. Examine various activation functions, including the UCF Perceptron model, and understand the structure and goals of multi-layer perceptrons (MLPs). Conclude by evaluating MLP performance on the MNIST dataset, gaining valuable insights into deep learning applications for computer vision tasks such as filtering, classification, detection, and segmentation.