Deep Learning: Multilayer Perceptron and Training

This lecture introduces the concept of deep learning, focusing on the multilayer perceptron (MLP) model. It begins with a recap of data representations, emphasizing the challenges of heterogeneous data and the importance of preprocessing. The instructor discusses the bag of words model for text and extends this idea to images using bags of visual words. The lecture then transitions to the MLP, explaining its structure, including input, hidden, and output layers, and the role of activation functions. The training process for MLPs is detailed, highlighting the use of gradient descent and backpropagation to optimize parameters. The instructor addresses the challenges of vanishing gradients and the significance of weight initialization strategies. Additionally, the lecture covers techniques to prevent overfitting, such as dropout and regularization. The session concludes with a discussion on transitioning from regression to classification tasks using softmax and cross-entropy loss functions, setting the stage for future lectures on applying these concepts to image data.