深度学习基础及卷积神经网络应用详解

In the first lecture on the basics of deep learning, the focus was on Convolutional Neural Networks (CNN), their structure, and practical applications. The lecture also covered the concept of loss functions, which measure the error between predicted values and actual values. Specifically, mean square error was discussed, along with error backpropagation using the gradient descent method and the chain rule for differentiation. Traditional neural network structures were reviewed, highlighting the limitation of the backpropagation algorithm in solving the training issues, especially concerning the vanishing gradient problem in deep neural networks. The vanishing gradient problem occurs when the chain rule of differentiation leads to diminishing gradients, as seen in popular activation functions like the Sigmoid function. Overall, the lecture emphasized the importance of understanding the fundamentals of deep learning, convolutional neural networks, loss functions, and the challenges faced in training deep neural networks. It provided a comprehensive overview of key concepts and practical applications in the field of deep learning.