Fundamentals of Neural Networks - Lab 3 - Deep CNN

The video tutorial covers the construction and evaluation of a deep convolutional neural network (CNN) using TensorFlow. It begins with an introduction to the CNN architecture and the Fashion MNIST dataset, which consists of 70,000 grayscale images across 10 categories. The tutorial then explains data preprocessing, including rescaling and visualization. The CNN model is built using TensorFlow's sequential API, and the importance of using the functional API for flexibility is highlighted. The model is trained and evaluated, achieving a 92% accuracy on the training set and 90% on the test set. The tutorial concludes with a discussion on prediction accuracy and potential research questions.

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