Fundamentals of Neural Networks - Lab 3 - Introduction to Neural Network

This video tutorial covers the basics of neural networks using Keras and TensorFlow. It begins with an introduction to neural networks and the Keras API, followed by data preparation using the FashionMNIST dataset. The tutorial then guides viewers through building a simple neural network architecture, compiling and training the model, and evaluating its performance. It concludes with experimentation and tuning of the model's parameters to optimize performance.

What is the purpose of using Keras and TensorFlow in the context of neural networks?

Describe the Fashion MNIST dataset and its relevance to computer vision.

Explain the significance of rescaling the image data in neural network training.

What is the role of the activation function in a neural network, and why is ReLU commonly used?

How does the softmax function contribute to multi-class classification in neural networks?

What are some common optimization algorithms used in training neural networks?

How can changing the number of epochs affect the performance of a neural network?

What is the difference between overfitting and underfitting in the context of model training?

Discuss the importance of using a separate test dataset for evaluating model performance.

What strategies can be employed to tune the parameters of a neural network effectively?