Gradient descent, how neural networks learn: Deep learning - Part 2 of 4

The video recaps neural network structure and introduces gradient descent, a key concept in machine learning. It explains how networks learn through adjusting weights and biases to minimize a cost function. The video discusses the performance of a neural network on handwritten digit recognition and explores its limitations. It encourages active engagement with the material and provides resources for further learning. The video concludes with insights into modern image recognition networks and their learning processes.

What is the primary goal of the video discussed in the text?

Explain the concept of gradient descent as introduced in the video.

What are the roles of weights and biases in a neural network?

How does the network classify a given digit according to the text?

Describe how the neural network learns from training data.

Discuss the implications of the network's performance on unseen images.

How does the structure of the MNIST database facilitate the training of neural networks?

What is the significance of the cost function in the training of a neural network?

What does the term 'back propagation' refer to in the context of neural networks?

What are the limitations of the neural network discussed in the video?