5. Learning a subject

Prompt Example: 

1. Explain the fundamental concepts of neural networks. 
2. Provide examples and visual aids if possible. 
3. Suggest further readings and resources for in-depth understanding. 
4. Audience: University students new to neural networks and machine learning. 
5. Expected Outcome: A clear understanding of the basics of neural networks. 

Example User Query:  

Explain in 350 words the fundamentals of neural networks, including terms like neurons, connections, and a learning rule. Provide examples and diagrams if possible, and suggest further readings for an in-depth understanding. The tone should be academic but accessible to new university students learning about neural networks and machine learning.

ELM’s Response: 

Neural networks are a key component of modern machine learning, modelled after the human brain's structure and function. They are made up of interconnected units called neurons, which process and transmit information. Each neuron computes a weighted sum of its input signals, adds a bias term, and passes the result through an activation function to produce an output.

Neurons are organised in layers: an input layer, one or more hidden layers, and an output layer. The connections between neurons, known as weights, are crucial for the network's behaviour. These weights are adjusted during training to minimise the difference between the network’s predictions and the actual outcomes.

The primary learning rule for training neural networks is the backpropagation algorithm, which involves forward and backward propagation. In forward propagation, input data moves through the network to generate an output. The difference between the predicted and actual output is measured using a loss function. Backward propagation then uses this error to refine the weights. It computes the gradient of the loss function for each weight using calculus's chain rule and adjusts the weights to reduce the loss.

A basic example of a neural network is a feedforward network used for recognising handwritten digits. It takes image pixels as input, processes them through layers of neurons, and outputs a digit prediction.

Here's a basic diagram to illustrate: 

Image

For further understanding, I recommend the following readings: 

1. "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville - A comprehensive guide offering theoretical and practical insights into neural networks and deep learning.
2. "Neural Networks and Deep Learning" by Michael Nielsen - An accessible online book introducing fundamental concepts with practical examples.
3. Coursera’s Deep Learning Specialisation by Andrew Ng - Online courses providing a beginner-friendly introduction to neural networks, complete with hands-on practice.