Lecture Notes Review - Neural Networks

These flashcards cover key concepts and vocabulary from the lecture on Neural Networks, helping reinforce students' understanding of the material.

Neural Networks (NNs)

Computational models inspired by the structure and function of the human brain.

Backpropagation learning rule

An algorithm for training neural networks, allowing them to minimize errors by adjusting weights.

Hebbian learning rule

A learning principle that suggests that synaptic strength is increased when the presynaptic and postsynaptic neurons are activated simultaneously.

Universal Turing machine

A theoretical machine that can simulate any algorithm, and in this context, refers to the capabilities of neural networks.

Biological Neural Network (BNN)

A network of neurons in biological systems, mimicking the structure and function of human brains.

Artificial Neural Network (ANN)

A computing system that mimics the operation of human brain neurons to process data.

Gradient descent

An optimization algorithm often used for minimizing a loss function in training a neural network.

Neural Network Layer

A level in a neural network where computations take place; commonly includes input, hidden, and output layers.

Deep Learning

A subset of machine learning involving neural networks with many layers, capable of learning high-level abstractions.

Generative Adversarial Network (GAN)

A framework for generating data by pitting two neural networks against each other - a generator and a discriminator.

Pattern completion

A process in neural networks where missing pieces of data are inferred based on existing data points.

Threshold function

An activation function in perceptrons that determines whether a neuron will fire based on input data.

McCulloch-Pitts Neuron

The first mathematical model of a biological neuron to perform logical functions, forming the basis for neural networks.

Deep Neural Network (DNN)

A neural network with multiple hidden layers that can represent complex patterns in data.