ML lecture 4 - Neural Networks

Study Notes

Neural Networks and Multi-Layer Perceptrons

Perceptrons gained interest in the 1960s but had slow convergence, especially with small class margins or non-linearly separable classes
Perceptrons guarantee separation for linearly separable classes but may converge slowly or not at all for non-linearly separable classes
Gradient descent is an iterative optimization algorithm used in neural networks to find local minimums of cost functions
Multilayer neural networks, such as multi-layer perceptrons (MLPs), can solve problems that single-layer networks cannot, like the XOR problem
Feed-forward neural networks (FFNN) consist of layers of neurons with synaptic strength (weights) and activation strength
FFNN are used for regression or classification, with the output layer returning continuous values or probabilities using the softmax function
Recurrent neural networks (RNNs) have all-to-all connections and can express feedback or time progression
Multi-layer neural networks can achieve non-linear separators and more complex tasks than single-layer networks
Non-linear activation functions are introduced into multi-layer networks to enable more processing power than a single layer
Backpropagation is used to compute gradients and train multi-layer neural networks
Credit-assignment problem arises in multi-layer networks, as errors can be due to weights from both previous and downstream layers
Hidden layers in multi-layer networks serve as feature detectors, capturing salient features of the training data through nonlinear transformations.

Description

Test your knowledge of neural network basics with this quiz. Explore topics such as dendrites, axons, cell bodies, synapse modeling, and the functioning of the simplest neural network, the perceptron.