University of Minnesota - Twin Cities

Breaking this down into to components: - What are Neurons? - How are they connected? - At first, we can think view a neural network as how our brains work like neurons signaling one another (Also the inspiration for its name). In a sense these models allow computer programs to recognize patterns and solve common problems. - Neural Networks are comprised of node layers. It contains the input layer, one (or more) hidden layers, and an output layer. - Nodes (neurons) are connected via arrows and each has an associated weight and threshold. If the node surpasses the threshold set, then that neuron is activated and the data is sent on the next layer. This process repeats until it goes to the output layer.

Neural Network

Python Neural Network Class

Training a neural network is the process of adjusting its weights and biases so that the network's outputs come to match the desired targets as closely as possible. Starting from an initial set of parameters, the network computes an output via forward propagation, compares that output to the target using a loss (error) function, and then updates the weights and biases based on the resulting error signal, typically via backpropagation combined with a gradient-based optimization step. This iterative fine-tuning of weights and biases from the training data, repeated until the network's performance converges, is what is meant by "training" a neural network.

Training Neural Networks

Machine Learning Yearning refers to neural networks as also known as deep learning.

Neural Networks Also Known as Deep Learning

In this context, a small neural network means a neural network with only a small number of hidden units, layers, or parameters.

Small Neural Network Scale

Neural network architecture is one of the important details for performance, and there has been much innovation in neural network architecture.

Neural Network Architecture Matters for Performance

The most common types of neural networks are: - Feedforward neural networks - Convolutional neural networks - Recurrent neural networks

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