Artificial Neural Networks

Artificial Neural Networks: A Summary

Artificial neural networks (ANNs) are computing systems modeled after the biological neural networks in animal brains.
ANNs are composed of connected units called artificial neurons that process signals and transmit them to other neurons.
Each neuron receives signals, processes them, and outputs the result of a non-linear function of the input.
ANNs learn by processing examples and adjusting the weights of neurons and edges according to a learning rule.
They can perform tasks by considering examples without being programmed with task-specific rules, such as identifying images that contain cats.
The first implemented artificial neural network was the perceptron, invented by Frank Rosenblatt in 1958.
Deep learning multilayer perceptrons (MLPs) were first trained by Alexey Grigorevich Ivakhnenko and Valentin Lapa in 1965.
Self-organizing maps (SOMs) were described by Teuvo Kohonen in 1982, and convolutional neural networks (CNNs) were introduced by Kunihiko Fukushima in 1980.
The backpropagation algorithm is an efficient application of the Leibniz chain rule to networks of differentiable nodes.
Long short-term memory (LSTM) is a deep learning method that uses recurrent residual connections to learn very deep learning tasks with long credit assignment paths.
ANNs have achieved human-competitive/superhuman performance on benchmarks such as traffic sign recognition and image recognition contests.
ANNs are composed of artificial neurons that take in data and perform specific operations and tasks on the data, with each link between neurons having a weight determining the strength of one node's influence on another.Neural Network Summary
Neurons take inputs, weight them, apply a bias, pass them through an activation function, and produce an output.
Neurons are organized into layers, including input, output, and hidden layers, and can have different connection patterns.
Hyperparameters are set before the learning process begins and include learning rate, number of hidden layers, and batch size.
Learning involves adjusting weights to improve accuracy by minimizing observed errors.
Learning paradigms include supervised learning, unsupervised learning, and reinforcement learning.
Supervised learning uses paired inputs and desired outputs, while unsupervised learning uses input data and a cost function.
Reinforcement learning aims to minimize long-term cost by weighting the network to perform actions.
Backpropagation is used to adjust connection weights to compensate for each error found during learning.
The learning rate defines the size of corrective steps taken to adjust for errors in each observation.
A cost function is used to evaluate how well the network is performing and can be defined ad hoc or based on desirable properties or the model.
Optimization is used to minimize the cost function and improve model performance.
Learning can be done via stochastic gradient descent or other methods, such as extreme learning machines, "no-prop" networks, training without backtracking, "weightless" networks, and non-connectionist neural networks.Overview of Artificial Neural Networks (ANNs)
ANNs model nonlinear processes and have found applications in various fields such as system identification and control, pattern recognition, medical diagnosis, finance, cybersecurity, and physics.
ANNs use probability distributions for instantaneous cost, observation, and transition, with a policy defined as the conditional distribution over actions given observations.
ANNs can learn through dynamic programming and neurodynamic programming, with self-learning neural networks capable of learning a goal-seeking behavior in a behavioral environment.
Neuroevolution creates neural network topologies and weights using evolutionary computation, while stochastic neural networks introduce random variations to optimize problems.
ANNs have evolved into various types, with convolutional neural networks for visual processing, long short-term memory for speech recognition and photo-realistic talking heads, and generative adversarial networks for competition in tasks.
Neural architecture search automates ANN design, with various approaches such as AutoML and AutoKeras.
ANNs require defining network layers, size, and connection type, as well as hyperparameters such as learning rate, stride, and receptive field.
ANNs have theoretical properties such as the ability to model any function, capacity to store information, and convergence on a single solution depending on cost function, optimization method, and data or parameters.Criticism and challenges of artificial neural networks
The convergence behavior of certain types of ANN architectures are more understood than others.
ANNs often fit target functions from low to high frequencies, which is referred to as the spectral bias or frequency principle.
Over-training arises in convoluted or over-specified systems when the network capacity significantly exceeds the needed free parameters.
Two approaches to address over-training are cross-validation and regularization.
Supervised neural networks that use a mean squared error cost function can use formal statistical methods to determine the confidence of the trained model.
A common criticism of neural networks, particularly in robotics, is that they require too much training for real-world operation.
Neural networks embody new and powerful general principles for processing information, but these principles are ill-defined.
Large and effective neural networks require considerable computing resources.
Advances in hardware have made the standard backpropagation algorithm feasible for training networks that are several layers deeper than before.
Analyzing what has been learned by an ANN is much easier than analyzing what has been learned by a biological neural network.
Hybrid models combining neural networks and symbolic approaches can better capture the mechanisms of the human mind.
Neuromorphic engineering or a physical neural network addresses the hardware difficulty directly, by constructing non-von-Neumann chips to directly implement neural networks in circuitry.