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.