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Neural network (machine learning)

1. Neural Network Fundamentals: – Neural networks are trained through empirical risk minimization. – Gradient-based methods like backpropagation are commonly used for parameter estimation. – […]

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1. Neural Network Fundamentals:

– Neural networks are trained through empirical risk minimization.
– Gradient-based methods like backpropagation are commonly used for parameter estimation.
– During training, ANNs learn from labeled data by updating parameters to minimize a defined loss function.
– Artificial neural networks consist of artificial neurons connected by edges.
– Neurons process signals using activation functions.
– Weights on neurons and edges adjust during learning.
– Neurons are organized into layers, including input, output, and hidden layers.
– ANNs aim to mimic the human brain’s architecture.
– Neurons in ANNs form connections to model complex relationships.
– ANNs use directed, weighted graphs for data processing.
– Artificial neural networks consist of simulated neurons with weighted connections.
– Neurons are structured in layers, especially in deep learning.
– Layers include input, hidden, and output layers.
– Neurons in one layer connect to adjacent layers.

2. History and Development:

– Digital computers evolved from the von Neumann model, while neural networks originated from connectionism.
– The simplest feedforward neural network is a linear network with a single layer of output nodes.
– Hebbian learning, a form of unsupervised learning, was introduced in the late 1940s.
– The perceptron, the first implemented artificial neural network, was invented by Frank Rosenblatt in 1958.
– Backpropagation algorithm introduced by Paul Werbos in 1982.
– Interest in the Ising model for neural networks in the late 1970s to early 1980s.
– Hopfield network popularized by John Hopfield in 1982.
– Time delay neural network (TDNN) combined convolutions and backpropagation in 1987.
– LeNet-5, a 7-level CNN by Yann LeCun in 1998.
– Neural networks successfully predicted the stock market and developed a self-driving car in 1995.
– Deep learning method LSTM introduced in 1997.
– Protein structure prediction transformed by neural networks from 1988 onward.
– Vanishing gradient problem identified in 1991.
– Adversarial neural networks proposed by Juergen Schmidhuber in 1991.
– RNN hierarchy with self-supervised learning proposed in 1992.
– Linear Transformer concept introduced by Juergen Schmidhuber in 1992.

3. Applications and Impact:

– Neural networks are used for predictive modeling and adaptive control.
– They can be trained via datasets to solve problems in artificial intelligence.
– Networks can learn from experience and derive conclusions from complex information.
– They are applied in various fields for tasks like image recognition and natural language processing.
– Applications include speech recognition, medical diagnosis, and autonomous vehicles.
– Neural networks potentially ushering in a new era beyond digital computing.
– Success of deep learning in image and visual recognition problems.
– Unsupervised pre-training and increased computing power enabled deep learning.

4. Learning and Optimization:

– Learning in neural networks involves adjusting weights for better task performance.
– The goal is to minimize errors through observations.
– Learning continues until error rate reduction plateaus.
– Cost functions are used to evaluate learning progress.
– Backpropagation is used to adjust connection weights to compensate for errors.
– Learning paradigms are separated into supervised, unsupervised, and reinforcement learning.
– Hyperparameters are set constants before the learning process.
– Learning Rate determines the size of corrective steps in model adjustments.
– Cost Function aids in minimizing errors and optimizing model performance.
– Self-learning involves updating memory matrix based on actions and consequences.

5. Technological Advancements and Models:

– Metal–oxide–semiconductor (MOS) VLSI technology enabled neural network development in the 1980s.
– CMOS technology provided more processing power for neural networks.
– GPUs and distributed computing increased computing power for deep learning.
– Neuromorphic computing and nanodevices show promise for neural computing.
– Neuroevolution utilizes evolutionary computation to create neural network topologies and weights.
– Stochastic neural networks introduce random variations for optimization.
– Different learning algorithms like evolutionary methods and simulated annealing.
– ANNs shifted focus to improving results rather than mimicking biology.
– Types of Artificial Neural Networks include static and dynamic components, supervised and unsupervised learning, hardware and software implementations, and evolution via learning for better results.
– Network Design involves understanding characteristics, data representation, learning algorithm selection, and automating design through neural architecture search.

Neural network (machine learning) (Wikipedia)

In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a model inspired by the structure and function of biological neural networks in animal brains.

An artificial neural network is an interconnected group of nodes, inspired by a simplification of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another.

An ANN consists of connected units or nodes called artificial neurons, which loosely model the neurons in a brain. These are connected by edges, which model the synapses in a brain. Each artificial neuron receives signals from connected neurons, then processes them and sends a signal to other connected neurons. The "signal" is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs, called the activation function. The strength of the signal at each connection is determined by a weight, which adjusts during the learning process.

Typically, neurons are aggregated into layers. Different layers may perform different transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly passing through multiple intermediate layers (hidden layers). A network is typically called a deep neural network if it has at least 2 hidden layers.

Artificial neural networks are used for various tasks, including predictive modeling, adaptive control, and solving problems in artificial intelligence. They can learn from experience, and can derive conclusions from a complex and seemingly unrelated set of information.

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