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Jai Ganesh

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Registered: 2005-06-28

Posts: 52,763

Neural Network

Neural Network

Gist

A neural network is a machine learning model inspired by the human brain, consisting of interconnected nodes (neurons) in layers that process data, learn patterns, and make decisions or predictions, powering applications like image recognition, NLP, and voice assistants. These adaptable systems learn from examples, adjusting connections (weights) to improve accuracy without explicit rule programming, mimicking how biological brains process information.

In neuroscience, a biological neural network is a physical structure found in brains and complex nervous systems – a population of nerve cells connected by synapses. In machine learning, an artificial neural network is a mathematical model used to approximate nonlinear functions.

Summary

In the context of biology, a neural network is a population of biological neurons chemically connected to each other by synapses. A given neuron can be connected to hundreds of thousands of synapses. Each neuron sends and receives electrochemical signals called action potentials to its connected neighbors. A neuron can serve an excitatory role, amplifying and propagating signals it receives, or an inhibitory role, suppressing signals instead.

Populations of interconnected neurons that are smaller than neural networks are called neural circuits. Very large interconnected networks are called large scale brain networks, and many of these together form brains and nervous systems.

Signals generated by neural networks in the brain eventually travel through the nervous system and across neuromuscular junctions to muscle cells, where they cause contraction and thereby motion.

Neural network (machine learning)

In machine learning, a neural network or neural net (NN), also called artificial neural network (ANN), is a computational model inspired by the structure and functions of biological neural networks.

A neural network consists of connected units or nodes called artificial neurons, which loosely model the neurons in the brain. Artificial neuron models that mimic biological neurons more closely have also been recently investigated and shown to significantly improve performance. These are connected by edges, which model the synapses in the 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 totality 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 two 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.

Details

Neural networks are machine learning models that mimic the complex functions of the human brain. These models consist of interconnected nodes or neurons that process data, learn patterns and enable tasks such as pattern recognition and decision-making.

Neural networks are capable of learning and identifying patterns directly from data without pre-defined rules. These networks are built from several key components:

* Neurons: The basic units that receive inputs, each neuron is governed by a threshold and an activation function.
* Connections: Links between neurons that carry information, regulated by weights and biases.
* Weights and Biases: These parameters determine the strength and influence of connections.
* Propagation Functions: Mechanisms that help process and transfer data across layers of neurons.
* Learning Rule: The method that adjusts weights and biases over time to improve accuracy.

Learning in neural networks follows a structured, three-stage process:

* Input Computation: Data is fed into the network.
* Output Generation: Based on the current parameters, the network generates an output.
* Iterative Refinement: The network refines its output by adjusting weights and biases, gradually improving its performance on diverse tasks.

In an adaptive learning environment:

* The neural network is exposed to a simulated scenario or dataset.
* Parameters such as weights and biases are updated in response to new data or conditions.
* With each adjustment, the network’s response evolves allowing it to adapt effectively to different tasks or environments.

Importance of Neural Networks

* Identify Complex Patterns: Recognize intricate structures and relationships in data; adapt to dynamic and changing environments.
* Learn from Data: Handle vast datasets efficiently; improve performance with experience and retraining.
* Drive Key Technologies: Power natural language processing (NLP); enable self-driving vehicles; support automated decision-making systems.
* Boost Efficiency: Streamline workflows and processes; enhance productivity across industries.
* Backbone of AI: Serve as the core driver of artificial intelligence progress; continue shaping the future of technology and innovation.

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It appears to me that if one wants to make progress in mathematics, one should study the masters and not the pupils. - Niels Henrik Abel.

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