Artificial Neural Network for Machine Learning and Deep Learning

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BisweswarThakur1

Artificial Neural Network

Contd.
• The term "Artificial Neural Network" is derived from Biological neural
networks that develop the structure of a human brain. Similar to the
human brain that has neurons interconnected to one another,
artificial neural networks also have neurons that are interconnected
to one another in various layers of the networks. These neurons are
known as nodes.

The given figure illustrates the typical diagram of Biological Neural Network.

The typical Artificial Neural Network looks something like the given
figure.

Relationship between Biological neural network and
artificial neural network:
Biological Neural Network Artificial Neural Network
Dendrites Inputs
Cell nucleus Nodes
Synapse Weights
Axon Output

The architecture of an artificial neural network:
• In order to define a neural network that consists of a large number of
artificial neurons, which are termed units arranged in a sequence of
layers. Lets us look at various types of layers available in an artificial
neural network.

Artificial Neural Network for Machine Learning and Deep Learning

Layer in ANN
• Input Layer:
• As the name suggests, it accepts inputs in several different formats
provided by the programmer.
• Hidden Layer:
• The hidden layer presents in-between input and output layers. It
performs all the calculations to find hidden features and patterns.

Contd.
• Output Layer:
• The input goes through a series of transformations using the hidden layer, which
finally results in output that is conveyed using this layer.
• The artificial neural network takes input and computes the weighted sum of the
inputs and includes a bias. This computation is represented in the form of a
transfer function.
It determines weighted total is passed as an input to an activation function to
produce the output.

The document provides an introduction to artificial neural networks. It discusses biological neurons and how artificial neurons are modeled. The key aspects covered are: - Artificial neural networks (ANNs) are modeled after biological neural systems and are comprised of basic units (nodes/neurons) connected by links with weights. - ANNs learn by adjusting the weights of connections between nodes through training algorithms like backpropagation. This allows the network to continually learn from examples. - The network is organized into layers with connections only between adjacent layers in a feedforward network. Backpropagation is used to calculate weight adjustments to minimize error between actual and expected outputs. - Learning can be supervised, using examples of inputs and outputs, or

ANN.pptx bgyikkl jyrf hfuk kiyfvj jiyfv kuyfcv18X5F8NDeekshitha

neuralnetwork.pptxSherinRappai1

This document discusses artificial neural networks. It defines neural networks as computational models inspired by the human brain that are used for tasks like classification, clustering, and pattern recognition. The key points are: - Neural networks contain interconnected artificial neurons that can perform complex computations. They are inspired by biological neurons in the brain. - Common neural network types are feedforward networks, where data flows from input to output, and recurrent networks, which contain feedback loops. - Neural networks are trained using algorithms like backpropagation that minimize error by adjusting synaptic weights between neurons. - Neural networks have many applications including voice recognition, image recognition, robotics and more due to their ability to learn from large amounts of data.

neuralnetwork.pptxSherinRappai

This document discusses artificial neural networks. It defines neural networks as computational models inspired by the human brain that are used for tasks like classification, clustering, and pattern recognition. The key points are: - Neural networks contain interconnected artificial neurons that can perform complex computations. They are inspired by biological neurons in the brain. - Common neural network types are feedforward networks, where data flows from input to output, and recurrent networks, which contain feedback loops. - Neural networks are trained using algorithms like backpropagation that minimize error by adjusting synaptic weights between neurons. - Neural networks have various applications including voice recognition, image recognition, and robotics due to their ability to learn from large amounts of data.

Artificial neural network by arpit_sharmaEr. Arpit Sharma

This document provides an introduction to artificial neural networks. It discusses biological neurons and how artificial neurons are modeled. The key components of a neural network including the network architecture, learning approaches, and the backpropagation algorithm for supervised learning are described. Applications and advantages of neural networks are also mentioned. Neural networks are modeled after the human brain and learn by modifying connection weights between nodes based on examples.

Basics of Artificial Neural Network Subham Preetam

This document provides an overview of artificial neural networks. It discusses the biological neuron model that inspired artificial neural networks. The key components of an artificial neuron are inputs, weights, summation, and an activation function. Neural networks have an interconnected architecture with layers of nodes. Learning involves modifying the weights through algorithms like backpropagation to minimize error. Neural networks can perform supervised or unsupervised learning. Their advantages include handling complex nonlinear problems, learning from data, and adapting to new situations.

Neural NetworkAshish Kumar

Neural networks are computing systems inspired by biological neural networks. They are composed of interconnected nodes that process input data and transmit signals to each other. The document discusses various types of neural networks including feedforward, recurrent, convolutional, and modular neural networks. It also describes the basic architecture of neural networks including input, hidden, and output layers. Neural networks can be used for applications like pattern recognition, data classification, and more. They are well-suited for complex, nonlinear problems. The document provides an overview of neural networks and their functioning.

Artificial neural networkDEEPASHRI HK

- The document introduces artificial neural networks, which aim to mimic the structure and functions of the human brain. - It describes the basic components of artificial neurons and how they are modeled after biological neurons. It also explains different types of neural network architectures. - The document discusses supervised and unsupervised learning in neural networks. It provides details on the backpropagation algorithm, a commonly used method for training multilayer feedforward neural networks using gradient descent.

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Neural networks are programs that mimic the human brain by learning from large amounts of data. They use simulated neurons that are connected together to form networks, similar to the human nervous system. Neural networks learn by adjusting the strengths of connections between neurons, and can be used to perform tasks like pattern recognition or prediction. Common neural network training algorithms include gradient descent and backpropagation, which help minimize errors by adjusting connection weights.

Artificial neural networksAkashRanjandas1

Artificial neural networks (ANNs) are computing systems inspired by biological neural networks. ANNs consist of interconnected nodes that mimic neurons and pass signals to each other via weighted connections. There are two main reasons for building ANNs: to solve problems like pattern recognition that require massively parallel processing, and to better understand natural information processing in the brain. ANNs process information in parallel through a large number of simple nodes. The output of each node is determined by the inputs it receives and the weights assigned to those connections. ANNs can be used for applications like pattern recognition, control systems, and forecasting.

Neural networksBasil John

The document provides an overview of artificial neural networks (ANNs). It discusses the history of ANNs, how they work by mimicking biological neurons, different learning paradigms like supervised and unsupervised learning, and applications. Key points include: ANNs consist of interconnected artificial neurons that receive inputs, change their activation based on weights, and send outputs; backpropagation is used for supervised learning to minimize errors by adjusting weights from the output layer backwards; ANNs can be used for problems like pattern recognition, prediction, and data processing.

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The document provides an overview of artificial neural networks (ANNs). It discusses how ANNs were inspired by biological neural networks and how each artificial neuron works similarly to a biological neuron by receiving input from other neurons, changing its internal activation based on that input, and sending output signals to other neurons. The document also explains that backpropagation is a learning algorithm used in ANNs where the error is calculated at the output and distributed back through the network to adjust weights between neurons in order to minimize error.

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The document provides an overview of artificial neural networks and biological neural networks. It discusses the components and functions of the human nervous system including the central nervous system made up of the brain and spinal cord, as well as the peripheral nervous system. The four main parts of the brain - cerebrum, cerebellum, diencephalon, and brainstem - are described along with their roles in processing sensory information and controlling bodily functions. A brief history of artificial neural networks is also presented.

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This document provides an overview of artificial neural networks. It discusses the biological inspiration from neurons in the brain and how artificial neural networks mimic this structure. The key components of artificial neurons and various network architectures are described, including fully connected, layered, feedforward, and modular networks. Supervised and unsupervised learning approaches are covered, with backpropagation highlighted as a commonly used supervised algorithm. Applications of neural networks are mentioned in areas like medicine, business, marketing and credit evaluation. Advantages include the ability to handle complex nonlinear problems and noisy data.

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Artificial neural networks (ANNs) operate by simulating the human brain. ANNs consist of interconnected artificial neurons that receive inputs, change their internal activation based on weights, and send outputs. Backpropagation is a learning algorithm used in ANNs where the error is calculated and distributed back through the network to adjust the weights, minimizing errors between predicted and actual outputs.

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This document discusses neural networks from biological and artificial perspectives. Biologically, neurons are cells in the brain that transmit electrochemical signals between each other via connections called axons. Artificially, neural networks are modeled after biological neural connections and use units and weighted connections. The document also describes the STATISTICA Neural Networks program for creating and training neural networks. It allows designing networks, importing and analyzing data, choosing network types, setting activation functions, and creating applications using neural network APIs.

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An artificial neural network (ANN) is a machine learning approach that models the human brain. It consists of artificial neurons that are connected in a network. Each neuron receives inputs, performs calculations, and outputs a value. ANNs can be trained to learn patterns from data through examples to perform tasks like classification, prediction, clustering, and association. Common ANN architectures include multilayer perceptrons, convolutional neural networks, and recurrent neural networks.

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Artificial neural networks are computational models inspired by the human brain that are designed to recognize patterns. The document discusses key aspects of artificial neural networks including their graph-like structure consisting of nodes and connections, how data is represented, their ability to handle noise and provide fast outputs once trained, and challenges in interpreting their predictions. Applications mentioned include medicine, business, marketing, and credit evaluation.

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Artificial Neural Network for Machine Learning and Deep Learning

1. Artificial Neural Network

2. Introduction "Artificial Neural Networks: A Sub-Field of AI" • Biologically inspired sub-field of AI modeled after the human brain. • Computed network based on biological neural networks. • Similar to human brain, has interconnected neurons in various layers. • Nodes are linked to each other in the network.

3. Contd. • The term "Artificial Neural Network" is derived from Biological neural networks that develop the structure of a human brain. Similar to the human brain that has neurons interconnected to one another, artificial neural networks also have neurons that are interconnected to one another in various layers of the networks. These neurons are known as nodes.

4. The given figure illustrates the typical diagram of Biological Neural Network.

5. The typical Artificial Neural Network looks something like the given figure.

6. Relationship between Biological neural network and artificial neural network: Biological Neural Network Artificial Neural Network Dendrites Inputs Cell nucleus Nodes Synapse Weights Axon Output

7. The architecture of an artificial neural network: • In order to define a neural network that consists of a large number of artificial neurons, which are termed units arranged in a sequence of layers. Lets us look at various types of layers available in an artificial neural network.

9. Layer in ANN • Input Layer: • As the name suggests, it accepts inputs in several different formats provided by the programmer. • Hidden Layer: • The hidden layer presents in-between input and output layers. It performs all the calculations to find hidden features and patterns.

10. Contd. • Output Layer: • The input goes through a series of transformations using the hidden layer, which finally results in output that is conveyed using this layer. • The artificial neural network takes input and computes the weighted sum of the inputs and includes a bias. This computation is represented in the form of a transfer function. It determines weighted total is passed as an input to an activation function to produce the output.

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