CBSE Class 11th AI (Artificial Intelligence) | 8. Neural Network by Abraham | Learn Smarter
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8. Neural Network

Neural networks are advanced computational models that replicate the functioning of the human brain, enabling machines to learn and make decisions. The chapter covers their structure, including input, hidden, and output layers, as well as various types of neural networks such as feedforward and convolutional networks. Additionally, it discusses their applications across multiple domains and highlights challenges like data dependency and interpretability. Despite these limitations, neural networks prove to be powerful tools in artificial intelligence.

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Sections

  • 8

    Neural Network

    Neural networks mimic the human brain's learning process and are integral to modern artificial intelligence.

    Learning Practice

  • 8.1

    Biological Vs Artificial Neural Network

    This section compares biological neural networks (BNNs) with artificial neural networks (ANNs), highlighting their structural and functional similarities and differences.

    Learning Practice

  • 8.1.1

    Biological Neural Network (Bnn)

    The Biological Neural Network (BNN) explores the structure and function of the human brain's neurons and their communication.

    Learning Practice

  • 8.1.2

    Artificial Neural Network (Ann)

    Artificial Neural Networks (ANNs) are mathematical models inspired by biological neural networks, consisting of interconnected nodes that process inputs to simulate human learning.

    Learning Practice

  • 8.2

    Structure Of An Artificial Neural Network

    An Artificial Neural Network (ANN) consists of layers that process data, including an input layer, one or more hidden layers, and an output layer.

    Learning Practice

  • 8.2.1

    Input Layer

    The Input Layer in an Artificial Neural Network (ANN) is responsible for accepting raw data and features for processing.

    Learning Practice

  • 8.2.2

    Hidden Layer(S)

    Hidden layers in an artificial neural network perform intermediate computations and pattern extraction from data between the input and output layers.

    Learning Practice

  • 8.2.3

    Output Layer

    The output layer in an artificial neural network produces the final result of the data processing.

    Learning Practice

  • 8.3

    Components Of A Neuron (Perceptron)

    This section introduces the structure and functioning of a single neuron, known as a perceptron, focusing on its inputs, weights, summation function, and activation function.

    Learning Practice

  • 8.4

    Types Of Neural Networks

    This section introduces different types of neural networks, including Feedforward Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks.

    Learning Practice

  • 8.4.1

    Feedforward Neural Network

    Feedforward Neural Networks are a type of artificial neural networks where information flows solely in one direction, from input to output, without any cycles.

    Learning Practice

  • 8.4.2

    Convolutional Neural Network (Cnn)

    Convolutional Neural Networks (CNNs) are specialized neural networks primarily used for processing and analyzing images.

    Learning Practice

  • 8.4.3

    Recurrent Neural Network (Rnn)

    Recurrent Neural Networks (RNNs) are designed to process sequential data while maintaining memory of previous inputs.

    Learning Practice

  • 8.5

    Applications Of Neural Networks

    Neural networks are applied in various fields such as image recognition, natural language processing, healthcare, finance, and self-driving cars.

    Learning Practice

  • 8.6

    Learning Process In Neural Networks

    The learning process in neural networks involves forward propagation, loss functions, and backpropagation.

    Learning Practice

  • 8.6.1

    Forward Propagation

    Forward propagation is the process of passing inputs through the layers of a neural network to generate predictions.

    Learning Practice

  • 8.6.2

    Loss Function

    The loss function measures the difference between predicted and actual outputs in neural networks.

    Learning Practice

  • 8.6.3

    Backpropagation

    Backpropagation is a crucial method in neural networks for adjusting weights to minimize prediction error during training.

    Learning Practice

  • 8.7

    Limitations Of Neural Networks

    This section outlines the key limitations faced by neural networks, including their data requirements, computational costs, interpretability issues, and risks of overfitting.

    Learning Practice

  • 8.8

    Key Terms

    This section outlines essential terms and definitions related to neural networks, crucial for understanding their functioning.

    Learning Practice

References

Chapter_8_Neural.pdf

Class Notes

Memorization

What we have learnt

  • Neural networks emulate hum...
  • The architecture of artific...
  • Neural networks are applica...

Final Test

Revision Tests