Deep Learning and Neural Networks

Deep Learning is a transformative area of machine learning that employs artificial neural networks with multiple layers to identify complex data patterns. The progression from basic perceptron models to advanced structures like CNNs and RNNs showcases the capabilities of deep learning in various applications, including computer vision and natural language processing. Understanding these concepts equips individuals to leverage deep learning in developing sophisticated AI solutions.

Sections

  • 7

    Deep Learning And Neural Networks

    This section introduces deep learning as a subfield of machine learning, elaborating on neural networks and their architectures, including the perceptron, multi-layer networks, CNNs, and RNNs.

    Learning Practice

  • 7.1

    Introduction To Deep Learning

    Deep Learning is a subfield of machine learning utilizing deep neural networks to model complex data patterns effectively.

    Learning Practice

  • 7.1.1

    Why Deep Learning?

    Deep Learning utilizes multi-layered artificial neural networks to automatically extract features and model complex data patterns.

    Learning Practice

  • 7.2

    From Perceptron To Multi-Layer Neural Networks

    This section explores the evolution from the basic Perceptron model to more complex Multi-Layer Neural Networks, which are capable of solving intricate, non-linear problems.

    Learning Practice

  • 7.2.1

    The Perceptron

    The Perceptron is a foundational type of neural network that consists of a single neuron, capable of making binary decisions based on weighted inputs.

    Learning Practice

  • 7.2.2

    Multi-Layer Neural Networks

    Multi-layer Neural Networks, or MLPs, are neural networks composed of multiple layers that allow for the modeling of complex, non-linear patterns in data.

    Learning Practice

  • 7.3

    Backpropagation And Activation Functions

    This section explains the backpropagation algorithm used for training multi-layer neural networks and introduces various activation functions that enable networks to learn complex mappings.

    Learning Practice

  • 7.3.1

    Backpropagation Algorithm

    The backpropagation algorithm is essential for training multi-layer neural networks by minimizing the output loss through gradient descent.

    Learning Practice

  • 7.3.2

    Activation Functions

    Activation functions are essential components in neural networks, introducing non-linearity that enables them to learn complex data patterns.

    Learning Practice

  • 7.4

    Introduction To Cnns And Rnns

    This section introduces Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) for sequential data.

    Learning Practice

  • 7.4.1

    Convolutional Neural Networks (Cnns)

    Convolutional Neural Networks (CNNs) are specialized neural networks designed for processing grid-like data, typically used in image analysis.

    Learning Practice

  • 7.4.2

    Recurrent Neural Networks (Rnns)

    Recurrent Neural Networks (RNNs) are designed to process sequential data, utilizing hidden states to maintain information from previous time steps.

    Learning Practice

References

Chapter 7_ Deep Learning and Neural Networks.pdf

Class Notes

Memorization

What we have learnt

  • Deep Learning utilizes mult...
  • Multi-layer Perceptrons enh...
  • Backpropagation serves as t...

Final Test

Revision Tests

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