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Feedforward neural networks (FNN) Deep Learning - Part 1

Contributors

AvatarKaivan Kamali

Questions

Objectives

Requirements

last_modification Last modification: Jul 9, 2021

What is an artificial neural network?

Speaker Notes

What is an artificial neural network?


Artificial Neural Networks


Inspiration for neural networks

Sketch of a biological neuron and its components


Celebral cortex


Celebral cortex


Perceptron

Neurons forming the input and output layers of a single layer feedforward neural network


Learning in Perceptron


Limitations of Perceptron


Multi-layer FNN

Neurons forming the input, output, and hidden layers of a multi-layer feedforward neural network


Activation functions

Table showing the formula, graph, derivative, and range of common activation functions


Supervised learning


Classification problems

Three images illustrating binary, multiclass, and multilabel classifications and their label representation


Output layer

Output layer (Continued)


Loss/Cost functions


Cross Entropy Loss/Cost functions

Cross Entropy loss function

Cross Entropy cost function


Quadratic Loss/Cost functions

Quadratic loss function

Quadratic cost function


Backpropagation (BP) learning algorithm


Backpropagation error

Backpropagation error


Backpropagation formulas

Backpropagation formulas


Types of Gradient Descent


Vanishing gradient problem


Car purchase price prediction


For references, please see tutorial’s References section


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Getting Help

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Thank you!

This material is the result of a collaborative work. Thanks to the Galaxy Training Network and all the contributors! Galaxy Training Network This material is licensed under the Creative Commons Attribution 4.0 International License.