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Feedforward neural networks (FNN) Deep Learning - Part 1


Authors: Kaivan Kamali avatar Kaivan Kamali




last_modification Published: Jun 2, 2021
last_modification Last Updated: 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


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

Screenshot of the gtn stats page with 21 topics, 170 tutorials, 159 contributors, 16 scientific topics, and a growing community

Speaker Notes

Getting Help

Speaker Notes

Join an event

Event schedule

Speaker Notes

Thank you!

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