Backpropagation in Neural Networks | Back Propagation Algorithm with Examples | Simplilearn

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This video covers What is Backpropagation in Neural Networks? Neural Network Tutorial for Beginners includes a definition of backpropagation, working of backpropagation, benefits of backpropagation, and applications.
00:00 - What is Backpropagation?
This phase contains the definition of backpropagation with diagrammatic representation.
01:41 - What is Backpropagation in neural networks?
This phase of the video has specifically explained the role of backpropagation in neural networks.
02:23 - How does Backpropagation in neural networks work?
This phase of the video explains functioning, activation, and loss function in simple words.
05:28 - Benefits of Backpropagation
This content highlights the importance of backpropagation and gives you a reason to choose the same.
05:54 - Applications of Backpropagation
This phase covers applications of backpropagation in different fields.
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Backpropagation is an algorithm that is created to test errors that will travel back from input nodes to output nodes. It is applied to improve accuracy in data mining and machine learning.The concept of backpropagation in neural networks was first introduced in the 1960s. An artificial neural network is made up of bunches of connected input/output units, each of which is connected by a software program and has a certain weight. This kind of network is based on biological neural networks, which contain neurons coupled to one another across different network levels. In this instance, neurons are shown as nodes.
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[@MrAhsan99]

add some visualizations not just a static frame of ANN.

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during the backpropagation process we use gradient descent to change the hyperparameters true or false

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