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Multi-Layer Perceptron Learning Feed Forward Learning Back Propagation Algorithm by Mahesh Huddar
Mahesh Huddar
Overview
This video explains the Multi-Layer Perceptron (MLP) network, a type of feedforward artificial neural network, and its learning algorithm. An MLP consists of at least three layers: an input layer, one or more hidden layers, and an output layer. Neurons in each layer are fully connected to neurons in the subsequent layer. The video details the forward propagation process, where input data is processed through the layers to produce an output, and the backpropagation algorithm, used to calculate and propagate errors backward through the network. It covers how to compute errors at both the output and hidden layers and subsequently update the weights and biases using a defined learning rate to minimize these errors. The explanation includes the mathematical formulas for these calculations, emphasizing the role of activation functions like sigmoid.
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Chapters
- •MLP is a feedforward neural network with multiple layers.
- •It requires at least three layers: input, hidden, and output.
- •Neurons in adjacent layers are fully connected.
- •Input layer passes data, hidden layers process, output layer produces results.
- •Input layer: Receives input and transforms it to the next layer without modification.
- •Hidden layers: Receive input from the previous layer, perform computations, and pass output to the next layer.
- •Output layer: Receives input from the last hidden layer and outputs the final result to the environment.
- •Activation functions are used in hidden and output layer neurons.
- •Common types include linear and non-linear functions.
- •Sigmoid is often used for binary classification.
- •Softmax is typically used for multi-class classification.
- •Input: Input vector (features) and target output.
- •Learning rate (alpha) must be defined.
- •Weights and biases for connections are typically initialized randomly between -5 and +5.
- •Calculate net input and output for each neuron, starting from the input layer.
- •Input layer output is a direct transfer of input values.
- •Subsequent layers use activation functions (e.g., sigmoid) to compute outputs based on weighted inputs.
- •Error is calculated as the difference between target output and estimated (calculated) output.
- •Error = Target Output - Estimated Output.
- •Propagates the calculated error backward from the output layer to the hidden layers.
- •Error at hidden layers is a proportion of the output layer error, weighted by connection weights.
- •Specific formulas are used to calculate errors for output layer neurons and hidden layer neurons.
- •Update weights using the formula: Delta W = learning rate * error * input.
- •New weight = Previous weight + Delta W.
- •Update biases using the formula: Delta Theta J = learning rate * error (bias input is considered 1).
- •New bias = Previous bias + Delta Theta J.
Key Takeaways
- 1MLPs are versatile feedforward networks with layered structures.
- 2Fully connected layers allow complex data transformations.
- 3Activation functions are crucial for introducing non-linearity and enabling learning.
- 4Forward propagation calculates the network's output for a given input.
- 5Backpropagation is the core algorithm for learning by adjusting weights based on error.
- 6Error calculation involves comparing predicted output with the target.
- 7Weights and biases are iteratively updated to minimize the error.
- 8Learning rate controls the step size during weight and bias updates.