Multilayer Perceptron (MLP) Neural Network Architecture: A Comprehensive Overview
Slides Outline:
I. Introduction to Multilayer Perceptrons
A. Definition and historical context
B. Applications of MLPs in various domains
II. Basic Structure and Functionality of an MLP
A. Input layer
1. Receiving and encoding input data
2. Importance of feature engineering
B. Hidden layers
1. Role of hidden layers in learning complex representations
2. Depth and breadth of hidden layers
3. Activation functions (e.g., sigmoid, ReLU, tanh)
C. Output layer
1. Producing the final predictions or outputs
2. Activation function selection based on problem type (e.g., classification, regression)
III. The Forward Propagation Process
A. Mathematical formulation of the forward pass
B. Weighted sum and activation function application
C. Propagation of information through the network layers
D. Importance of layer-wise computations
IV. The Backpropagation Algorithm for Training
A. Overview of the backpropagation algorithm
B. Calculation of gradients through the chain rule
C. Error backpropagation from output to input layers
D. Updating model parameters (weights and biases)
E. Convergence and optimization techniques (e.g., gradient descent, momentum, AdaGrad)
V. Activation Functions
A. Sigmoid function
B. Hyperbolic tangent (tanh) function
C. Rectified Linear Unit (ReLU) function
D. Leaky ReLU and other variants
E. Selecting appropriate activation functions for different problems
VI. Regularization Techniques
A. L1 and L2 regularization
B. Dropout
C. Early stopping
D. Batch normalization
E. Choosing the right regularization method for your MLP
VII. Practical Considerations for MLP Implementation
A. Data preprocessing and normalization
B. Hyperparameter tuning (e.g., learning rate, batch size, number of layers, units per layer)
C. Overfitting and underfitting: detection and mitigation
D. Evaluation metrics and performance analysis
E. Deployment and real-world applications
VIII. Advanced Topics (optional)
A. Ensemble methods with MLPs (e.g., bagging, boosting)
B. Convolutional layers and CNNs
C. Recurrent layers and RNNs
D. Transfer learning and fine-tuning
E. Interpretability and explainability of MLP models
IX. Conclusion
A. Summary of key MLP concepts and techniques
B. Potential future developments and research directions
C. Resources for further learning and exploration
The goal of these slides is to provide a comprehensive and accessible overview of the multilayer perceptron neural network architecture. The presentation starts with an introduction to MLPs, their definition, and various applications. It then delves into the basic structure and functionality of an MLP, explaining the roles of the input layer, hidden layers, and output layer, as well as the importance of activation functions.
The slides then dive deeper into the inner workings of the MLP, covering the forward propagation process and the backpropagation algorithm for training the network.
13. Artificial neural network
Mathematical / computational model that tries to
simulate the structure and/or functional aspects of
biological neural networks
Such networks can be used to learn complex functions
from examples.
4/7/2024
Introduction to Deep Learning
13
14. Artificial Neural Network
area
rooms
yp= w1*area +
w2*rooms + b
b
w1
w2
Area in Sq. Mts Number of Rooms Is it a family
house?
f(Yp)
4/7/2024
Introduction to Deep Learning
14
15. Artificial Neural Network
area
rooms
yp= w1*area +
w2*rooms + b
b
w1
w2
f(Yp)
Learnable
Parameters
Learnable
Parameters
Area in Sq. Mts Number of Rooms Is it a family
house?
4/7/2024
Introduction to Deep Learning
15
16. Artificial Neural Network
area
rooms
yp= w1*area +
w2*rooms + b
b
w1
w2
f(Yp)
Learnable
Parameters
Activation
function
Area in Sq. Mts Number of Rooms Is it a family
house?
Learnable
Parameters
4/7/2024
Introduction to Deep Learning
16
17. Artificial Neural Network
area
rooms
yp= w1*area +
w2*rooms + b
b
w1
w2
f(Yp)
Learnable
Parameters
Activation
function
Learn from
examples..
Area in Sq. Mts Number of Rooms Is it a family
house?
700 2 No
900 1 Yes
1200 1 yes
Learnable
Parameters
4/7/2024
Introduction to Deep Learning
17
18. Artificial Neural Network
area
rooms
yp= w1*area +
w2*rooms + b
b
w1
w2
Yp’=f(Yp)
Learnable
Parameters
Activation
function
Area in Sq. Mts Number of Rooms Is it a family
house?
700 2 No
900 1 Yes
1200 1 yes Learn from
examples..
Loss(Y, Yp’)
A loss
function
Learnable
Parameters
4/7/2024
Introduction to Deep Learning
18
19. Artificial Neural Network
area
rooms
yp= w1*area +
w2*rooms + b
b
w1
w2
f(Yp)
Learnable
Parameters
Activation
function
Area in Sq. Mts Number of Rooms Is it a family
house? (Y)
700 2 No (0)
900 1 Yes (1)
1200 1 Yes (1) Learn from
examples..
Loss(Y, Yp)
A loss
function
Learnable
Parameters
Error Back propagation
4/7/2024
Introduction to Deep Learning
19
21. Artificial Neural Network
area
rooms
yp= w1*area +
w2*rooms + b
b
w1
w2
f(Yp)
Learnable
Parameters
Activation
function
Learn from
examples..
Loss(Y, Yp)
A loss
function
Learnable Parameters –
Adjust the weights
Error Back propagation
4/7/2024
Introduction to Deep Learning
21
Area in Sq. Mts Number of Rooms Is it a family
house?
700 2 No
900 1 Yes
1200 1 yes
22. Computation graph and gradient
calculation
4/7/2024
Introduction to Deep Learning
22
23. Computation graph and gradient calculation
23
Introduction to Deep Learning 4/7/2024
24. Computation graph and gradient calculation
24
Introduction to Deep Learning 4/7/2024
