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Multilayer Perceptrons (MLP) - Coggle Diagram
Multilayer Perceptrons (MLP)
Architecture
Input Layer
Input Features
Data Input
Hidden Layer(s)
Neurons
Weights
Biases
Activation Function
ReLU
Sigmoid
Tanh
Softmax
Output Layer
Prediction
Classification
Regression
Data Processing
Data Collection
Data Cleaning
Missing Value Handling
Feature Selection
Feature Scaling
Normalization
Train-Test Split
Learning Process
Forward Propagation
Compute Weighted Sum
Apply Activation Function
Loss Function
Mean Squared Error (MSE)
Cross-Entropy Loss
Binary Cross-Entropy
Backpropagation
Error Calculation
Gradient Computation
Weight Update
Gradient Descent
Stochastic Gradient Descent (SGD)
Adam Optimizer
RMSProp
Training
Initialize Weights
Feed Input Data
Compute Output
Calculate Loss
Update Parameters
Repeat Epochs
Trained Model
Applications
Classification
Spam Detection
Sentiment Analysis
Regression
Price Prediction
Image Recognition
Speech Recognition
Medical Diagnosis
Fraud Detection
Pattern Recognition
Performance Evaluation
Accuracy
Precision
Recall
F1-Score
Confusion Matrix
ROC-AUC
Advantages
Learns Complex Patterns
Handles Non-linear Data
High Accuracy
Feature Learning
Flexible Architecture
Limitations
Overfitting
Requires Large Dataset
High Computational Cost
Long Training Time
Hyperparameter Tuning
Real-World Uses
Face Recognition
Recommendation Systems
Autonomous Vehicles
Healthcare
Finance
Robotics