Multilayer Perceptron (MLP)
The fundamental building block of deep learning networks
What is a Multilayer Perceptron?
A class of feedforward artificial neural network with multiple layers
A Multilayer Perceptron (MLP) is a class of feedforward artificial neural network that consists of at least three layers of nodes: an input layer, one or more hidden layers, and an output layer. Each node in one layer connects with a certain weight to every node in the following layer. MLPs are fully connected networks where each neuron in one layer is connected to all neurons in the next layer.
MLPs use a supervised learning technique called backpropagation for training. The key characteristic that gives neural networks their power is the ability to learn non-linear relationships through activation functions applied at each neuron.
Key Concepts in MLPs
- Neurons & Layers: The basic computational units (neurons) are organized into layers. The input layer receives the data, hidden layers perform computations, and the output layer produces the final result.
- Activation Functions: Non-linear functions like ReLU, sigmoid, or tanh that introduce non-linearity into the network, allowing it to learn complex patterns.
- Backpropagation: The algorithm used to train MLPs, which calculates the gradient of the loss function with respect to the weights and biases.
- Weight Initialization: The process of setting initial values for the network weights, which is crucial for proper training.
- Forward Pass: The process of computing the output of the network given an input by propagating values through the layers.
- Loss Function: A function that measures the difference between the network's predictions and the actual target values.
How MLPs Work
The MLP learning process follows these steps:
- Forward Propagation: Input data is fed through the network, with each neuron computing a weighted sum of its inputs, applying an activation function, and passing the result to the next layer.
- Loss Calculation: The difference between the network's output and the expected output is calculated using a loss function.
- Backward Propagation: The gradient of the loss function with respect to each weight is calculated, starting from the output layer and moving backward.
- Weight Update: The weights are updated using an optimization algorithm (like gradient descent) to minimize the loss function.
- Iteration: Steps 1-4 are repeated for multiple epochs until the network converges to a satisfactory solution.
Advantages and Limitations
Advantages
- Can learn non-linear relationships
- Adaptable to various problem types
- Robust to noisy data
- Capable of parallel processing
- Can model complex patterns
- Foundation for more complex neural networks
Limitations
- Prone to overfitting with small datasets
- Computationally intensive to train
- Requires careful hyperparameter tuning
- Black-box nature limits interpretability
- Sensitive to feature scaling
- May get stuck in local minima