What an MLP is

An MLP is a feed-forward neural network with:

• an input layer
• one or more hidden layers
• an output layer

  flowchart LR
  I[Input layer] --> H1[Hidden layer 1]
  H1 --> H2[Hidden layer 2]
  H2 --> O[Output layer]

Why hidden layers matter

Hidden layers + non-linear activations allow the model to learn:

• curves
• interactions
• complex decision boundaries

Typical uses

MLP works best on:

• tabular data (sometimes)
• small image/text tasks (but CNNs/Transformers are usually better)

A tiny Keras example (conceptual)

# Note: This requires TensorFlow/Keras installed.
from tensorflow import keras
from tensorflow.keras import layers
 
model = keras.Sequential([
    layers.Dense(64, activation="relu"),
    layers.Dense(64, activation="relu"),
    layers.Dense(1, activation="sigmoid"),
])

# Note: This requires TensorFlow/Keras installed.
from tensorflow import keras
from tensorflow.keras import layers
 
model = keras.Sequential([
    layers.Dense(64, activation="relu"),
    layers.Dense(64, activation="relu"),
    layers.Dense(1, activation="sigmoid"),
])

Mini-checkpoint

If you remove non-linear activations from all hidden layers, what happens?

• The whole network behaves like a linear model.