Neural Networks Introduction

Neural networks are layers of weighted sums + non-linear activations. During training, the model adjusts weights to reduce prediction error using gradient descent and backpropagation.

How a neural network learns

Neural networks are layers of weighted sums + non-linear activations. During training, the model adjusts weights to reduce prediction error using gradient descent and backpropagation.

Input layer -> hidden layers -> output layer.
Loss function measures how wrong predictions are.
Optimizer updates parameters iteratively.

Minimal dense network

Binary classifier in Keras

from tensorflow import keras

model = keras.Sequential([
    keras.layers.Dense(32, activation="relu", input_shape=(20,)),
    keras.layers.Dense(16, activation="relu"),
    keras.layers.Dense(1, activation="sigmoid"),
])

model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
model.fit(X_train, y_train, epochs=10, batch_size=32)