Neural Networks Intro

Let's Think About This for a Second

A Neural Network is an algorithm inspired by the way neurons connect in the human brain — it has a layer structure consisting of an Input Layer (where data enters), Hidden Layer(s) (the 'brain' layers that learn patterns, whose number/size can be tuned), and an Output Layer (the final prediction). Each Neuron combines input values using weights, passes the result through an Activation Function (ReLU, Sigmoid), and produces an output — 'Deep Learning' refers to a Neural Network with many (deep) Hidden Layers (the Large Language Models you'll see in this site's AI/LangChain tutorials are also based on Neural Network architecture).

Connecting to a Real Scenario

Writing `from sklearn.neural_network import MLPClassifier; model = MLPClassifier(hidden_layer_sizes=(100, 50), max_iter=500); model.fit(X_train, y_train)` trains a simple neural network with 2 Hidden Layers (100 neurons, 50 neurons) — scikit-learn's `MLPClassifier` is meant for basic neural networks, while dedicated frameworks like TensorFlow/PyTorch are typically used in production for image/text/large-scale deep learning.

Neural Network = an algorithm made up of an Input Layer, Hidden Layer(s), and an Output Layer
Neuron = a unit that combines inputs with weights and passes them through an Activation Function
Deep Learning = a Neural Network with many (deep) Hidden Layers

Let's Look at It Together

python

from sklearn.neural_network import MLPClassifier

model = MLPClassifier(
    hidden_layer_sizes=(100, 50),
    activation='relu',
    max_iter=500,
    random_state=42
)
model.fit(X_train, y_train)

accuracy = model.score(X_test, y_test)
print(f"Neural Network accuracy: {accuracy:.2f}")

You should see

Neural Network accuracy: 0.90

Try It in 5 Minutes

Try different `hidden_layer_sizes` values for `MLPClassifier` (e.g. (10,), (100, 50), (200, 100, 50)), and compare accuracy/training time.

A Quick Word of Caution

A Neural Network's interpretability is much lower than a Decision Tree's (it's often called a 'black box') — if interpretability matters for a regulation-heavy or high-stakes decision (loans, medical), you should factor in this trade-off.