Neural networks are at the heart of deep learning — inspired by how the human brain works.
📌 What is a Neural Network?
A neural network is a set of connected layers that learn patterns from data.
Structure of a Basic Neural Network:
1️⃣ Input Layer – Takes raw features (like pixels, numbers, words)
2️⃣ Hidden Layers – Learn patterns through weighted connections
3️⃣ Output Layer – Gives predictions (like class labels or values)
📘 Key Concepts
1. Neuron (Node)
Each node receives inputs, multiplies them with weights, adds bias, and passes the result through an activation function.
output = activation(w1x1 + w2x2 + ... + b)2. Activation Functions
They introduce non-linearity — essential for learning complex data.
Popular ones:
• ReLU – Most common
• Sigmoid – Good for binary output
• Tanh – Range between -1 to 1
3. Forward Propagation
Data flows from input → hidden layers → output. Each layer transforms the data using learned weights.
4. Loss Function
Measures how far the prediction is from the actual result.
Example: Mean Squared Error, Cross Entropy
5. Backpropagation + Gradient Descent
The network adjusts weights to minimize the loss using derivatives. This is how it learns from mistakes.
📌 Example with Keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
model = Sequential()
model.add(Dense(64, activation='relu', input_shape=(10,)))
model.add(Dense(1, activation='sigmoid'))
➡️ 10 inputs → 64 hidden units → 1 output (binary classification)
🎯 Why It Matters
Neural networks power modern AI:
• Face recognition
• Spam filters
• Chatbots
• Language translation
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