Introduction to Neural Networks

Neural networks are at the heart of modern artificial intelligence. They're inspired by the structure of the human brain and have revolutionized fields like image recognition, natural language processing, and more.

What is a Neural Network?

A neural network is a computing system made up of interconnected nodes (neurons) that process information. Each connection has a weight that adjusts during training.

Basic Components

Neurons

Each neuron receives inputs, processes them with an activation function, and produces an output.

Layers

Neural networks consist of:

• Input Layer: Receives data
• Hidden Layers: Process information
• Output Layer: Produces results

Activation Functions

Common activation functions include:

• ReLU (Rectified Linear Unit)
• Sigmoid
• Tanh
• Softmax (for classification)

Building Your First Neural Network

Using TensorFlow/Keras:

```python from tensorflow import keras from tensorflow.keras import layers

model = keras.Sequential([ layers.Dense(128, activation='relu', input_shape=(784,)), layers.Dense(64, activation='relu'), layers.Dense(10, activation='softmax') ])

model.compile( optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'] ) ```

Training Process

1. Forward pass: Data flows through the network
2. Calculate loss: Compare predictions with actual values
3. Backpropagation: Update weights to reduce loss
4. Repeat: Continue until model converges

Applications

Neural networks are used in:

• Image classification
• Speech recognition
• Language translation
• Recommendation systems
• Autonomous vehicles

The possibilities are endless!