📌 What is an ANN?

An Artificial Neural Network (ANN) is a computational model inspired by the way biological neurons in the human brain work.
It consists of layers of interconnected nodes (neurons) that process data, learn patterns, and make predictions.

📌 Structure of an ANN

1. Input Layer
   • Receives raw features (e.g., pixel values in an image, sensor readings, text embeddings).
   • Each neuron represents one input feature.
2. Hidden Layers
   • Perform transformations on input data using weights, biases, and activation functions.
   • Can be one (shallow ANN) or many (deep ANN).
3. Output Layer
   • Produces the final prediction.
   • For classification → probabilities for each class.
   • For regression → continuous value.

📌 How ANN Works (Step-by-Step)

1. Weighted Sum
   Each neuron calculates: z=∑(wi⋅xi)+bz = \sum (w_i \cdot x_i) + bz=∑(wi​⋅xi​)+b where www = weight, xxx = input, bbb = bias.
2. Activation Function
   Applies a non-linear transformation (e.g., ReLU, Sigmoid, Tanh) so the network can model complex patterns.
3. Forward Propagation
   Input flows layer by layer until an output is generated.
4. Loss Function
   Measures how far predictions are from actual values (e.g., MSE, Cross-Entropy).
5. Backpropagation
   Computes gradients of the loss w.r.t. weights and biases.
6. Weight Update
   Uses Gradient Descent (or variants like Adam) to minimize loss.

📌 Types of Neural Networks

• Feedforward ANN (FNN): Simple one-directional flow (no loops).
• Convolutional Neural Network (CNN): Specialized for images & computer vision.
• Recurrent Neural Network (RNN): Good for sequential data (speech, text, time-series).
• Deep Neural Network (DNN): Many hidden layers (deep learning).

📌 Example: ANN for Spam Detection

• Input Layer: Email features (keywords, frequency, sender info).
• Hidden Layers: Extract relationships (e.g., “free money” + “click link”).
• Output Layer: 0 = Not Spam, 1 = Spam.

📊 ANN Architecture Diagram (Conceptual)

Input Layer → Hidden Layer(s) → Output Layer
   x1 ──▶ ●───▶ ●
   x2 ──▶ ●───▶ ●───▶ y (prediction)
   x3 ──▶ ●───▶ ●

📌 Advantages of ANN

✅ Learns complex non-linear patterns
✅ Adaptable across domains (vision, NLP, speech, healthcare, finance)
✅ Improves with large datasets

📌 Challenges of ANN

❌ Requires lots of data and computation power
❌ Difficult to interpret (“black box”)
❌ Risk of overfitting if not regularized

📖 Analogy:

• ANN is like a brain-inspired calculator:
  • Neurons (nodes) = tiny decision makers.
  • Synapses (weights) = connection strengths.
  • Learning = adjusting connections until predictions are accurate.