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Artificial Neural Networks (ANNs) are computing systems inspired by how the human brain works. Instead of one large brain, an ANN has many tiny processing units called neurons that work together to recognize patterns and learn from data
🧠 1. Inspiration from the Brain
Just like the brain has interconnected neurons, ANNs have nodes (artificial neurons) connected in a network. These connections help the system learn from examples, rather than just follow instructions.
🔢 2. Main Structure of an ANN
An ANN is typically divided into layers:
Input Layer – Receives raw data (like pixels of an image).
Hidden Layers – Perform calculations and extract patterns.
Output Layer – Gives the final answer (like “cat” or “dog”).
Each layer is connected to the next using links that have values called weights. These weights determine how important each connection is.
⚙️ 3. How It Works (Step by Step)
Data Enters: You feed the network inputs (e.g., numbers, images).
Weighted Sum: Each neuron multiplies inputs by weights to decide how much importance they carry.
Activation Function: This mathematical function decides if the signal should go forward.
Prediction: The final output layer produces a result based on the calculations.
Learning: The network compares its prediction with the actual answer and adjusts its weights to improve next time. This learning step is called backpropagation.
📊 4. Learning With Examples
During training, the network sees many examples (like thousands of cat images). Each time it predicts incorrectly, it adjusts itself, making small changes to improve accuracy. After many cycles, it gets better at recognizing patterns.
🧠 5. Why Use an ANN?
ANNs are powerful because:
They learn instead of just follow rules.
They can find hidden patterns humans might miss.
They are used in image recognition, language translation, prediction systems, medical diagnosis, and more.