Introduction to AI Artificial neural network xor

   Artificial neural network 

artificial neural network are computational models inspired by the biological neural networks present in animal brains. ANNS are composed of interconnected nodes, often referred to as neurons or units, organized into layers 

First, we must download the zip file containing the Artificial Neural Network (ANN) code. Then, open the Arduino Integrated Development Environment (IDE) and run the Serial monitor. This will allow us to observe the progressive training and the final results. The program will transmit a set of training data every one thousand cycles, enabling us to witness how the network is gradually "learning" and approaching the correct answers.

xor function 

  xor code on Arduino 

expected output 

To demonstrate this historically significant problem, one typically creates a simple neural network with one or more hidden layers and trains it on the XOR dataset. The goal is to observe how the network learns to approximate the XOR function through iterative training.

overall the variation in the number of training cycles and the final results demonstrates the complicity of neural network training to various factors.

Artificial intelligence refers to the capability of computers and machines to mimic human intelligence and perform tasks that typically require human-like thinking, reasoning, and problem-solving abilities. 

Artificial Intelligence (AI) has left an indelible mark on history by increasing numerous sectors, from technology to healthcare and beyond. 

here are the key reasons:

-Industrial Evolution: In industry, AI has transformed manufacturing processes, optimizing efficiency, and streamlining maintenance.

- Financial Sector: In finance, AI-powered algorithms have enhanced risk management, fraud detection, and investment strategies,

The number of training cycles can differ each time an AI model is trained due to various reasons:

- Random Starting Point: AI models often initialize their parameters randomly before training. This means that each training session begins from a different starting point.

- Stopping Criteria: Training may stop early based on certain criteria, like achieving a target accuracy.