!Click The Green Flag Twice! Enter the input and output amounts and watch the Neural Network generate. Press 1 or 2 to show or hide variables and lists. How It Works: 1. The inputs and outputs are collectively summed and subsequently multiplied by 1.8 to determine the quantity of hidden neurons. 2. The output points are positioned and centered on the display utilizing the modulo block to ascertain whether the outputs are even or odd. 3. The hidden layer of points is arranged, and each output point transmits its coordinates to the x and y tables, where each corresponding hidden layer point is assigned these coordinates and employs the anti-tangent block to ascertain the direction of the connecting lines (neurons). 4. The procedure detailed in Step 3 is reiterated for both the input layer and the hidden layer.
How Does A Neural Network Work? A computer neural network (or artificial neural network, ANN) is a type of machine learning model designed to mimic the structure and function of the human brain to solve complex problems. Composed of interconnected "neurons" arranged in layers, these networks analyze vast amounts of data, learn patterns, and improve their accuracy over time without explicit programming, making them the foundation of modern AI technologies like ChatGPT and facial recognition. Structure: ANNs consist of an input layer (receives data), hidden layers (process data), and an output layer (delivers the final prediction). Neurons & Weights: Interconnected nodes pass information to each other. The strength of this connection is determined by "weights," which are adjusted during training to minimize errors. Learning Process: Neural networks learn by processing data, comparing their output to the desired result, and adjusting their internal parameters (weights and biases) to improve accuracy. Deep Learning: When a network contains many hidden layers, it is known as a "deep neural network". Videos: https://www.youtube.com/watch?v=jmmW0F0biz0 https://www.youtube.com/watch?v=YKcF6L-0jRo
