Post Article 32: Neural Network Foundations – How Machines Learn 🧠

Article 32: Neural Network Foundations – How Machines Learn 🧠 A Neural Network is a mathematical model inspired by the neurons in a human brain. While a simple model like Linear Regression finds a straight line, a Neural Network can find any complex shape in data. 1. The Structure of a Neural Network A network consists of layers of Neurons, Input Layer - receives the raw data (like pixels of an image or numbers in a table). Hidden Layers - are between the input and output. They perform the math and find hidden patterns. More hidden layers mean the network is Deep. Output Layer - gives the final prediction 2. Inside the Neuron (The Math) Every neuron does a simple calculation. It takes inputs, multiplies them by Weights, adds a Bias and passes the result through an Activation Function. Weights - represent the strength of the connection. If a weight is high, that input is very important. Bias - extra number that helps the neuron decide when to activate. Activation Function -mathematical gate that decides the final output of the neuron. (ReLU, Sigmoid and Softmax functions) 3. The Learning Process (Forward Propagation) - Data enters the Input Layer. - The machine multiplies the data by the current Weights. - The data moves through the Hidden Layers. - The machine makes a Prediction in the Output Layer. - The machine calculates the Loss 4. How the Machine Corrects Itself (Backpropagation) This is the most advanced part of DL. Backpropagation is the process of moving backward from the error to update the weights. The machine uses the Chain Rule from calculus to calculate the Gradient It finds exactly how much each weight contributed to the total error. It then changes the weights slightly to make the error smaller for the next time. Summary 📝 Neural Networks use layers of neurons to find complex patterns. Each neuron uses Weights, Bias and Activation Functions to process data. Forward Propagation makes a prediction, and Backpropagation uses math to correct errors by updating weights. Optimizers like Adam make this process fast and efficient. ✨ 🙊😁. In the next article (Article 33), we discuss Computer Vision, where we learn how CNNs (Convolutional Neural Networks) see images! 📷⏭️ 🙊😁 ✍️ @TheInfinityAI