How Multi-Layer Perceptrons Actually Work (Step-by-Step) скачать в хорошем качестве

How Multi-Layer Perceptrons Actually Work (Step-by-Step)

The Multi-Layer Perceptron (MLP) is the foundation of modern Deep Learning. In this video, we visualize exactly how layers of neurons combine to solve complex problems that a single perceptron can't. We start by looking at the limitations of a single neuron and demonstrate why "Hidden Layers" are the secret to intelligent machines. Whether you are building your first neural network in Python or studying the math behind the code, this tutorial breaks down the MLP architecture. In this video, we cover: The Limit of One: Why single perceptrons fail at non-linear problems (like XOR). The Power of Layers: Adding "Hidden Layers" to create a Multi-Layer Perceptron. Inside the Node: Weights, Biases, and Activation Functions explained. The Learning Loop: How Forward Propagation and Backpropagation work together to minimize error. By the end of this video, you will have a clear mental model of how an MLP processes data and "learns" from its mistakes.