This article uses a narrative of an AI neuron to explain fundamental concepts of neural networks, including neuron structure, weighted sums, activation functions, loss functions, gradient descent, and learning rate, making complex AI topics accessible to beginners.
This article introduces artificial neural networks, explains the role of artificial neurons and their weighted connections, and provides an overview of common activation functions—including linear, nonlinear ramp, threshold/step, and sigmoid forms—highlighting their characteristics and typical saturation values.
This article explains the role of activation functions in deep learning, covering their definition, why they are needed, the main categories—including linear, binary step, and various non‑linear functions such as Sigmoid, TanH, ReLU, Leaky ReLU, ELU, Softmax and Swish—along with each function's mathematical form, advantages, disadvantages, and practical usage recommendations.
This article explains how biological neurons inspire artificial neural networks, describing neuron concepts, threshold firing, weighted inputs, bias, activation functions such as the step and sigmoid functions, and shows how these ideas are expressed mathematically and visualized with diagrams.
The article explains how basic mathematical principles—starting with simple predictors and linear classifiers, then extending to multi‑classifier systems, activation functions, and weight adjustments—underpin neural network architecture, illustrating each step with concrete examples to show how mathematics drives AI model training and performance.
This tutorial explains neural network fundamentals by defining layers, weights, biases, and sigmoid activation, then walks through building a Python class that implements forward propagation, a sum‑of‑squared‑error loss, and backpropagation using the chain rule and gradient descent to train a simple two‑layer network.
Despite the hype that deep learning demands heavy calculus and linear algebra, this article shows beginners how basic concepts like derivatives and partial derivatives can be grasped with simple analogies, explains activation functions, learning rates, and the role of training and testing data in neural networks.
