Tagged articles

activation function

8 articles · Page 1 of 1

Jun 23, 2026 · Artificial Intelligence

Understanding NLP Activation Functions: The Role of Softmax

The article explains how the softmax activation function converts neural network outputs into probability distributions for multi‑class NLP tasks, describes its mathematical form and S‑shaped behavior, and discusses the inductive approach, data quality, training objectives, and interpretability challenges in deep learning language models.

Data QualityNLPactivation function

0 likes · 4 min read

Understanding NLP Activation Functions: The Role of Softmax

Liangxu Linux

Mar 23, 2024 · Artificial Intelligence

Understanding AI Neurons: A Storytelling Guide to Basics of Neural Networks

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.

AI basicsNeural Networkactivation function

0 likes · 9 min read

Understanding AI Neurons: A Storytelling Guide to Basics of Neural Networks

Model Perspective

Aug 6, 2022 · Artificial Intelligence

Understanding Activation Functions in Artificial Neural Networks

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.

activation functionartificial neural networkdeep learning

0 likes · 2 min read

Understanding Activation Functions in Artificial Neural Networks

Code DAO

May 12, 2022 · Artificial Intelligence

How Activation Functions Work in Deep Learning

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.

Neural NetworkReLUSigmoid

0 likes · 13 min read

How Activation Functions Work in Deep Learning

Python Programming Learning Circle

Mar 5, 2020 · Artificial Intelligence

Biological Neurons and Their Simple Mathematical Representation in Neural Networks

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.

BiasNeuronThreshold

0 likes · 13 min read

Biological Neurons and Their Simple Mathematical Representation in Neural Networks

Tencent Cloud Developer

Nov 9, 2018 · Artificial Intelligence

Demystifying Neural Networks: A Mathematical Approach

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.

BackpropagationMathematicsXOR problem

0 likes · 15 min read

Demystifying Neural Networks: A Mathematical Approach

Tencent Cloud Developer

Oct 15, 2018 · Artificial Intelligence

Neural Network Fundamentals: Building Your Own Neural Network from Scratch in Python

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.

BackpropagationNeural NetworkPython

0 likes · 8 min read

Neural Network Fundamentals: Building Your Own Neural Network from Scratch in Python

21CTO

Aug 29, 2017 · Artificial Intelligence

Why You Don't Need Advanced Math to Start Learning Deep Learning

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.

Derivativesactivation functionmachine learning basics

0 likes · 12 min read

Why You Don't Need Advanced Math to Start Learning Deep Learning