What is 神经网络入门?
Introduction to Neural Networks is Brilliant’s introductory course on neural networks. The course uses hands-on experiments to help learners understand the inner workings of models and build intuition about the problems neural networks can solve, rather than requiring them to master complex mathematical derivations right from the start.
The current course catalog includes 15 lessons and 112 exercises, grouped by neural networks, neurons, and layers, covering topics such as computer vision problems, decision boundaries, XOR, Sigmoid, single-neuron training, hidden layers, curve fitting, and shape recognition.
The official recommendation is that learners have a basic understanding of algebra and logic, though prior programming experience is not required. Learners can review the course content on the course page and log in to begin, progressively completing interactive exercises. Please note that this course introduction does not constitute a full-fledged production‑grade model development curriculum, and access rights and subscription terms should be confirmed directly on the platform.
Key features
Interactive Neural Network Learning
Neurons and Activation Functions
Decision Boundary and Classification
Hidden layers and curve fitting
Complete the exercises step by step by topic.
Use cases and audience
AI beginners with basic algebraic knowledge
- Building Neural Network Intuition
- Understanding the Basic Concepts of Machine Learning
- As conceptual learning prior to programming
Questions in the course content and interactive exercises
Conceptual understanding, practice feedback, and learning progress
How to use
- 01
Read the course syllabus and review your foundational algebra and logic knowledge.
- 02
Log in to the platform and start with the neural networks chapter.
- 03
Complete the interactive exercises, then proceed to the topics of neurons and network layers.
Official website & sources
Updated on 2026-10-24
