Advanced 11 planned topics AI
Introduction to Neural Networks
Learn how neural networks compute, train, and generalize. Free neural networks introduction.
Learning Outcomes
- Draw a one-hidden-layer network
- Explain weights, bias, and activation
- Describe gradient descent in one paragraph
- Know vanishing gradients as a later topic
Overview
A neural network is a graph of weighted connections. Training adjusts weights so outputs match targets.
This introduction uses a tiny network to explain activation, loss, and learning rate.
It is the conceptual sibling of our deep learning page.
What this course will cover
- 1. Perceptron
- 2. MLP
- 3. Activations
- 4. Training loop
- 5. Hyperparameters
- 6. Regularization
Start a complete tutorial now
Full 6-level lessons are live for C, C++, Java, Python, and JavaScript. Use them while the remaining Neural Networks lessons are added.