AI guides | Uni-Sine

How AI works

Gradient descent visualization: SGD, momentum and AdamFree gradient descent visualization: race SGD, momentum, RMSProp and Adam down a loss surface, tune the learning rate and watch each loss curve fall.
How do neural networks learn? Gradient descent explainedHow neural networks learn from examples: weights, the loss, gradient descent and backpropagation, with a worked example of one learning step.
How to write better AI prompts: 7 techniques that workHow to write better AI prompts: give context and a clear goal, show examples, ask for a format, let the model reason, and refine with follow-ups.
Language model simulator: tokens, next words, temperatureFree language model simulator: see how text becomes tokens, what odds a model gives each next token, and how temperature changes what it writes.
Neural network visualization: watch a network learnBuild a small neural network and watch it learn live: its decision boundary, weights and loss curve. A neural network playground with experiments to try.
Neural network weights and biases explainedWhat weights and biases are in a neural network, how a neuron uses them, why the bias matters, how to count them, and how training learns them.
Overfitting vs underfitting: how to spot and fix eachOverfitting vs underfitting in machine learning: what each looks like, how to spot them from loss curves, and how to fix them, with worked examples.
What is a token in AI? Meaning, examples and costLearn what a token in AI is, see how text really splits into tokens, and estimate tokens and cost yourself, with worked examples and real prices.

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