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| <title>Data Driven World Interface</title> | |
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| <p class="eyebrow">Course Interface</p> | |
| <h1>Data Driven World</h1> | |
| <p> | |
| Explore NumPy matrix operations through an interactive visual lab. | |
| Choose operations in natural language, set matrix shapes, and inspect | |
| the generated NumPy code with input/output visualizations. | |
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| <section class="cards"> | |
| <a class="card" href="numpy-lab.html"> | |
| <h2>NumPy Matrix Lab</h2> | |
| <p> | |
| Operations, shape-aware inputs, NumPy snippets, and visualized arrays | |
| up to 3 dimensions. | |
| </p> | |
| <span>Open Lab</span> | |
| </a> | |
| <a class="card" href="gradient-descent.html"> | |
| <h2>Gradient Descent Studio</h2> | |
| <p> | |
| Dynamic optimization walkthrough for one-feature linear regression | |
| with step-level gradients, costs, and descent-direction landscapes. | |
| </p> | |
| <span>Open Studio</span> | |
| </a> | |
| <a class="card" href="linear-regression-steps.html"> | |
| <h2>Linear Regression Step Trainer</h2> | |
| <p> | |
| Solve hand-calculation style gradient and one-step parameter updates | |
| on two easy samples with optional z-normalization. | |
| </p> | |
| <span>Open Trainer</span> | |
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