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## Hyper Parameters 🔡
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### What is it?
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**Hyperparameters** contain the data that govern the training process itself. </br>
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As an ex. if the **learning rate** is too big, our model may skip the optimal solution, if the **learning rate** is too small we may need to many iterations to get the best result, so we try to find a **learning rate** that fits for our purpose.
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## What are Weights and Biases?
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**Weights** and **Biases** are the **learnable parameters of your model**. As well as **neural networks**, they appear with the same names in related models such as linear regression. Most machine learning algorithms include some **learnable parameters** like this.
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## 📝 Example Code with Comments 📝
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```
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import tensorflow as tf # deep learning library. Tensors are just multi-dimensional arrays
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