> For the complete documentation index, see [llms.txt](https://fall2019.fullstackdeeplearning.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://fall2019.fullstackdeeplearning.com/course-content/training-and-debugging/start-simple.md).

# Start Simple

{% embed url="<https://www.youtube.com/watch?v=xTjPrYUmPhk>" %}
Start Simple - Troubleshooting
{% endembed %}

## Summary

* **Choose simple architecture**:
  * LeNet/ResNet for images.
  * LSTM for sequences.
  * Fully-connected network with one hidden layer for all other tasks.
* **Use sensible hyper-parameter defaults**:
  * Adam optimizer with a “magic” learning rate value of 3e-4.
  * ReLU activation for fully-connected and convolutional models and TanH activation for LSTM models.
  * He initialization for ReLU and Glorot initialization for TanH.
  * No regularization and data normalization.
* **Normalize data inputs**: subtracting the mean and dividing by the variance.
* **Simplify the problem**:
  * Working with a small training set around 10,000 examples.
  * Using a fixed number of objects, classes, input size, etc.
  * Creating a simpler synthetic training set like in research labs.


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