# Start Simple {% embed url="" %} 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. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://fall2019.fullstackdeeplearning.com/course-content/training-and-debugging/start-simple.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.