> For the complete documentation index, see [llms.txt](https://docs.zama.org/concrete-ml/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zama.org/concrete-ml/1.4/deep-learning/examples.md). # Deep Learning Examples These examples illustrate the basic usage of Concrete ML to build various types of neural networks. They use simple data-sets, focusing on the syntax and usage of Concrete ML. For examples showing how to train high-accuracy models on more complex data-sets, see the [Demos and Tutorials](/concrete-ml/1.4/getting-started/showcase.md) section. ## FHE constraints considerations The examples listed here make use of [simulation](/concrete-ml/1.4/advanced-topics/compilation.md#fhe-simulation) to perform evaluation over large test sets. Since FHE execution can be slow, only a few FHE executions can be performed. The [correctness guarantees](/concrete-ml/1.4/getting-started/concepts.md#cryptography-concepts) of Concrete ML ensure that accuracy measured with simulation is the same as that which will be obtained during FHE execution. Some examples constrain accumulators to 7-8 bits, which can be sufficient for simple data-sets. Up to 16-bit accumulators can be used, but this introduces a slowdown of 4-5x compared to 8-bit accumulators. ## List of Examples ### 1. Step-by-step guide to building a custom NN [![](/files/XmsH0zlj3FLAzrOPK16X) Quantization aware training example](https://github.com/zama-ai/concrete-ml/blob/release/1.4.x/docs/advanced_examples/QuantizationAwareTraining.ipynb) This shows how to use Quantization Aware Training and pruning when starting out from a classical PyTorch network. This example uses a simple data-set and a small NN, which achieves good accuracy with low accumulator size. ### 2. Custom convolutional NN on the [Digits](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) data-set [![](/files/XmsH0zlj3FLAzrOPK16X) Convolutional Neural Network](https://github.com/zama-ai/concrete-ml/blob/release/1.4.x/docs/advanced_examples/ConvolutionalNeuralNetwork.ipynb) Following the [Step-by-step guide](/concrete-ml/1.4/deep-learning/fhe_friendly_models.md), this notebook implements a Quantization Aware Training convolutional neural network on the MNIST data-set. It uses 3-bit weights and activations, giving a 7-bit accumulator. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## 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, and the optional `goal` query parameter: ``` GET https://docs.zama.org/concrete-ml/1.4/deep-learning/examples.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. 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.