# Quick start Deep Java Library (DJL) is designed to be easy to get started with and simple to use. The easiest way to learn DJL is to read the [beginner tutorial](../jupyter/tutorial/README.md) or our [examples](../examples/README.md). You can also view our 1.5 hour long (in 8 x ~10 minute segments) DJL 101 tutorial video series: [<img src="https://resources.djl.ai/images/djl-101.jpg" width="560" height="315">](https://www.youtube.com/embed/?list=PLC1JzXeHJitDmZBHupMGZE2zHzU8fEvfD&listType=playlist) ## Prerequisites * You need a Java Development Kit (JDK) installed on your system. We recommend using JDK 8, but any later version also works. For more information, see [Setup](development/setup.md). * You need git installed on your system to clone the DJL repository. ## Try DJL We developed several interactive toolkits for you to experience DJL. You can start from here to run inference online and download starter template with DJL. See [DJL Future Labs](interactive_tool.md) ## Beginner tutorial To get started, we recommend that you follow our short [beginner tutorial](../jupyter/tutorial/README.md). It takes you through some of the basics of deep learning to create a model, train your model, and run inference using your trained model. ## Run examples DJL also provides examples for both training and performing inference with deep learning models. You can find the examples and their source code in the [examples](https://github.com/deepjavalibrary/djl/tree/master/examples) directory. All of our examples are executed by a simple command. For detailed command line instructions, see each example’s Readme.md file. - [Train your first model](../examples/docs/train_mnist_mlp.md) - [Single-shot Object Detection inference example](../examples/docs/object_detection.md) - [More examples](https://github.com/deepjavalibrary/djl/tree/master/examples) - [Jupyter examples](../jupyter/README.md) ## Other resources - [JavaDoc API Reference](https://javadoc.djl.ai/) - [Contributor Documentation](development/README.md) - [FAQ](faq.md)