Dive Into Deep Learning Djl

An interactive deep learning book with code, math, and discussions Provides Deep Java Library(DJL) implementations Adopted at 175 universities from 40 countries Amazon Scientist CMU Assistant Professor Amazon Research ScientistMathematics for Deep Learning Amazon Applied ScientistMathematics for Deep Learning Postdoctoral Researcher at ETH Zürich Recommender Systems This project is modified from the original Dive Into Deep Learning book by Aston Zhang, Zachary C.

Lipton, Mu Li, Alex J. Smola and all the community contributors. GitHub of the original book: https://github.com/d2l-ai/d2l-en. We have adapted the book to to use Java and the Deep Java Library(DJL). All the notebook here can be downloaded and run using Java Kernel. We also compiled the book into a website.

This project is currently being developed and maintained by AWS and the DJL community. Please follow the instruction here for how to run notebook using Java kernel. Please follow the contributor guide here Dive into Deep Learning (D2L) is a book that teaches all of the concepts of deep learning. It covers topics including the basics of deep learning, gradient descent, convolutional neural networks, recurrent neural networks, computer vision, natural language processing, recommender systems, and generative adversarial networks. The DJL edition is our adaptation of the original open source book.

Instead of using python like the original, we modified it to use Java and DJL concepts in the text. If you are looking for a more comprehensive understanding of deep learning or more focus on the fundamentals, this is the best resource to use. There was an error while loading. Please reload this page. Dive into Deep Learning (D2L) is a book that teaches all of the concepts of deep learning. It covers topics including the basics of deep learning, gradient descent, convolutional neural networks, recurrent neural networks, computer vision, natural language processing, recommender systems, and generative adversarial networks.

The DJL edition is our adaptation of the original open source book. Instead of using python like the original, we modified it to use Java and DJL concepts in the text. If you are looking for a more comprehensive understanding of deep learning or more focus on the fundamentals, this is the best resource to use. arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning. DJL is designed to be easy to get started with and simple to use for Java developers. DJL provides a native Java development experience and functions like any other regular Java library. You don't have to be machine learning/deep learning expert to get started.

You can use your existing Java expertise as an on-ramp to learn and use machine learning and deep learning. You can use your favorite IDE to build, train, and deploy your models. DJL makes it easy to integrate these models with your Java applications. Because DJL is deep learning engine agnostic, you don't have to make a choice between engines when creating your projects. You can switch engines at any point. To ensure the best performance, DJL also provides automatic CPU/GPU choice based on hardware configuration.

DJL's ergonomic API interface is designed to guide you with best practices to accomplish deep learning tasks. The following pseudocode demonstrates running inference: The following pseudocode demonstrates running training: Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning. DJL is designed to be easy to get started with and simple to use for Java developers. DJL provides a native Java development experience and functions like any other regular Java library.

You don't have to be machine learning/deep learning expert to get started. You can use your existing Java expertise as an on-ramp to learn and use machine learning and deep learning. You can use your favorite IDE to build, train, and deploy your models. DJL makes it easy to integrate these models with your Java applications. Because DJL is deep learning engine agnostic, you don't have to make a choice between engines when creating your projects. You can switch engines at any point.

To ensure the best performance, DJL also provides automatic CPU/GPU choice based on hardware configuration. DJL's ergonomic API interface is designed to guide you with best practices to accomplish deep learning tasks. The following pseudocode demonstrates running inference: The following pseudocode demonstrates running training:

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