The Java Implementation Of Dive Into Deep Learning D2l Ai

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 There was an error while loading. Please reload this page.

Interactive deep learning book with code, math, and discussions Implemented with PyTorch, NumPy/MXNet, JAX, and TensorFlow Adopted at 500 universities from 70 countries Star You can modify the code and tune hyperparameters to get instant feedback to accumulate practical experiences in deep learning. We offer an interactive learning experience with mathematics, figures, code, text, and discussions, where concepts and techniques are illustrated and implemented with experiments on real data sets. You can discuss and learn with thousands of peers in the community through the link provided in each section. 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 An Engine-Agnostic Deep Learning Framework in Java A universal scalable machine learning model deployment solution The Java implementation of Dive into Deep Learning (D2L.ai) A universal scalable machine learning model deployment solution

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. 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.

In order to get you up and running for hands-on learning experience, we need to set you up with an environment for running Python, Jupyter notebooks, the relevant libraries, and the code needed to... JDK 11 (or above) are required to run the examples provided in this folder. To confirm the java path is configured properly, run: Use the following command to install Jupyter Notebook in Python 3: By default jupyter notebook runs only Python3. You need to install the Java kernel to run Java code such as DJL.

The D2L.ai repository hosts the code and content for the "Dive into Deep Learning" (D2L) book, an interactive open-source textbook that teaches deep learning with code, mathematics, and discussions. The book integrates narrative explanations, mathematical formulations, visualizations, and executable code examples across multiple deep learning frameworks. This page provides a high-level overview of the D2L.ai project's architecture, components, and organization. For information about the build system specifically, see Build System. For details on how the project supports multiple frameworks, see Framework Support. The project delivers a complete educational system with integrated teaching materials, code, and interactive notebooks that can be run on various platforms including locally, on Google Colab, Amazon SageMaker, and Amazon SageMaker Studio Lab.

Sources: README.md20-27 static/frontpage/frontpage.html200-206 The D2L.ai system consists of multiple integrated components that work together to deliver the educational content, code examples, and interactive experiences.

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