Djl Dive Into Deep Learning 0 1 0 Documentation

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 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. To get started with deep learning, we will need to develop a few basic skills. All machine learning is concerned with extracting information from data. So we will begin by learning the practical skills for storing, manipulating, and preprocessing data.

Moreover, machine learning typically requires working with large datasets, which we can think of as tables, where the rows correspond to examples and the columns correspond to attributes. Linear algebra gives us a powerful set of techniques for working with tabular data. We will not go too far into the weeds but rather focus on the basic of matrix operations and their implementation. Additionally, deep learning is all about optimization. We have a model with some parameters and we want to find those that fit our data the best. Determining which way to move each parameter at each step of an algorithm requires a little bit of calculus, which will be briefly introduced.

Fortunately, the autograd package automatically computes differentiation for us, and we will cover it next. Next, machine learning is concerned with making predictions: what is the likely value of some unknown attribute, given the information that we observe? To reason rigorously under uncertainty we will need to invoke the language of probability. In the end, the official documentation provides plenty of descriptions and examples that are beyond this book. To conclude the chapter, we will show you how to look up documentation for the needed information. 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:

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. 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 This folder contains examples and documentation for the Deep Java Library (DJL) project. Note: when searching in JavaDoc, if your access is denied, please try removing the string undefined in the url. Please activate JavaScript to enable the search functionality.

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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.

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