1 Introduction Dive Into Deep Learning 0 1 0 Documentation Djl

Until recently, nearly every computer program that we interact with daily was coded by software developers from first principles. Say that we wanted to write an application to manage an e-commerce platform. After huddling around a whiteboard for a few hours to ponder the problem, we would come up with the broad strokes of a working solution that might probably look something like this: (i) users... To build the brains of our application, we’d have to step through every possible corner case that we anticipate encountering, devising appropriate rules. Each time a customer clicks to add an item to their shopping cart, we add an entry to the shopping cart database table, associating that user’s ID with the requested product’s ID. While few developers ever get it completely right the first time (it might take some test runs to work out the kinks), for the most part, we could write such a program from first...

Our ability to design automated systems from first principles that drive functioning products and systems, often in novel situations, is a remarkable cognitive feat. And when you are able to devise solutions that work \(100\%\) of the time, you should not be using machine learning. Fortunately for the growing community of machine learning (ML) scientists, many tasks that we would like to automate do not bend so easily to human ingenuity. Imagine huddling around the whiteboard with the smartest minds you know, but this time you are tackling one of the following problems: Write a program that predicts tomorrow’s weather given geographic information, satellite images, and a trailing window of past weather. Write a program that takes in a question, expressed in free-form text, and answers it correctly.

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 Just a few years ago, there were no legions of deep learning scientists developing intelligent products and services at major companies and startups.

When the youngest among us (the authors) entered the field, machine learning did not command headlines in daily newspapers. Our parents had no idea what machine learning was, let alone why we might prefer it to a career in medicine or law. Machine learning was a forward-looking academic discipline with a narrow set of real-world applications. And those applications, e.g., speech recognition and computer vision, required so much domain knowledge that they were often regarded as separate areas entirely for which machine learning was one small component. Neural networks then, the antecedents of the deep learning models that we focus on in this book, were regarded as outmoded tools. In just the past five years, deep learning has taken the world by surprise, driving rapid progress in fields as diverse as computer vision, natural language processing, automatic speech recognition, reinforcement learning, and statistical...

With these advances in hand, we can now build cars that drive themselves with more autonomy than ever before (and less autonomy than some companies might have you believe), smart reply systems that automatically... Already, these tools exert ever-wider impacts on industry and society, changing the way movies are made, diseases are diagnosed, and playing a growing role in basic sciences—from astrophysics to biology. This book represents our attempt to make deep learning approachable, teaching you the concepts, the context, and the code. For any computing technology to reach its full impact, it must be well-understood, well-documented, and supported by mature, well-maintained tools. The key ideas should be clearly distilled, minimizing the onboarding time needing to bring new practitioners up to date. Mature libraries should automate common tasks, and exemplar code should make it easy for practitioners to modify, apply, and extend common applications to suit their needs.

Take dynamic web applications as an example. Despite a large number of companies, like Amazon, developing successful database-driven web applications in the 1990s, the potential of this technology to aid creative entrepreneurs has been realized to a far greater degree in... Testing the potential of deep learning presents unique challenges because any single application brings together various disciplines. Applying deep learning requires simultaneously understanding (i) the motivations for casting a problem in a particular way; (ii) the mathematics of a given modeling approach; (iii) the optimization algorithms for fitting the models to... Teaching both the critical thinking skills required to formulate problems, the mathematics to solve them, and the software tools to implement those solutions all in one place presents formidable challenges. Our goal in this book is to present a unified resource to bring would-be practitioners up to speed.

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. 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. Until recently, nearly every computer program that you might have interacted with during an ordinary day was coded up as a rigid set of rules specifying precisely how it should behave. Say that we wanted to write an application to manage an e-commerce platform. After huddling around a whiteboard for a few hours to ponder the problem, we might settle on the broad strokes of a working solution, for example: (i) users interact with the application through an...

To build the brains of our application, we might enumerate all the common events that our program should handle. For example, whenever a customer clicks to add an item to their shopping cart, our program should add an entry to the shopping cart database table, associating that user’s ID with the requested product’s... We might then attempt to step through every possible corner case, testing the appropriateness of our rules and making any necessary modifications. What happens if a user initiates a purchase with an empty cart? While few developers ever get it completely right the first time (it might take some test runs to work out the kinks), for the most part we can write such programs and confidently launch... Our ability to manually design automated systems that drive functioning products and systems, often in novel situations, is a remarkable cognitive feat.

And when you are able to devise solutions that work \(100\%\) of the time, you typically should not be worrying about machine learning. Fortunately for the growing community of machine learning scientists, many tasks that we would like to automate do not bend so easily to human ingenuity. Imagine huddling around the whiteboard with the smartest minds you know, but this time you are tackling one of the following problems: Write a program that predicts tomorrow’s weather given geographic information, satellite images, and a trailing window of past weather. Write a program that takes in a factoid question, expressed in free-form text, and answers it correctly. 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. 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. 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.

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.

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