Djl Dive Into Deep Learning Dive Into Deep Learning 0 1 0

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 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. 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. 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. Finding the area of a polygon had remained mysterious until at least 2,500 years ago, when ancient Greeks divided a polygon into triangles and summed their areas. To find the area of curved shapes, such as a circle, ancient Greeks inscribed polygons in such shapes. As shown in Section 2.4, an inscribed polygon with more sides of equal length better approximates the circle. This process is also known as the method of exhaustion. In fact, the method of exhaustion is where integral calculus (will be described in sec_integral_calculus) originates from.

More than 2,000 years later, the other branch of calculus, differential calculus, was invented. Among the most critical applications of differential calculus, optimization problems consider how to do something the best. As discussed in Section 2.3.10.1, such problems are ubiquitous in deep learning. In deep learning, we train models, updating them successively so that they get better and better as they see more and more data. Usually, getting better means minimizing a loss function, a score that answers the question “how bad is our model?” This question is more subtle than it appears. Ultimately, what we really care about is producing a model that performs well on data that we have never seen before.

But we can only fit the model to data that we can actually see. Thus we can decompose the task of fitting models into two key concerns: i) optimization: the process of fitting our models to observed data; ii) generalization: the mathematical principles and practitioners’ wisdom that guide... To help you understand optimization problems and methods in later chapters, here we give a very brief primer on differential calculus that is commonly used in deep learning. We begin by addressing the calculation of derivatives, a crucial step in nearly all deep learning optimization algorithms. In deep learning, we typically choose loss functions that are differentiable with respect to our model’s parameters. Put simply, this means that for each parameter, we can determine how rapidly the loss would increase or decrease, were we to increase or decrease that parameter by an infinitesimally small amount.

Regression refers to a set of methods for modeling the relationship between data points \(\mathbf{x}\) and corresponding real-valued targets \(y\). In the natural sciences and social sciences, the purpose of regression is most often to characterize the relationship between the inputs and outputs. Machine learning, on the other hand, is most often concerned with prediction. Regression problems pop up whenever we want to predict a numerical value. Common examples include predicting prices (of homes, stocks, etc.), predicting length of stay (for patients in the hospital), demand forecasting (for retail sales), among countless others. Not every prediction problem is a classic regression problem.

In subsequent sections, we will introduce classification problems, where the goal is to predict membership among a set of categories. Linear regression may be both the simplest and most popular among the standard tools to regression. Dating back to the dawn of the 19th century, linear regression flows from a few simple assumptions. First, we assume that the relationship between the features \(\mathbf{x}\) and targets \(y\) is linear, i.e., that \(y\) can be expressed as a weighted sum of the inputs \(\textbf{x}\), give or take some noise... Second, we assume that any noise is well-behaved (following a Gaussian distribution). To motivate the approach, let us start with a running example.

Suppose that we wish to estimate the prices of houses (in dollars) based on their area (in square feet) and age (in years). To actually fit a model for predicting house prices, we would need to get our hands on a dataset consisting of sales for which we know the sale price, area and age for each... In the terminology of machine learning, the dataset is called a training data set or training set, and each row (here the data corresponding to one sale) is called an example (or data instance,... The thing we are trying to predict (here, the price) is called a label (or target). The variables (here age and area) upon which the predictions are based are called features or covariates. Typically, we will use \(n\) to denote the number of examples in our dataset.

We index the data instances by \(i\), denoting each input as \(x^{(i)} = [x_1^{(i)}, x_2^{(i)}]\) and the corresponding label as \(y^{(i)}\). 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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