D2l Dive Into Deep Learning 1 0 3 Documentation

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. 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 Alongside giant datasets and powerful hardware, great software tools have played an indispensable role in the rapid progress of deep learning.

Starting with the pathbreaking Theano library released in 2007, flexible open-source tools have enabled researchers to rapidly prototype models, avoiding repetitive work when recycling standard components while still maintaining the ability to make low-level... Over time, deep learning’s libraries have evolved to offer increasingly coarse abstractions. Just as semiconductor designers went from specifying transistors to logical circuits to writing code, neural networks researchers have moved from thinking about the behavior of individual artificial neurons to conceiving of networks in terms... So far, we have introduced some basic machine learning concepts, ramping up to fully-functional deep learning models. In the last chapter, we implemented each component of an MLP from scratch and even showed how to leverage high-level APIs to roll out the same models effortlessly. To get you that far that fast, we called upon the libraries, but skipped over more advanced details about how they work.

In this chapter, we will peel back the curtain, digging deeper into the key components of deep learning computation, namely model construction, parameter access and initialization, designing custom layers and blocks, reading and writing... These insights will move you from end user to power user, giving you the tools needed to reap the benefits of a mature deep learning library while retaining the flexibility to implement more complex... While this chapter does not introduce any new models or datasets, the advanced modeling chapters that follow rely heavily on these techniques. This section displays classes and functions (sorted alphabetically) in the d2l package, showing where they are defined in the book so you can find more detailed implementations and explanations. See also the source code on the GitHub repository. Defines the computation performed at every call.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while... The residual connection followed by layer normalization. Defines the computation performed at every call. Interactive deep learning book with code, math, and discussions Implemented with PyTorch, NumPy/MXNet, JAX, and TensorFlow Adopted at 500 universities from 70 countries

Image data is represented as a two-dimensional grid of pixels, be the image monochromatic or in color. Accordingly each pixel corresponds to one or multiple numerical values respectively. So far we have ignored this rich structure and treated images as vectors of numbers by flattening them, irrespective of the spatial relation between pixels. This deeply unsatisfying approach was necessary in order to feed the resulting one-dimensional vectors through a fully connected MLP. Because these networks are invariant to the order of the features, we could get similar results regardless of whether we preserve an order corresponding to the spatial structure of the pixels or if we... Ideally, we would leverage our prior knowledge that nearby pixels are typically related to each other, to build efficient models for learning from image data.

This chapter introduces convolutional neural networks (CNNs) (LeCun et al., 1995), a powerful family of neural networks that are designed for precisely this purpose. CNN-based architectures are now ubiquitous in the field of computer vision. For instance, on the Imagnet collection (Deng et al., 2009) it was only the use of convolutional neural networks, in short Convnets, that provided significant performance improvements (Krizhevsky et al., 2012). Modern CNNs, as they are called colloquially, owe their design to inspirations from biology, group theory, and a healthy dose of experimental tinkering. In addition to their sample efficiency in achieving accurate models, CNNs tend to be computationally efficient, both because they require fewer parameters than fully connected architectures and because convolutions are easy to parallelize across... Consequently, practitioners often apply CNNs whenever possible, and increasingly they have emerged as credible competitors even on tasks with a one-dimensional sequence structure, such as audio (Abdel-Hamid et al., 2014), text (Kalchbrenner et al.,...

Some clever adaptations of CNNs have also brought them to bear on graph-structured data (Kipf and Welling, 2016) and in recommender systems. First, we will dive more deeply into the motivation for convolutional neural networks. This is followed by a walk through the basic operations that comprise the backbone of all convolutional networks. These include the convolutional layers themselves, nitty-gritty details including padding and stride, the pooling layers used to aggregate information across adjacent spatial regions, the use of multiple channels at each layer, and a careful... We will conclude the chapter with a full working example of LeNet, the first convolutional network successfully deployed, long before the rise of modern deep learning. In the next chapter, we will dive into full implementations of some popular and comparatively recent CNN architectures whose designs represent most of the techniques commonly used by modern practitioners.

Please activate JavaScript to enable the search functionality. From here you can search these documents. Enter your search words into the box below and click "search". Note that the search function will automatically search for all of the words. Pages containing fewer words won't appear in the result list. So far we have discussed how to process data and how to build, train, and test deep learning models.

However, at some point we will hopefully be happy enough with the learned models that we will want to save the results for later use in various contexts (perhaps even to make predictions in... Additionally, when running a long training process, the best practice is to periodically save intermediate results (checkpointing) to ensure that we do not lose several days’ worth of computation if we trip over the... Thus it is time to learn how to load and store both individual weight vectors and entire models. This section addresses both issues. For individual tensors, we can directly invoke the load and save functions to read and write them respectively. Both functions require that we supply a name, and save requires as input the variable to be saved.

We can now read the data from the stored file back into memory. We can store a list of tensors and read them back into memory. We can even write and read a dictionary that maps from strings to tensors. This is convenient when we want to read or write all the weights in a model.

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