6 Builders Guide Dive Into Deep Learning 1 0 3 Documentation D2l

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. 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. 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.

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 When we first introduced neural networks, we focused on linear models with a single output.

Here, the entire model consists of just a single neuron. Note that a single neuron (i) takes some set of inputs; (ii) generates a corresponding scalar output; and (iii) has a set of associated parameters that can be updated to optimize some objective function... Then, once we started thinking about networks with multiple outputs, we leveraged vectorized arithmetic to characterize an entire layer of neurons. Just like individual neurons, layers (i) take a set of inputs, (ii) generate corresponding outputs, and (iii) are described by a set of tunable parameters. When we worked through softmax regression, a single layer was itself the model. However, even when we subsequently introduced MLPs, we could still think of the model as retaining this same basic structure.

Interestingly, for MLPs, both the entire model and its constituent layers share this structure. The entire model takes in raw inputs (the features), generates outputs (the predictions), and possesses parameters (the combined parameters from all constituent layers). Likewise, each individual layer ingests inputs (supplied by the previous layer) generates outputs (the inputs to the subsequent layer), and possesses a set of tunable parameters that are updated according to the signal that... While you might think that neurons, layers, and models give us enough abstractions to go about our business, it turns out that we often find it convenient to speak about components that are larger... For example, the ResNet-152 architecture, which is wildly popular in computer vision, possesses hundreds of layers. These layers consist of repeating patterns of groups of layers.

Implementing such a network one layer at a time can grow tedious. This concern is not just hypothetical—such design patterns are common in practice. The ResNet architecture mentioned above won the 2015 ImageNet and COCO computer vision competitions for both recognition and detection (He et al., 2016) and remains a go-to architecture for many vision tasks. Similar architectures in which layers are arranged in various repeating patterns are now ubiquitous in other domains, including natural language processing and speech. To implement these complex networks, we introduce the concept of a neural network module. A module could describe a single layer, a component consisting of multiple layers, or the entire model itself!

One benefit of working with the module abstraction is that they can be combined into larger artifacts, often recursively. This is illustrated in Fig. 6.1.1. By defining code to generate modules of arbitrary complexity on demand, we can write surprisingly compact code and still implement complex neural networks. Fig. 6.1.1 Multiple layers are combined into modules, forming repeating patterns of larger models.¶

One factor behind deep learning’s success is the availability of a wide range of layers that can be composed in creative ways to design architectures suitable for a wide variety of tasks. For instance, researchers have invented layers specifically for handling images, text, looping over sequential data, and performing dynamic programming. Sooner or later, you will need a layer that does not exist yet in the deep learning framework. In these cases, you must build a custom layer. In this section, we show you how. To start, we construct a custom layer that does not have any parameters of its own.

This should look familiar if you recall our introduction to modules in Section 6.1. The following CenteredLayer class simply subtracts the mean from its input. To build it, we simply need to inherit from the base layer class and implement the forward propagation function. Let’s verify that our layer works as intended by feeding some data through it. We can now incorporate our layer as a component in constructing more complex models. As an extra sanity check, we can send random data through the network and check that the mean is in fact 0.

Because we are dealing with floating point numbers, we may still see a very small nonzero number due to quantization. In tab_intro_decade, we illustrated the rapid growth of computation over the past two decades. In a nutshell, GPU performance has increased by a factor of 1000 every decade since 2000. This offers great opportunities but it also suggests that there was significant demand for such performance. In this section, we begin to discuss how to harness this computational performance for your research. First by using a single GPU and at a later point, how to use multiple GPUs and multiple servers (with multiple GPUs).

Specifically, we will discuss how to use a single NVIDIA GPU for calculations. First, make sure you have at least one NVIDIA GPU installed. Then, download the NVIDIA driver and CUDA and follow the prompts to set the appropriate path. Once these preparations are complete, the nvidia-smi command can be used to view the graphics card information. In PyTorch, every array has a device; we often refer it as a context. So far, by default, all variables and associated computation have been assigned to the CPU.

Typically, other contexts might be various GPUs. Things can get even hairier when we deploy jobs across multiple servers. By assigning arrays to contexts intelligently, we can minimize the time spent transferring data between devices. For example, when training neural networks on a server with a GPU, we typically prefer for the model’s parameters to live on the GPU. You might have noticed that a MXNet tensor looks almost identical to a NumPy ndarray. But there are a few crucial differences.

One of the key features that distinguishes MXNet from NumPy is its support for diverse hardware devices. 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.

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