Deep Learning Frameworks

Deep learning is arguably the most popular aspect of AI, especially when it comes to data science (DS) applications. But what exactly are deep learning frameworks, and how are they related to other terms often used in AI and data science?


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In a nutshell, a system is a standalone program or script designed to accomplish a certain task or set of tasks. In a data science setting, a system often corresponds to a data model. However, systems can include features beyond just models, such as an I/O process or a data transformation process.

Deep Learning (DL) is a subset of AI that is used for predictive analytics, using an AI system called an Artificial Neural Network (ANN). Predictive analytics is a group of data science methodologies that are related to the prediction of certain variables. This includes various techniques such as classification, regression, etc. As for an ANN, it is a clever abstraction of the human brain, at a much smaller scale. ANNs manage to approximate every function (mapping) that has been tried on them, making them ideal for any data analytics related task. In data science, ANNs are categorized as machine learning methodologies.

At their cores, all DL frameworks work similarly, particularly when it comes to the development of DL networks. First, a DL network consists of several neurons organized in layers; many of these are connected to other neurons in other layers. In the simplest DL network, connections take place only between neurons in adjacent layers.

Having knowledge of multiple DL frameworks gives you a better understanding of the AI field. You will not be limited by the capabilities of a specific framework. For example, some DL frameworks are geared towards a certain programming language, which may make focusing on just that framework an issue, since languages come and go. After all, things change very rapidly in technology, especially when it comes to software. What better way to shield yourself from any unpleasant developments than to be equipped with a diverse portfolio of DL know-how?

As a set of techniques, DL is language-agnostic; any computer language can potentially be used to apply its methods and construct its data structures (the DL networks), even if each DL framework focuses on specific languages only. This is because it is more practical to develop frameworks that are compatible with certain languages, some programming languages are used more than others, such as Python. The fact that certain languages are more commonly used in data science plays an important role in language selection, too. Besides, DL is more of a data science framework nowadays anyway, so it is marketed to the data science community mainly, as part of Machine Learning (ML). This likely contributes to the confusion about what constitutes ML and AI these days.

Deep learning frameworks add value to AI and DS practitioners in various ways. The most important value-adding processes include ETL processes, building data models, and deploying these models. Beyond these main functions, a DL framework may offer other things that a data scientist can leverage to make their work easier. For example, a framework may include some visualization functionality, helping you produce some slick graphics to use in your report or presentation. As such, it’s best to read up on each framework’s documentation, becoming familiar with its capabilities to leverage it for your data science projects.

A DL framework can be helpful in fetching data from various sources, such as databases and files. This is a rather time-consuming process if done manually, so using a framework is very advantageous. The framework will also do some formatting on the data, so that you can start using it in your model without too much data engineering. However, doing some data processing of your own is always useful, particularly if you have some domain knowledge.

The main function of a DL framework is to enable you to efficiently build data models. The framework facilitates the architecture design part, as well as all the data flow aspects of the ANN, including the training algorithm. In addition, the framework allows you to view the performance of the system as it is being trained, so that you gain insight about how likely it is to overfit.

Model deployment is something that DL frameworks can handle, too, making movement through the data science pipeline swifter. This mitigates the risk of errors through this critical process, while also facilitating easy updating of the deployed model. All this enables the data scientist to focus more on the tasks that require more specialized or manual attention. For example, if you (rather than the DL model) worked on the feature engineering, you would have a greater awareness of exactly what is going into the model.

Deep learning is a very broad AI category, encompassing several data science methodologies through its various systems. As we have seen, for example, it can be successfully used in classification — if the output layer of the network is built with the same number of neurons as the number of classes in the dataset. When DL is applied to problems with the regression methodology, things are simpler, as a single neuron in the output layer is enough. Reinforcement learning is another methodology where DL is used; along with the other two methodologies, it forms the set of supervised learning, a broad methodology under the predictive analytics umbrella (see Appendix B).

DL frameworks make it easy and efficient to employ DL in a data science project. Of course, part of the challenge is deciding which framework to use. Because not all DL frameworks are built equal, there are factors to keep in mind when comparing or evaluating these frameworks.

Interpretability is the capability of a model to be understood in terms of its functionality and its results. Although interpretability is often a given with conventional data science systems, it is a pain point of every DL system. This is because every DL model is a “black box,” offering little to no explanation for why it yields the results it does. Unlike the framework itself, whose various modules and their functionality is clear, the models developed by these frameworks are convoluted graphs. There is no comprehensive explanation as to how the inputs you feed them turn into the outputs they yield.

Model maintenance

Maintenance is essential to every data science model. This entails updating or even upgrading a model in production, as new data becomes available. Alternatively, the assumptions of the problem may change; when this happens, model maintenance is also needed. In a DL setting, model maintenance usually involves retraining the DL network. If the retrained model doesn’t perform well enough, more significant changes may be considered such as changing the architecture or the training parameters. Whatever the case, this whole process is largely straightforward and not too time-consuming.

When to use DL over conventional data science systems

Deciding when to use a DL system instead of a conventional method is an important task. It is easy to be enticed by the new and exciting features of DL, and to use it for all kinds of data science problems. However, not all problems require DL. Sometimes, the extra performance of DL is not worth the extra resources required. In cases where conventional data science systems fail, or don’t offer any advantage (like interpretability), DL systems may be preferable. Complex problems with lots of variables and cases with non-linear relationships between the features and the target variables are great matches for a DL framework.

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