Logo

THINK BIG

Analytics

Best Deep Learning software of 2020

#1

Microsoft Cognitive Toolkit (previously CNTK)

47%
#2

Deep Cognition

47%
#3

Keras

46%
#4

Amazon Rekognition

46%
#5

Neural Designer

46%
#6

Microsoft Computer Vision API

46%
#7

Clarifai

46%
#8

OpenCV

45%
#9

Microsoft Video API

45%
#10

Google Cloud Vision API

44%

Related to Deep Learning Software

Deep Learning Software Trends

Deep learning software gives you the tools to perform unsupervised machine learning on your data, allowing you to extract even the most nuanced insights and patterns. Deep learning software is being used (and developed) by almost every major company working with machine learning.

Small- to mid-size companies are also using deep learning software, especially as machine learning and AI become increasingly common in business intelligence and analytics applications. As a specific subset of machine learning, deep learning offers more specialized uses— particularly image categorization, clustering, and other applications of “unsupervised” learning.

The primary benefit of using deep learning software is having algorithms and neural networks pre-built for convenient use. In the early days of machine learning, data scientists had to build and train their neural networks, often with mixed results.

Thanks to deep learning software, these days are no more; now almost anyone can harness the power of deep learning and implement AI into their applications. As data pipelines grow larger and AI becomes increasingly crucial to business intelligence, deep learning software will become an essential part of analytics.

Why use deep learning software?

Deep learning software offers users the ability to integrate deep learning capabilities into their applications and analytics. Unlike other forms of machine learning, deep learning is unsupervised and non-linear, making it extremely useful for identifying patterns in otherwise unlabeled, “chaotic” datasets.

But just what is deep learning, and when is deep learning’s “unsupervised” structure superior to “supervised” types of machine learning? Read on to find out.

Supervised vs. unsupervised learning

Machine learning tasks usually fall into two major categories: “supervised” and “unsupervised.” The distinction, however, isn’t whether or not a data scientist is watching the screen as software crunches the data (that would be ridiculous!); instead, the difference between supervised and unsupervised learning lies in exactly how the algorithms classify data.

Consider, for example, a machine learning algorithm tasked with classifying pictures of pets into their respective species. So, given pictures of cats, dogs, birds, and other pets, the algorithm should be able to categorize cats as “cats,” dogs as “dogs,” and so on.

While both supervised and unsupervised learning can accomplish this task, there are a few key differences in how each learning style does so.

In supervised learning, machine learning algorithms compare uncategorized data against a set of pre-categorized data. By “measuring” the differences between each piece of uncategorized data and the pieces of pre-categorized data, supervised learning algorithms can perform reasonably accurate categorization and classification tasks.

In our pet categorization example, a supervised learning algorithm would use pre-categorized pictures of cats, dogs, and other species to compare with each new pet picture. With the “traits” of cats and dogs and other species already defined, the supervised learning algorithm analyzes the traits of each new picture and places them into the most “similar” category.

Note, however, that this ability has one major drawback: The accuracy of supervised learning depends on the quality of the pre-classified data. In other words, if there isn’t enough categorized data to compare against, then the algorithm won’t be able to classify as accurately.

As a result, many supervised learning algorithms require thousands – if not millions – of individual data points to achieve high levels of accuracy. Further, categories are limited to those which are already defined; so, if our pre-categorized pet data includes cats, dogs, and dogs but not lizards, each lizard picture would remain unclassified.

Unsupervised learning and deep learning, by contrast, don’t need pre-defined categories. Instead, unsupervised learning assesses similarities and differences between each data point and “clusters” them into self-made categories.

Applies to the pet categorization example, an unsupervised learning (or deep learning) algorithm would eventually recognize the similarities between cats (e.g. pointy ears, whiskers, etc.) and those between dogs (e.g. floppy ears, pointed noses, etc.), and place them into distinct categories. The algorithm may have no true understanding of what a “dog” or a “cat” is, but it would be able to recognize the visual distinctions.

This capability of unsupervised learning is what makes it such a powerful tool; without the need for large amounts of pre-categorized data, deep learning allows for newfound flexibility and accuracy in many machine learning applications.

More results from less data

As we discussed in the previous section, deep learning requires much less data than traditional machine learning approaches such as supervised learning. Since deep learning doesn’t need massive, pre-labeled datasets to effectively categorize raw data, it’s an ideal solution for those with smaller amounts of data.

This same capability is also useful for analyzing abstract datasets, particularly those whose “categories” might be unknown or undeterminable. For example, astrophysicists have used deep learning to categorize asteroids into distinct types based on their physical properties. Thanks to deep learning, astrophysicists discovered that some types of asteroids fell into previously unrecognized categories.

Of course, the benefits of deep learning aren’t limited to classifying asteroids or pet species; business intelligence, for example, helps increase sales by using deep learning to identify previously unrecognized patterns in customer activity or demands. Had business intelligence used supervised learning, they might have missed crucial trends by assuming certain patterns.

Truly, any application involving making sense of abstract data can benefit from deep learning software—even if the data sets aren’t very large or very clear.

Effortless, pre-built neural networks

Both deep learning and traditional machine learning (e.g. supervised learning) utilize neural networks. Neural networks are biologically-inspired architectures that attempt to replicate how the human brain processes information.

Even though neural networks are still only abstractions of true neural function, they’ve proven extremely useful for most applications of machine learning and AI. Instead of traditional programming logic utilizing sequences of “true” and “false,” neural networks offer increased flexibility and more abstract processing.

In any case, building neural networks from scratch is a difficult and time-consuming task, with many engineers and scientists devoting their entire careers to it. Thankfully, once built, many neural networks are easily tailored to specific tasks—allowing you to avoid the process of building them from scratch or hiring expensive specialists.

Deep learning software offers this same benefit, which is especially useful since deep learning neural networks are often far more complicated. With deep learning software, you can easily deploy time-tested neural networks to whatever task you need. In most cases, these neural networks will gradually adapt to your specific tasks and become more efficient over time.

Better insights

Extracting insights using supervised learning and other traditional machine learning methods often comes with limitations. Since these learning styles use pre-labeled data to analyze and “understand” raw data, the results are often limited.

Although the “limited results” of supervised learning are often acceptable for many machine learning tasks, they become truly limiting when trying to identify new trends and patterns.

Consider the pet species categorization example from earlier: Supervised learning would categorize pet pictures based on a set of pre-labeled pet pictures, thereby limiting the results to those pre-labeled pictures. If a pet picture came up that wasn’t of the same species as any in the pre-labeled pictures, then the algorithm could misclassify the pet (i.e., lizards aren’t cats!).

By contrast, deep learning software makes its categories based on similarities and differences that it identifies on its own. In the pet categorization example, deep learning software would correctly categorize cats as cats and dogs as dogs, but would also recognize that lizards are neither cats nor dogs.

This capability proves useful for many datasets, especially those whose patterns and insights are uncertain to analysts. As a result, deep learning software offers insights far greater than those of other machine learning applications.

Essential to any type of classification/image recognition

As we’ve seen in the previous examples, deep learning’s “clustering” abilities make it ideal for many classification and recognition tasks. These abilities are quickly proving useful for applications of machine learning where some “clusters” of data might be unpredictable.

One example of such an application is self-driving cars, which use machine learning to recognize road signs and obstacles. Here, object recognition is crucial: If the car’s software can’t recognize certain object on the road (such as certain animals or pedestrians), then it could cause a dangerous collision.

To help avoid collisions, engineers and programmers spend months training onboard neural networks to recognize various objects. As the cars continue to drive, their neural networks continue to improve as new objects are recognized and “learned.”

This capability is only truly possible with deep learning. While it can be achieved using supervised learning and other traditional machine learning methods, it’s a risky prospect; what if the car encounters something that its engineers didn’t expect?

Such “traditionalist” approaches would have to be capable of handling unrecognized objects. Engineers and programmers could spend months trying to think of every possible thing the car could encounter, but their lists would inevitably fall short at one point—who knows what a car could encounter on the road?

Of course, these same principles apply to other classification problems. Whether it’s sorting pet species or recognizing road obstacles, deep learning software is becoming ideal for most recognition tasks—especially those with unpredictable outcomes.

Essential to the future of analytics and intelligence

In the previous sections, we’ve outlined many examples showcasing the usefulness of deep learning. These examples share a common theme: Deep learning performs “unsupervised” learning to identify trends and patterns on its own, all without the need for large, pre-labeled datasets.

As companies being to gather more nuanced data, data pipelines are growing larger and more complex. While traditional forms of analysis are still capable of processing this data, they may be missing out on important new insights and trends. Even traditional methods of machine learning could be missing out, especially those which pre-assume the nature of the insights they want to extract.

With new trends and insights often being unpredictable, deep learning software is quickly becoming the favorite – if not the only ­– solution for analytics and business intelligence. Of course, analytics and business intelligence aren’t the only applications of deep learning.

Who uses deep learning software?

Deep learning software is used by anyone looking to identify trends in datasets—particularly trends which might not have been expected. While this capability has made deep learning software particularly useful for data science and analytics, deep learning is also benefitting other areas as well.

Data Scientists and Analysts

By definition, data science is not necessarily machine learning or, by extension, artificial intelligence; as a practice, data science has existed for far longer than computers, let alone machine learning. However, machine learning and AI are often the “brains” behind many modern data science applications.

Data science is, simply put, the practice of identifying trends and patterns in data. Traditionally, this practice was done “by hand” on datasets which would be laughably small by today’s standards. Today, doing any sort of robust data science without computers or machine learning would be highly impractical, if not impossible.

As a result, machine learning has become the primary driver of identifying patterns and making predictions from data. With this capability, businesses can use customer data to predict future purchases or subtle changes in demand—just to name a few possibilities!

Where machine learning has been the primary driver of this capability, deep learning is now the next stage of its evolution. As data scientists are tasked with analyzing larger amounts of complex data, even traditional forms of machine learning are becoming limited in their capabilities.

By contrast, deep learning software gives data science and analysts all the tools they need to identify even the most nuanced patterns and predict previously unexpected events.

Business Intelligence and Analytics

Today’s business intelligence and analytics often work as extensions of data science, with data science itself forming their bases. Since deep learning and machine learning form the basis of data science, then it’s only reasonable to conclude that business intelligence and analytics are, by extension, based on machine learning!

This case isn’t always true, of course— especially considering more traditional forms of business intelligence and analytics. Like with data science, these tasks were previously performed “by hand” using limited data sets; and, also like with data science, these tasks are becoming increasingly improved thanks to advances in deep learning.

For some small- to mid-size businesses, using machine learning and other “advanced” methods of business intelligence and analytics might seem like overkill—and, for some, it probably is. However, as more businesses become data-driven, using deep learning software and other machine learning tools will not only be inevitable but also more accessible than ever before.  

Software Engineers and Programmers

Behind data science, behind business intelligence and analytics, and behind machine learning and AI are teams of software engineers and programmers. Although these are often the very people who develop these tools for our use, some engineers and programmers are improving their applications and products by integrating deep learning software.

Software engineers and programmers don’t always have the skills to develop deep learning software on their own—and, frankly, nor should they. As these technologies become increasingly advanced, their development will likely be delegated to small, highly-specialized teams of AI engineers. Meanwhile, software developers and programmers will be able to reap the benefits of seamless deep learning integration.

Such integrations of deep learning into the software will become even more essential as data becomes crucial to many applications. In any case, developers and programmers will have to become more familiar with deep learning software if they want their applications to work with modern data.

…And anyone else analyzing large amounts of data.

Deep learning software makes it possible to identify subtle patterns and unexpected trends of any size datasets. Whether you’re a data scientist, a business analyst, a software engineer, or anyone working with data, deep learning software can give you the upper edge on analytics that modern data requires.

Despite its myriad of uses, however, most deep learning software shares a few common features.

Features

Deep learning software should work with your existing platforms. While most deep learning software packages are compatible with multiple platforms, you’ll want to make sure that the package you choose is compatible with your existing platforms. Some deep learning software is only compatible with single platforms, such as how BigDL is made exclusively for Apache Spark.

However, most deep learning software is cross-platform, offering compatibility with Linux, Windows, macOS, and sometimes more specific platforms.

Deep learning software should utilize a language you and your team are familiar with. Just like with platforms, deep learning software normally utilizes a handful of programming languages. However, some are a bit more specialized: MATLAB’s Deep Learning Toolbox, for example, is made specifically for the MATLAB programming language, whereas Flux is made specifically for the Julia programming language.

As a result, you’ll want to choose your deep learning software based on either your existing platforms or those you wish to adopt. In most cases, deep learning software normally uses either Python or C++ as an interface, with other languages such as Java, R, and MATLAB following closely behind.

Deep learning software should (usually) offer pre-trained models. Despite its robustness, convenience is key when it comes to deep learning software. As a result, your deep learning software of choice should almost always include pre-trained models, which will allow you to perform many deep learning tasks “out of the box.”

Intel is the notable exception to this rule, with their Data Analytics Acceleration and Math Kernel libraries offering no pre-trained models as of June 2020. However, given that most people using these libraries are building more “customized” solutions, having pre-trained models is beside the point in this case.

Deep learning software should offer either recurrent or convolutional neural networks (but ideally both!). Recurrent and convolutional neural networks are crucial to many deep learning tasks, though some tasks might favor one over the other. In any case, your deep learning software should ideally offer both types of neural networks, unless you have a very specific application in mind.

Deep learning software should offer other features and neural networks specific to your applications. Specific tasks such as probability distributions are normally left to specific neural networks, such as Restricted Boltzmann Machines (RBMs) and Deep Belief Networks (DBNs) for probability distributions. While most deep learning software is robust enough to include more specific networks such as these, they aren’t as common as recurrent or convolutional neural networks. As a result, you’ll want to be sure that your deep learning software of choice offers these specific capabilities.

Deep learning software usually offers parallel execution capabilities. Parallel execution allows multiple CPUs to power a single database operation, which is often necessary for many large-scale machine learning operations. Thankfully, most deep learning software offers this capability.

FAQ

Q: What is deep learning?

A: Deep learning is a type of machine learning which uses neural networks to analyze data. Common applications of deep learning include machine vision, language recognition, self-driving cars, and more.

Q: What are some popular examples of deep learning software?

A: Some of the most popular examples of deep learning software include TensorFlow, PyTorch, and MATLAB’s Deep Learning Toolbox. Some preexisting analytics tools, such as Wolfram Mathematica, also offer deep learning capabilities.

Q: What are the fundamentals of deep learning?

A: In most cases, deep learning uses biologically-inspired neural networks to identify the high-level features of raw data. While the types of neural networks can vary, most perform what’s known as “representation learning” to identify these high-level features.

Q: What is the process for getting started with deep learning?

A: Most deep learning software offers out-of-the-box deep learning and analytics solutions. These are normally supported on most common platforms, such as Linux and Windows.

Q: What kind of deep learning infrastructure do I need?

A: At a minimum, deep learning infrastructure requires a database and/or data storage solution (such as Hadoop), plus a computer equipped with deep learning software and enough processing power to support your specific tasks.

Q: What are some deep learning specialization areas?

A: Specialized examples of deep learning include computer and machine vision, language processing, and image recognition. Some deep learning software is also being used for more specialized uses such as drug development.

Recap

Deep learning software provides you with the tools necessary to generate insights and predictions from raw data. While deep learning can perform both supervised and unsupervised learning, the unsupervised learning aspects of deep learning are making it an ideal solution for extracting previously unpredictable trends and patterns in data.