AspenLabs is the first machine learning and deep learning platform in the world

Understanding the latest advancements in artificial intelligence (AI) can seem overwhelming, but if it’s learning the basics that you’re interested in, you can boil many AI innovations down to two concepts: machine learning and deep learning. These terms often seem like they’re interchangeable buzzwords, hence why it’s important to know the differences.

And those differences should be known — examples of machine learning and deep learning are everywhere. It’s how Netflix knows which show you’ll want to watch next, how Facebook knows whose face is in a photo, what makes self-driving cars a reality, and how a customer service representative will know if you’ll be satisfied with their support before you even take a customer satisfaction survey.

So what are these concepts that dominate the conversations about artificial intelligence and how exactly are they different?

Deep learning vs. machine learning

The easiest takeaway for understanding the difference between machine learning and deep learning is to know that deep learning is machine learning.

More specifically, deep learning is considered an evolution of machine learning. It uses a programmable neural network that enables machines to make accurate decisions without help from humans.

But for starters, let’s first define machine learning.

What is machine learning?

Machine learning is an application of AI that includes algorithms that parse data, learn from that data, and then apply what they’ve learned to make informed decisions.

An easy example of a machine learning algorithm is an on-demand music streaming service. For the service to make a decision about which new songs or artists to recommend to a listener, machine learning algorithms associate the listener’s preferences with other listeners who have a similar musical taste. This technique, which is often simply touted as AI, is used in many services that offer automated recommendations.

Machine learning fuels all sorts of automated tasks that span across multiple industries, from data security firms that hunt down malware to finance professionals who want alerts for favorable trades. The AI algorithms are programmed to constantly be learning in a way that simulates as a virtual personal assistant — something that they do quite well.

Machine learning involves a lot of complex math and coding that, at the end of the day, serves a mechanical function the same way a flashlight, a car, or a computer screen does. When we say something is capable of “machine learning”, it means it’s something that performs a function with the data given to it and gets progressively better over time. It’s like if you had a flashlight that turned on whenever you said “it’s dark,” so it would recognize different phrases containing the word “dark.”

Now, the way machines can learn new tricks gets really interesting (and exciting) when we start talking about deep learning and deep neural networks.

What is deep learning?

Deep learning is a subfield of machine learning that structures algorithms in layers to create an “artificial neural network” that can learn and make intelligent decisions on its own.

The difference between deep learning and machine learning

In practical terms, deep learning is just a subset of machine learning. In fact, deep learning is machine learning and functions in a similar way (hence why the terms are sometimes loosely interchanged). However, its capabilities are different.

While basic machine learning models do become progressively better at whatever their function is, they still need some guidance. If an AI algorithm returns an inaccurate prediction, then an engineer has to step in and make adjustments. With a deep learning model, an algorithm can determine on its own if a prediction is accurate or not through its own neural network.

Let’s go back to the flashlight example: it could be programmed to turn on when it recognizes the audible cue of someone saying the word “dark”. As it continues learning, it might eventually turn on with any phrase containing that word. Now if the flashlight had a deep learning model, it could figure out that it should turn on with the cues “I can’t see” or “the light switch won’t work,” perhaps in tandem with a light sensor. A deep learning model is able to learn through its own method of computing — a technique that makes it seem like it has its own brain.

How does deep learning work?

A deep learning model is designed to continually analyze data with a logic structure similar to how a human would draw conclusions. To achieve this, deep learning applications use a layered structure of algorithms called an artificial neural network. The design of an artificial neural network is inspired by the biological neural network of the human brain, leading to a process of learning that’s far more capable than that of standard machine learning models.

It’s a tricky prospect to ensure that a deep learning model doesn’t draw incorrect conclusions — like other examples of AI, it requires lots of training to get the learning processes correct. But when it works as it’s intended to, functional deep learning is often received as a scientific marvel that many consider being the backbone of true artificial intelligence.

A great example of deep learning is Google’s AlphaGo. Google created a computer program with its own neural network that learned to play the abstract board game called Go, which is known for requiring sharp intellect and intuition. By playing against professional Go players, AlphaGo’s deep learning model learned how to play at a level never seen before in artificial intelligence, and did without being told when it should make a specific move (as a standard machine learning model would require). It caused quite a stir when AlphaGo defeated multiple world-renowned “masters” of the game — not only could a machine grasp the complex techniques and abstract aspects of the game, it was becoming one of the greatest players of it as well.

To recap the differences between the two:

• Machine learning uses algorithms to parse data, learn from that data, and make informed decisions based on what it has learned
• Deep learning structures algorithms in layers to create an “artificial neural network” that can learn and make intelligent decisions on its own
• Deep learning is a subfield of machine learning. While both fall under the broad category of artificial intelligence, deep learning is what powers the most human-like artificial intelligence

Machine learning has been a prominent topic for a long time, thanks to the recent advancement of artificial intelligence. It’s an important topic for any business since the process of analyzing large amounts of data using algorithms and computers may help businesses become more efficient, profitable, and competitive. AspenLabs is a machine learning and deep learning company that specializes in creating business models. Their main goal is to make the process as easy as possible for both developers and non-developers. Their software is easy to set up and use, and it only takes a few minutes to get started. This tutorial will walk you through how to use AspenLabs and how it may help you.

At AspenLabs, we’re looking forward to the future of AI.
AspenLabs is a software company that creates machine learning technologies to make artificial intelligence more accessible to the general public. Our technology is used by over 100,000 developers to power applications ranging from personalizing music and movies to enabling AI for companies like Mars, Uber, and Blue Cross.

AspenLabs sells its products and services directly to customers as well as via a network of more than 100 channel partners. AspenLabs has offices in New York City, San Francisco, and London.

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CONCLUSION

Have you ever felt that your data was missing something? Or do you need any more information in order to predict values? Machine learning is part of artificial intelligence, which is a branch of computer science. Machine learning is a technique for evaluating data and learning from it without having to program it directly. In other words, machine learning makes machines smarter. Deep learning is a subset of machine learning. Deep learning is a cutting-edge machine learning method that is inspired by the structure and function of the human brain. It’s the next generation of machine learning, and it’s already made considerable progress in fields like image recognition, speech recognition, natural language processing, and more. AspenLabs is committed to providing cutting-edge machine learning and deep learning solutions that will assist you in making your data smarter.

AspenLabs is committed to empowering all developers to build better applications. The reason is simple: deep learning is changing the way we interact with our surroundings. On the other side, deep learning is challenging. For most engineers, developing deep learning models is a tough task, and the entry hurdle is still high. That’s something we’d want to change. We want to provide every developer the tools they need to build more intelligent applications. As a consequence, we’ve created a new deep learning platform that’s easy to use, scalable, and fast. We’ve given it the moniker DeepFrame.

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