Deep Learning vs Machine Learning in Business
In recent years, the term “deep learning” has found its place in the business sector when discussing artificial intelligence, big data or analytics. Deep Learning is a promising approach in artificial intelligence. It can help us to develop autonomous self-teaching methods that can revolutionize industries.
Deep Learning is currently being used by large multinational companies around the world. One is Google, which uses the technology in its image and voice recognition systems. Netflix and Amazon use these algorithms in their recommendation and search systems. And MIT has given computers the capability to predict the future with the technology.
But let’s start from the beginning…
Deep Learning vs Machine Learning: The Main Differences
As Nvidia explained, the easiest way to differentiate AI, ML, and DL is to visualize them as concentric circles with AI. The first one forms the biggest circle. In this circle, we would find machine learning, and then the deep learning area, inside both of them.
Artificial Intelligence is one of the brightest keys for our future. In recent years, this technology has experienced an authentic revolution at all levels since 2015. The high availability of GPUs and the Big Data explosion have had a considerable impact on our prospects.
Artificial Intelligence allows us to program a computer to do something. Put simply, we tell the machine what to do. Although the Artificial Intelligence market is still small, we can find this technology in many applications of our daily life. This includes digital assistants, voice recognition systems, translation technologies, etc.
According to PWC, it is possible to define three basic forms of AI:
- Assisted: AI that improves what your business is already doing.
- Augmented: AI that enables your business to do things it couldn’t otherwise do.
- Autonomous: AI that acts on its own, on behalf of your business goals.
Let’s take a look at the main applications, i.e., current and future applications:
Machine Learning is described as a subset of AI. We provide a computer with data and the computer learns on its own. It improves tasks with experience. In other words, machines are given the information they need to do the job themselves.
This technology is showing great potential. It is providing indispensable tools for industris to move towards change. Alone, the machine trains itself to analyze data. It then learns from that data and forecasts possible futures.
These algorithms have been developed over years. Using decision trees, Inductive Logic Programming (ILP), clustering, reinforced learning, and more. Or using neural networks, which learn and behave in a remarkably similar way to human brains.
In this article, explained what machine learning is and its main business applications. These include facial, voice or object recognition, search engines, anti-spam, anti-virus, prediction and forecasts, reading comprehension, autonomous vehicles and robots, analysis of economic data, etc.
According to this, Machine learning sounds like state-of-the-art Artificial Intelligence, but let’s take go step -or circle- further. What’s Deep Learning?
Deep Learning is at the forefront of technology. It’s the idealized image of science fiction that so many of us have been dreaming of – or dreading – for so long.
Deep Learning is a subfield of machine learning that is inspired by the human brain. It teaches computers to do what comes naturally to humans. To this end, the software trains itself to perform tasks with a large set of labeled data which can be used to make decisions on other issues. This technology is possible thanks to a type of neural network called deep neural networks.
It can be used to add sound to silent films, classify objects in photographs, color black and white images, forecast energy market prices, detect cancer, and a ton of other applications.
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