What Is Deep Learning & How Does It All Work?
Just a few decades ago, no one could imagine a world where machines would act and react exactly as human.. Today, Artificial Intelligence (AI), sometimes referred to as machine intelligence, has made this very phenomenon possible. One of the trendy topics that keep coming up in AI conversations is deep learning. So, what is deep learning? If you think that this is another fad that’s being used to push old-fashioned AI under a new fancy label, think again!
Deep learning is basically a subfield of machine learning that’s focused on algorithms influenced by the configuration and function of the brain, generally referred to as artificial neural networks. In other words, deep learning is an approach to artificial intelligence that is exhibiting great promise when it comes to creating autonomous, self-teaching systems.
How Deep Learning Works
Essentially, deep learning entails providing a computer system with sufficient data, which it can utilize to make decisions about other data. Data is normally fed through neural networks, which constitute logical constructions that extract numerical values or ask a series of binary true/false queries. As data is pulled from neural networks, and the true/false queries are returned, the deep learning process can make inferences from the results.
Deep learning can be applied to different forms of data, including written words, speech, audio, video and machine signals. With these data types, computer systems can provide answers or conclusions that seem like they’ve been given by humans. Not only do computer systems provide answers and conclusions with precision, but they also do it incredibly fast.
How Deep Learning Differs from Machine Learning
Machine learning is a sub-discipline of artificial intelligence. It acquires some of the core ideas of artificial intelligence and focuses them on deciphering problems with neural networks devised to mimic human decision-making. It is better thought of as the current state-of-the-art technology in the field of AI that the industry and society at large can use to drive change.
Deep learning, on the other hand, focuses even more intently on a subset of machine intelligence techniques and tools. Applying them to decipher problems that require human or artificial ‘thought’. As such, deep learning is thought of as the cutting-edge of cutting-edge.
Examples of How Deep Learning is Used
There is no doubt that deep learning is among the emerging technologies that are revolutionizing many industries. Many companies have adopted deep learning since its inception. The following are the notable companies that have joined the bandwagon:
- Google: The company uses the technology in its image and voice recognition algorithms.
- Netflix: The company uses it to enable viewers to decide what they want to watch next.
- Amazon: Uses it to enable buyers to decide what to buy.
- Gridspace: Uses it to drive sophisticated speech recognition systems.
- Nervana: Uses it to enable users to develop their own use-case deep learning networks.
- Deep Genomics: Uses it to forecast how both natural and therapeutic genetic variation alters cellular process such as RNA polyadenylation, gene splicing, and DNA-to-RNA.
There’s no question that deep learning is among the AI technologies that are at the top of the hype curve. As companies increasingly realize the incredible potential that results from unraveling data in whichever format it comes in, AI systems such as deep learning will become their ultimate solution.