The story of Deep Learning
Deep learning (also known as deeply structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data. Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.
It was developed following the early Perceptron learning algorithm, which was limited in its ability to understand the ambiguity of “or” within natural language. To resolve this problem several layers of learning algorithms needed to be developed. There may a lot of layers in deep learning according to problem complexity. And in this algorithm, we can use large amount data to train system. Processing a large amount of data and having a large number of neurons-layers require high processor capacity. CPUs are inadequate for this job now. The system which wants to run deep learning needs much more CPU power.
Here is where the GPUs came into play.
GPU-accelerated computing is the use of a graphics processing unit (GPU) together with a CPU to accelerate deep learning, analytics, and engineering applications. GPUs play a huge role in accelerating applications in platforms ranging from artificial intelligence to cars, drones, and robots. (Read more).
A simple way to understand the difference between a GPU and a CPU is to compare how they process tasks. A CPU consists of a few cores optimised for sequential serial processing while a GPU has a massively parallel architecture consisting of thousands of smaller, more efficient cores designed for handling multiple tasks simultaneously. (Source)
The core of deep learning is that we now have fast enough computers and enough data to actually train large neural networks. That as we construct larger neural networks and train them with more and more data, their performance continues to increase. This is generally different to other machine learning techniques that reach a plateau in performance. This is the key point why deep learning has become so trending topic today. The representative figure is given below.
Deep learning methods aim at learning feature hierarchies with features from higher levels of the hierarchy formed by the composition of lower-level features. Automatically learning features at different levels of deliberation permit a system to learn complex functions mapping the input to the output directly from data, without depending completely on human-crafted features. An example of the working mechanism of deep learning is given below.
There are a lot of companies which are already starting to use deep learning. Explanations for the most famous ones are given the continuation of this section. .
Startup Deep Genomics, which is backed by Bloomberg Beta and True Ventures among others, has fed deep learning machines tons of existing cellular information in order to teach machines to predict outcomes from alterations to the genome, whether naturally occurring or through medical treatment. The technology could provide the most precise understanding of an individual’s specific disease or abnormality and how that person’s well-being can best be advanced.
More devices become internet-enabled, hackers have an increasing number of entry points to infiltrate systems and cloud infrastructure. The best cybersecurity practices not only create more secure systems but can predict where the next attack will come from. This is critical since hackers are always on the hunt for the next vulnerable endpoint, so protecting against cyber attack requires “thinking” like a hacker. Companies like Israel-based and Blumberg Capital-based Deep Instinct aim to use deep learning in order to recognize new threats that have never been detected before and thus keep organizations one step ahead of cyber criminals.
There are already plenty of cars on the road with driver-assistance capabilities, but these cars still rely on users to take over when an unforeseen event occurs that the car isn’t programmed to respond to. As Sameep Tandon of startup Drive.ai notes, the challenge with self-driving cars is handling the “edge cases,” such as weather. This is why, using deep learning, Drive.ai plans to help the car build up experience through simulations of many kinds of driving conditions. Nvidia is also working on self-driving car technology. Nvidia says it has used deep learning to train a car to drive on marked and unmarked roads and along the highway in various weather conditions, without the need to program every possible “if, then, else” statement. In this sector, Google and Many of the big companies in the automobile industry are doing research on driverless cars.
Since deep learning has already seen widespread experimentation and refinement for textual analysis, it’s no surprise that Google, the leader in search, has made widespread deep-learning-based updates to its search technology. Google’s deep-learning-based RankBrain technology was added to how Google manages and fills search queries back in 2015. The technology helps handle queries that have not been seen before.
So Apple moved Siri voice recognition to a neural-net based system for US users on that late July day (it went worldwide on August 15, 2014.) Some of the previous techniques remained operational but now the system leverages machine learning techniques, including types of deep learning. When users made the upgrade, Siri still looked the same, but now it was supercharged with deep learning.
(Some of the examples are taken from this link. If you want to read more, you may check this link also.)
