3 Things You Must to Know About Deep Learning

Posted by Great Learning on May 17th, 2019

What is Deep Learning?

Deep learning is a technique used under machine learning which teaches computers to do what humans naturally do - learn by examples. Deep learning is the key technology behind driverless cars which enables them to recognize a stop sign or distinguish a pedestrian from a lamppost. It is also the key to voice control in consumer devices such as phones, tablets, TVs, and hands-free speakers. Deep learning has been getting a lot of attention lately, and for all the good reasons. It’s able to achieve results that weren’t possible before.

Deep learning models can achieve impeccable accuracy. It may sometimes even exceed the human-level of performance. You can take up an online Deep Learning course which will not only enhance your resume/CV but will also add new domains to your work profile.   

Why Deep Learning Matters?

It’s often asked how deep learning manages to attain such impressive results; the answer is, through accuracy. Deep learning has achieved the highest recognition accuracy so far. This has helped consumer electronics to meet user expectations and is also crucial for safety-critical applications such as driverless cars.

Although deep learning was first theorized in the 1980s, following are the two main reasons why it has become useful only recently:

• Deep learning requires large amounts of labeled data which wasn’t available back then.

• Substantial computing power is a prerequisite for carrying out deep learning. The recent developments of high-performance GPUs have a parallel architecture and are efficient for deep learning. When these are combined with clusters or cloud computing, they enable development teams to reduce training time for deep learning networks, from weeks to hours or even lesser.

The applications of Deep learning can be found in a number of industries, from automated driving to medical devices. Have a look at a few examples of deep learning at work:

• Automated Driving: Researchers in the automotive industry use deep learning to automatically detect objects such as stop signs and traffic lights. In addition to that, deep learning is also used to detect pedestrians, which helps decrease the rate of accidents.

• Aerospace and Defense: Deep learning is used to identify objects from satellites that locate areas of interest, and help in identifying safe or unsafe zones for troops.

• Medical Research: Cancer researchers use deep learning to detect cancer cells automatically. Teams at UCLA have built an advanced microscope that yields a high-dimensional data set used to train a deep learning application to accurately identify the cancer cells.

• Industrial Automation: Deep learning is actively helping improve worker safety around heavy machinery. This is being done by automatically detecting when people or objects are within an unsafe distance of the machines.

• Electronics: Deep learning is being used in fields like automated hearing and speech translation. For example, the home assistance devices you own respond to your voice and know your preferences; this is achieved by deep learning applications.

How Deep Learning Works?

Deep learning models are often referred to as deep neural networks, as most deep learning methods use neural network architectures.

The term “deep” usually refers to the number of layers that are hidden in the neural network. Traditional neural networks usually contain 2-3 hidden layers, whereas deep networks can have as many as 150 of them. Deep learning models are trained by using large sets of labeled data and neural network architectures that learn features directly from the data eradicating the need for manual feature extraction.

Deep Learning has already become a game changer in the field of machine learning, and the scopes of growth are immense. You should consider opting for a deep learning training course, as it can help you with your career growth in ways more than one.