Understanding Deep Learning better

The term “Deep Learning” refers to a machine learning technique which trains computers to do what humans naturally do; that is, learning by example. It in fact is the key technology behind innovations such as the driverless cars. It is deep learning that enables these cars to identify a stop sign or differentiate between a lamppost and a pedestrian.

Deep learning is also the key to voice control in consumer devices such as TVs, phones, hand-free speakers, and tablets. As a part of deep learning, computer models learn to execute classification tasks directly from texts, images, or sound. Such models can in fact achieve state-of-art accuracy, at certain times going beyond what is deemed a human-level performance. Models are taught by utilizing a big set of labeled data and neural network architectures which contain several layers.

Deep learning has of late been getting quite a lot of attention, with many students and professionals opting for online deep learning courses. And there’s good reason behind it. It has helped achieve results that were never before possible.

How does Deep Learning attain better results?

The one word answer to the question above is – accuracy. Deep learning accomplishes recognition results with levels of accuracy higher than those achieved before. Deep learning has as a matter of fact helped the consumer electronic industry meet user expectations. It has also proved to be vital for safety-critical applications such as driverless cars.

And now that deep learning has been improved to a point where it is outperforming humans, it seems rather odd that despite being first theorized in the 80’s, this only came into significance recently. However, there’s good reason behind that as well –

·         Deep learning needs vast amounts of labeled data. For instance, thousands of hours of video and millions of images are required for the development of driverless cars.

·         Substantial computing power is also required for deep learning. With their parallel architecture, high-performance graphics processing units are effective for deep learning.

How are Machine Learning and Deep Learning different?

Deep learning is actually a specialized form of machine learning. In machine learning, workflow begins with relevant aspects being manually extracted from images, which are then used to create a model for categorizing the objects within the images.

However, in deep learning, the relevant aspects are extracted automatically from the images. Besides, deep learning achieves “end-to-end learning” – where a network is fed with raw data and a task for performing, and it learns to do it automatically. Also, deep learning algorithms move up with data while shallow learning assembles.

Shallow learning is a machine learning method that plateaus at a specific level of performance, when more examples and training data are added to the network. One great advantage with deep leaning is that it improves as the size of data increases.

At present, every software based startup is looking for people with knowledge of deep learning.  So, for someone wanting to make it big in this field, taking an online deep learning course might actually be a very good idea.