A Complete Overview of Deep Learning

The term deep learning brings up several questions in the mind of those people who have never heard or worked using this technology. What is it, what benefits can it bring to tech-driven companies, and what is its technical background?

As a subset of artificial intelligence (AI), deep learning stands strong behind several enthralling innovations including self-driving cars, image recognition, voice recognition, and more. You might know that this powerful technology has covered multiple facets of human lives. Over the past few years, deep learning has started grabbing enormous attention and for good reason. Today, it is achieving results that were not even imaginable before. If reports of the Fortune, are to be believed, startups primarily focused on AI had raised around $7.5 billion in the second quarter of 2016. Such huge interest in both artificial intelligence(AI) and deep learning technologies is based on their advantages. So, let’s try to understand what exactly deep learning is.

The Definition of Deep Learning

Deep learning, also known as deep structured learning or hierarchical learning, is a part of machine learning (ML) concerned with algorithms that model high-level abstractions in data using architectures comprising various nonlinear transformations. In other words, you can say deep learning is a technology that is based on artificial neural networks(ANNs).These artificial neural networks are made more effective by continually growing the volume of data to improve the efficiency of learning processes. Always remember that the bigger data volumes are, the more accurate the process is.

Know How Deep Learning Works

A deep machine learning process mainly comprises of 2 phases: training and inferring. You can consider the training part as a laborious process of labeling large volume of data sets and identifying similar features. The system compares these similar features and learns them to make precise conclusions when it confronts similar data the next time.

A deep machine learning training process includes following stages:

• ·         Artificial neural networks ask a set of binary false/true questions
• ·         Extracting numerical values from data blocks
• ·         Labeling Data
• ·         Classifying data according to the answers received

During the inferring phase, deep learning makes inferences and labels new data using the same previous knowledge.

Advantages of Deep Learning

Advanced Analysis: Owing to its enhanced data processing models, deep learning produces actionable results when deciphering data science tasks. Unlike machine learning (ML) that works only with labeled data, deep learning facilitates unsupervised learning methods that enable the system to become intelligent on its own. The potential to identify significant features on its own allows deep learning to facilitate data scientists with brief and dependable analysis results.

Got amazed after knowing how deep learning is powering enthralling innovations? Planning to make a career in deep learning? If yes, take deep learning courses online from any good institute.