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Everything You Need to Know About Deep Learning

Jul 23, 2018

Over the past couple of years, deep learning has strongly worked its way into the world of business. Often, deep learning is confused with machine learning and the terminologies are even used interchangeably. However, it is important to understand that both machine learning and deep learning are very different terms with independent definitions and functionalities.

What is deep learning?  

Deep learning is basically machine learning on a more in-depth level. This technology has taken inspiration from the way the human brain works. It requires high-end machines with distinct add-in graphics chips that are capable of crunching numbers and large volumes of data. In deep learning, a computer model learns to perform classification tasks directly from text, images, or sound. Interestingly, deep learning capabilities can achieve extremely high accuracy and precision, sometimes exceeding human-level performance. The models are trained by using a large set of labeled data and neural network architectures that contain many layers. Deep learning is a key technology behind one of the most recent technology trends that have slowly started to hit the rods – driverless cars. This technology enables cars to recognize a stop sign or to distinguish a pedestrian from a lamppost. They also are widely becoming popular for several other use cases such as the voice control in consumer devices like phones, tablets, TVs, and hands-free speakers.

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How is deep learning different from machine learning?

Unlike machine learning algorithms that break down problems into smaller parts and solve them individually, deep learning solves the problem from end to end. Also, the amount of data and time that has been fed to a deep learning algorithm is directly proportional to its efficiency at solving a task. Unlike machine learning, in deep learning, data isn’t provided for the program to use. Instead, all pixels within an image are scanned to discover edges that can be used to distinguish between different elements. Following this, the edges and shapes are sorted into a ranked order of possible importance to determine the different elements. Furthermore, this process requires more hardware to process the data generated by the algorithm. Programs using deep learnings algorithms also take longer to train compared to machine learning programs as they are learning on their own instead of relying on hand-fed shortcuts.

How does deep learning work?

This technique involves feeding a computer system with a lot of data, which later helps it to arrive at decisions regarding other data. This model is designed to continually analyze data with a logic structure similar to how a human would draw conclusions. This is achieved by using a layered structure of algorithms called an artificial neural network (ANN). This helps in creating machine intelligence that’s far more superior in terms of capability than that of standard machine learning models. Deep learning can be applied to any form of data including written words, video, audio, speech, and machine signals. They produce conclusions that are as accurate as the ones made by humans.


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What are some of the key applications of deep learning?

Deep learning has an array of impressive applications. Let’s take a look at some of the most prominent ones:

  • Self-driving cars – Though self-driving cars are in their early stages of testing, there have been several advanced developments in the way they have been performing. With deep learning, cars are learning to recognize obstacles and react to them appropriately with the help of sensors and onboard analytics.
  • Precision medicine – The field of healthcare has advanced to unfathomable levels. Now, healthcare professionals are looking at developing medicines that are genetically tailored to an individual’s genome by using deep learning techniques.
  • Recoloring black and white images – Black and white pictures and videos can be given back their original color by using this technology to teach computers to recognize objects and learn what they should look like to humans.
  • Gaming – Several gaming companies have been using deep learning capabilities. These systems have been taught to play and even win games.
  • Analysis and reporting – Deep learning systems are being used to analyze data and derive actionable and useful insights from them, accompanied with infographics which we can be easily comprehended.
  • Predicting the outcome of legal proceedings – Recently, a team of American and British researchers has developed a system that could correctly predict a court’s decision, when fed the basic facts of the case.
  • Cancer research – Cancer researchers are using this technology to automatically detect cancer cells. Researchers have built an advanced microscope that provides a high-dimensional data set that can be used to train applications to accurately identify cancer cells.

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