Deep Learning can be considered as a subset of machine learning (ML). It is a field based on learning and improving on its own by examining computer algorithms. While machine learning uses more straightforward concepts, deep learning works with artificial neural networks designed to imitate how humans think and learn. Until recently, neural networks were limited by computing power and, thus, limited in complexity. However, advancements in Big Data analytics have permitted larger, sophisticated neural networks, allowing computers to observe, learn, and react to complex situations faster than humans. Deep learning has aided image classification, language translation, speech recognition. It can be used to solve any pattern recognition problem and without human intervention.

• Introduction to  Deep Learning
• What is the difference between Deep Learning vs. Machine learning (ML)?
• How does Deep Learning work?
• Deep learning applications

Introduction to Deep Learnin

Deep learning is a subset of machine learning, essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain—albeit far from matching its ability—allowing it to “learn” from large amounts of data. While a neural network with a single layer can still make approximate predictions, additional hidden layers can help to optimize and refine for accuracy.

Deep learning drives many artificial intelligence (AI) applications and services that improve automation, performing analytical and physical tasks without human intervention. Deep learning technology lies behind everyday products and services (such as digital assistants, voice-enabled TV remotes, and credit card fraud detection) as well as emerging technologies (such as self-driving cars).

What is the difference between Deep Learning vs. Machine learning (ML)?

Deep learning distinguishes itself from classical machine learning by the type of data that it works with and the methods in which it learns.

• Machine learning algorithms leverage structured, labeled data to make predictions meaning that specific features are defined from the input data for the model and
• organized into tables. This doesn’t mean that ML doesn’t use unstructured data; it means that ML generally goes through some pre-processing to organize data into a structured format.
• Deep learning eliminates some of the data pre-processing that is typically involved with machine learning. These algorithms can ingest and process unstructured data, like text and images, and it automates feature extraction, removing some of the dependency on human experts.

How does Deep Learning work?

Deep learning neural networks, or artificial neural networks, are layers of nodes, much like the human brain is made up of neurons. Nodes within individual layers are connected to adjacent layers. The network is said to be deeper based on the number of layers it has. A single neuron in the human brain receives thousands of signals from other neurons. In an artificial neural network, signals travel between nodes and assign corresponding weights. A heavier weighted node will exert more effect on the next layer of nodes. The final layer compiles the weighted inputs to produce an output. Deep learning systems require powerful hardware because they have a large amount of data being processed and involve several complex mathematical calculations. Even with such advanced hardware, however, deep learning training computations can take weeks.

Deep learning algorithms are incredibly complex, and there are different types of neural networks to address specific problems or datasets. For example:

• Convolutional neural networks (CNNs) are used in computer vision and image processing applications. They can detect features and patterns within an image and enable tasks like object detection or recognition. In 2015, a CNN bested a human in an object recognition challenge for the first time.
• Recurrent neural networks (RNNs) are used in natural language processing (NLP) and speech recognition applications to leverage sequential or time-series data.

Deep Learning applications

Because deep learning models process information in ways similar to the human brain, they can be applied to many tasks. Deep learning is currently used in most common image recognition tools, natural language processing (NLP), and speech recognition software. These tools are starting to appear in applications as diverse as self-driving cars and language translation services.

Specific fields in which deep learning is currently being used include the following:

• Customer experience (CX). Deep learning models are already being used for chatbots. And, as it continues to mature, deep learning is expected to be implemented in various businesses to improve CX and increase customer satisfaction.
• Text generation. Machines are being taught the grammar and style of a piece of text. They are then using this model to automatically create a completely new text matching the proper spelling, grammar, and style of the original text.
• Aerospace and military. Deep learning is being used to detect objects from satellites that identify areas of interest, as well as safe or unsafe zones for troops.
• Industrial automation. Deep learning improves worker safety in environments like factories and warehouses by providing services that automatically detect when a worker or object is getting too close to a machine. 
• Adding color. Color can be added to black-and-white photos and videos using deep learning models. In the past, this was an extremely time-consuming, manual process.
• Medical research. Cancer researchers have started implementing deep learning into their practice as a way to detect cancer cells automatically.
• Computer vision. Deep learning has dramatically enhanced computer vision, providing extreme accuracy for object detection and image classification, restoration, and segmentation.