Machine-learning technology powers many aspects of modern society: from web searches to content filtering on social networks to recommendations on e-commerce websites, and it is increasingly present in consumer products such as cameras and smartphones. Machine-learning systems are used to identify objects in images, transcribe speech into text, match news items, posts, or products with users’ interests, and select relevant results of a search. Increasingly, these applications make use of a class of techniques called deep learning. Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example.
Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound. Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance. Models are trained by using a large set of labeled data and neural network architectures that contain many layers. Deep learning is a subset of machine learning. Usually, when people use the term deep learning, they are referring to deep artificial neural networks, and somewhat less frequently to deep reinforcement learning.
Deep is a technical term. It refers to the number of layers in a neural network. A shallow network has one so-called hidden layer, and a deep network has more than one. Multiple hidden layers allow deep neural networks to learn features of the data in a so-called feature hierarchy, because simple features (e.g. two pixels) recombine from one layer to the next, to form more complex features (e.g. a line). Nets with many layers pass input data (features) through more mathematical operations than nets with few layers and are therefore more computationally intensive to train. Computational intensity is one of the hallmarks of deep learning, and it is one reason why GPUs are in demand to train deep-learning models.
Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It’s achieving results that were not possible before.
Examples of Deep Learning at Work
Deep learning applications are used in industries from automated driving to medical devices.
Automated Driving: Automotive researchers are using deep learning to automatically detect objects such as stop signs and traffic lights. In addition, deep learning is used to detect pedestrians, which helps decrease accidents.
Aerospace and Defense: Deep learning is used to identify objects from satellites that locate areas of interest, and identify safe or unsafe zones for troops.
Medical Research: Cancer researchers are using deep learning to automatically detect cancer cells. Teams at UCLA built an advanced microscope that yields a high-dimensional data set used to train a deep learning application to accurately identify cancer cells.
Industrial Automation: Deep learning is helping to improve worker safety around heavy machinery by automatically detecting when people or objects are within an unsafe distance of machines.
Electronics: Deep learning is being used in automated hearing and speech translation. For example, home assistance devices that respond to your voice and know your preferences are powered by deep learning applications.
How Deep Learning Works
Most deep learning methods use neural network architectures, which is why deep learning models are often referred to as deep neural networks.
The term “deep” usually refers to the number of hidden layers in the neural network. Traditional neural networks only contain 2-3 hidden layers, while deep networks can have as many as 150. Deep learning models are trained by using large sets of labeled data and neural network architectures that learn features directly from the data without the need for manual feature extraction.
Figure 1 Neural networks, which are organized in layers consisting of a set of interconnected nodes
One of the most popular types of deep neural networks is known as convolutional neural networks (CNN or ConvNet). A CNN convolves learned features with input data and uses 2D convolutional layers, making this architecture well-suited to processing 2D data, such as images.
Difference between Machine Learning and Deep Learning
Deep learning is a specialized form of machine learning. A machine learning workflow starts with relevant features being manually extracted from images. The features are then used to create a model that categorizes the objects in the image. With a deep learning workflow, relevant features are automatically extracted from images. In addition, deep learning performs “end-to-end learning” – where a network is given raw data and a task to perform, such as classification, and it learns how to do this automatically.
Another key difference is deep learning algorithms scale with data, whereas shallow learning converges. Shallow learning refers to machine learning methods that plateau at a certain level of performance when you add more examples and training data to the network.
A key advantage of deep learning networks is that they often continue to improve as the size of your data increases.
Figure 2: Comparing a machine learning approach to categorizing vehicles (left) with deep learning (right)
In machine learning, you manually choose features and a classifier to sort images. With deep learning, feature extraction, and modeling steps are automatic.
 LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton, “Deep learning”, Nature 521, Number 7553 (2015): pp. 436-444.
 “What Is Deep Learning? 3 things you need to know”, available online at: https://in.mathworks.com/discovery/deep-learning.html
 “Artificial Intelligence, Machine Learning, and Deep Learning”, available online at: https://deeplearning4j.org/ai-machinelearning-deeplearning
 Navdeep, Ingestion, and Processing of Data for Big Data and IoT Solutions March 03, 2017, available online at: https://www.xenonstack.com/blog/ingestion-processing-data-for-big-data-iot-solutions