Deep learning is a part of the machine learning family that deals with representational learning as artificial neural networks. Deep learning is an artificial intelligence (AI) function that tries to mimic the working of the human brain in processing data and forming patterns for use in decision making. Deep learning is actually a subset of machine learning in artificial intelligence that consists of networks capable of unsupervised learning from unstructured or unlabeled data, also known as deep neural learning or deep neural networks.
So the fact here is that Deep Learning is a subset of Machine Learning whereas Machine Learning is a subset of Artificial Intelligence. So first let’s start with knowing a little more about artificial learning.
Artificial learning is a branch of science in which we try to impart human and animal intelligence to machines. In order to make machines intelligent to reduce human efforts and ease their day-to-day tasks, AI came into play. In the process, the machines are taught everything about human behavior such as how they talk, what languages they know and everything else.
Now, let us see what machine learning is. Machine learning is nothing but the study of computer algorithms that improve themselves with the use of experience and data. Machine learning algorithms actually build a model based on a sample of data, also known as “training data”, in order to make predictions or decisions without actually being programmed to do so.
History of Deep Learning
The origins of deep learning and neural networks date back to the 1950s when a British computer scientist and mathematician named Alan Turing predicted the future existence of supercomputers with human intelligence, and then scientists began to gradually simulate the human brain.
Let’s go back in time and look at the timeline of deep learning development.
|1943||The first mathematical model of a neural network|
|1950||The prediction of machine learning|
|1952||First machine learning programs|
|1957||Setting the foundation of deep neural networks|
|1959||Discovery of simple cells and complex cells|
|1965||The first working deep learning networks|
|1979-80||How to recognize visual patterns|
|1982||The creation of the Hopfield network|
|1985||A program which learned to produce English words|
|1986||Improvement in shape recognition and word prediction|
|1989||Machine reads handwritten digits|
|1993||A deep learning task is solved|
|1995||Support vector machines|
|1997||Long-short term memory was proposed|
|2009||Launch of ImageNet|
|2011||Creation of AlexNet|
|2012||The Cat Experiment|
|2014||Generative Adversarial Network (GAN)|
|2016||Powerful Machine Learning products|
How deep learning works
Deep learning neural networks, or artificial neural networks, attempt to mimic the human brain through a combination of data input, weights, and bias. These elements work together to accurately identify, classify and describe the objects within the data.
Neural networks are layers of nodes, just like the human brain is made up of neurons. Nodes within individual layers are connected to nearby layers. The network is said to be deep based on the number of layers. A single neuron in the human brain obtained thousands of signals from other neurons.
In an artificial neural network, signals run between nodes and assign the corresponding load. A heavily loaded node will have more impact on the next layer of nodes. The last layer compiles the loaded inputs to produce an output. Deep learning systems need strong hardware as they have a large quantity of data to be processed and involve many difficult mathematical calculations. However, with such advanced hardware, deep learning training computation can take weeks.
Deep learning systems require large amounts of data to produce accurate results; Accordingly, the information is fed in the form of huge data sets. When processing data, artificial neural networks are able to classify data with answers derived from a series of binary true or false questions involving highly difficult mathematical calculations.
For example, a facial recognition program learns to detect and identify the edges and lines of faces, then the more significant parts of the faces, and finally, the overall representation of the faces. Over time, the program trains itself, and the probability of right answers increases. In this case, the facial recognition program will correctly identify faces over time.
Deep Learning Applications
Virtual assistants are cloud-based applications that understand language voice commands and perform tasks for the user. Amazon Alexa, Cortana, Siri and Google Assistant are typical examples of virtual assistants. They have internet-connected tools to explore their full potential. Whenever a command is given to the assistant, they tend to provide a superior user experience backed by past experiences using deep learning algorithms.
Chatbots can solve customer problems in seconds. A chatbot is an AI application to talk online via text or text-to-speech. It is able to communicate and act almost like a person. Chatbots are used for customer interaction, marketing on social network sites and instant messaging to clients. It responds automatically to user input. It uses machine learning and deep learning algorithms to obtain different types of responses.
Deep learning has found its application in the healthcare sector. Computer-Aided Disease Detection and Computer-Aided Diagnosis are possible using Deep Learning. It is widely used for the diagnosis of life-threatening diseases such as cancer and diabetic retinopathy through the method of medical research, drug discovery, and medical imaging.
Companies such as Netflix, Amazon, YouTube and Spotify provide relevant movies, songs and video recommendations to reinforce their customer experience. All this is often due to deep learning. Backing up a person’s browsing history, interests, and behavior, online streaming companies offer tips to help make product and repair choices. Deep learning technology also won’t add sound to silent movies and automatically generate subtitles.
News Aggregation and faux News Detection
Deep learning allows you to customize the news count on the personality of the readers. You will collect and filter news information according to social, geographic and economic parameters and therefore the individual preferences of the reader. Neural networks help develop classifiers that will detect fake and biased news and take it away from your feed. They also warn you of potential privacy breaches.
A machine can learn the notes, structure and patterns of music and start producing music independently. Deep learning-based generative models such as WaveNet are often not used to develop raw audio. Long short term memory networks help to receive music automatically. Music21 is a Python toolkit for computer-aided musicology. It allows us to train a system to develop music by teaching the fundamentals of music theory, producing musical samples, and studying music.
Significant progress has been observed in image colorization using deep learning. Image colorization is taking the input of a grayscale image and then making the output of a color image. ChromaGAN is an example of an image colorization model. A generative network is modeled in an adversarial model that learns to color by incorporating both a perceptual and semantic understanding of class distribution and color.
Deep learning is heavily used to make robots perform human-like tasks. Robots powered by Deep Learning use real-time updates to understand the obstacles in their path and pre-plan their journeys instantly. It is often used for carrying goods in hospitals, factories, warehouses, inventory management, manufacturing products, etc.
“Self-driving cars are a natural extension of proactive security and clearly something we should always be doing”. – Elon Musk.
One of the fascinating technologies, self-driving cars, are designed using deep neural networks at an advanced level, where these cars use machine learning algorithms. They detect objects around the car, the space between the car and other vehicles, the location of pedestrian paths, identify traffic signals, determine the position of the driver, etc.
For example, Tesla is the most trusted brand that brings automatic, self-driving cars to the market.
In advertising, Deep Learning allows customizing the user experience. Deep learning helps publishers and advertisers to increase the importance of ads and promote ad campaigns. This will enable the ad network to reduce costs by reducing the cost per acquisition of a campaign from $60 to $30. You can create data-driven predictive ads, real-time ad bidding, and target display ads.
Limitations of Deep Learning
Deep learning has its advantages but it also has some limitations which you should keep in mind. Deep learning learns only from observation, so you need more data while training the deep learning model, the accuracy of the deep learning model is less if you do not provide more data. And there is no guarantee with what accuracy your deep learning model will work.
Future Scope Of Deep Learning
The proliferation of the Internet is being used in every work and technology is being developed using Artificial Intelligence and Machine Learning. Deep learning is a very helpful technique to me. With the help of concepts like self-driving cars, fraud detectors have been successful. Therefore Deep Learning has a good scope in the future, it is possible to make a career in Deep Learning and Machine Learning field by learning it.
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Along with this you also see What are Computer Networks? Full Information.