Artificial Neural Network and Deep Learning
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| Artificial Neural Network |
Artificial Neural network is a machine learning algorithm that mimics the human brain. The human brain consists of neurons as a basic unit of the brain. The artificial neural network consists of artificial neurons as a fundamental unit. These neurons are working for input, processing and output data in artificial neural networks. Artificial neural networks mainly consist of three layers, input, hidden and output layer. Input layers are used for data input, hidden layers are used for processing and output is used to show predictions.
Deep learning is a type of artificial neural
network which have more than one hidden layer. An artificial neural network with
more than one hidden layer is called deep neural network. Deep neural networks
are more efficient than artificial neural networks and machine learning
algorithms. Machine learning algorithms have limitations such as they need
manual feature engineering to get more accuracy and at the point machine
learning algorithms performance not improve instead of training with huge data however
deep learning performance increases as the data increases. Deep learning also consists
of many techniques which used to increase deep learning performance. Deep belief network, convolutional neural
network, recurrent neural network, deep autoencoders are different types of
deep learning.
Deep learning has many applications.
Some are discussed below.
COMPUTER VISION
Computer vision is the main area of research
and development of Deep Learning. Face recognition, face detection, object
detection, image tagging, image recognition, activity recognition are examples
of computer vision applications. Computer
vision is also used by Facebook and face detection feature is available for Facebook
users.
NATURAL LANGUAGE PROCESSING
Natural language processing is
another area where deep learning has been applied successfully. Natural language
processing is used to develop applications to solve problems related to natural
languages. Voice synthesis, sentiment analysis, language translation are some examples
of deep learning applications.
HEALTHCARE
Healthcare is a critical area where
deep learning is also used to solve problems. Cancer defection, tumor detection,
Disease detection and prediction, Medical Imaging are examples of deep learning
applications in healthcare.
SELF DRIVING CARS
Self driving car more complex
application of deep learning. Self driving cars are automatic, autonomous vehicles
which used AI more specifically Deep learning to learn from the environment to
drive the car with little human input or no input.
CYBERSECURITY
Computer networks and hosts produce big
data. This data can be used to train deep learning algorithm to solve cybersecurity
problems. Intrusion detection and prevention systems are mostly powered with
deep learning. IBM Security, Microsoft, Symantec have developed machine
learning and deep learning based security solutions.
Tags: Deep Learning, Artificial Neural Networks, Deep Learning Applications
