What is Deep Learning and Neural Networks in Artificial Intelligence?

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What is Deep learning?

The field of artificial intelligence is when machines can perform tasks typically requiring human intelligence. It comprises machine learning, where machines can learn by experience and acquire skills without human involvement. Deep learning is a subset of machine learning where, from large amounts of data, artificial neural networks, algorithms inspired by the human brain, learn. The deep learning algorithm would perform a task repeatedly, each time modifying it a little to enhance the result, similarly to how we learn from experience. We refer to “deep learning” because there are different (deep) layers in the neural networks that allow learning.

The amount of data we produce every day is overwhelming and it is the resource that makes deep learning real, currently estimated at 2.6 quintillion bytes. Since deep-learning algorithms need a lot of data to learn from, one explanation that deep learning capabilities have increased in recent years is this increase in data creation.

Deep learning algorithms benefit from the stronger computational resources available today, as well as the emergence of Artificial Intelligence (AI) as a service, in addition to more data production. Without a substantial initial investment, AI as a Service has provided smaller organizations access to artificial intelligence technology, and specifically the AI algorithms needed for deep learning. Deep learning helps computers to solve complex problems even while using a data set that is very broad, unstructured and inter-connected. The more algorithms learn about deep learning, the better they do.

What is Deep learning used for?

  • Customer experience. For chatbots, deep learning models have already been used. And, as it continues to evolve, in order to enhance customer service and improve customer loyalty, deep learning is expected to be introduced in different organizations.
  • Text generation. The grammar and structure of a piece of text is taught to computers and this model is then used to automatically generate a completely new text that matches the correct spelling, grammar and style of the original text.
  • Military and Aerospace. Deep learning is being used to detect satellite artifacts that classify areas of interest, as well as troops’ secure or dangerous zones.
  • Industrial automation. In environments such as factories and warehouses, deep learning enhances worker safety by offering services that automatically detect when a person or object is getting too close to a computer.
  • Color Adding. Using deep learning models, color can be applied to black and white images and videos.
  • Medical research. As a way to automatically identify cancer cells, cancer researchers have begun to integrate deep learning into their work.
  • Computer vision. Deep learning has greatly advanced computer vision, providing exceptionally accurate computers for object detection and classification, reconstruction and segmentation of images.

What is Neural Networks?

Generally, a neural network consists of a series of units or nodes connected. So-called weights, which are often nothing more than numerical values, know the relations between the neurons.

The weights between neurons shift as an artificial neural network learns, and so does the intensity of the relation Meaning: Given training data and a specific task such as number classification, we are looking for some fixed weights that enable the classification to be performed by the neural network.

For every job and every dataset, the array of weights is different. We cannot anticipate the values of these weights in advance, but they have to be learned by the neural network.

A neural network functions similarly to the neural network of the human brain. In a neural network, a “neuron” is a mathematical function which gathers and classifies information according to a particular architecture.

The network parallels mathematical approaches such as curve fitting and regression analysis in a strong way. A neural network comprises layers of nodes which are interconnected. Each node is a perceptron and is identical to a linear regression of multiples. The perceptron feeds the signal generated into an activation function by a multiple linear regression that may be a nonlinear.

In machine learning algorithms, neural networks are only one of the instruments and methods used. In several different machine learning algorithms, the neural network itself can be used as a part to process complex data inputs into a space that computers can understand. Neural networks, including image and speech recognition, and spam email filtering, to name a few, are being applied to many real-life issues today.

What are neural networks used for?

An artificial neural network (ANN) is a computer system designed to simulate how information is analyzed and processed by the human brain. It is the basis of artificial intelligence (AI) and resolves issues which, by human or statistical standards, would prove impossible or difficult. ANNs have self-learning capabilities that allow them, as more data is provided, to produce better results.

Data sets and task groups that are best evaluated using previously developed algorithms will still be available. It is not so much the algorithm that counts; it is the well-prepared data on the targeted predictor feedback that ultimately decides a neural network’s degree of performance.

Neural networks can be applied to a wide variety of issues, including images, videos, files, databases, and more, and can analyze several different forms of information. In order to understand the content of such inputs, they also do not require explicit programming.

There is practically no limit to the areas where this technique can be implemented because of the simplified approach to problem solving that neural networks bring. Some popular neural network applications today include image/pattern recognition, facial recognition, data mining, prediction of the trajectory of self-driving cars, filtering of email spam, and medical diagnosis.