Deep learning, or layered representations learning is a subfield of machine learning with an emphasis on learning successive layers of increasingly meaningful representations.
Therefore, the “depth” in deep learning comes from how many layers contribute to a model of the data (it’s common to have thousands of them).
These layered representations are learned via models called neural networks, structured in literal layers stacked on top of each other.
The term neural network is vaguely inspired in neurobiology, but deep-learning models are not models of the brain. There’s no evidence that the brain implements anything like the learning mechanisms used in modern deep-learning models. For our purposes, deep learning is a mathematical framework for learning representations from data.
What are Neural Networks?
Neural networks are a set of algorithms, they are designed to mimic the human brain, that is designed to recognize patterns. They interpret data through a form of machine perception by labeling or clustering raw input data.
Let’s take a moment to consider the human brain. Made up of a network of neurons, the brain is a very complex structure.
It’s capable of quickly assessing and understanding the context of numerous different situations. Computers struggle to react to situations in a similar way. Artificial Neural Networks are a way of overcoming this limitation.
Attributes of Neural Networks:
With the human-like ability to problem-solve — and apply that skill to huge datasets — neural networks possess the following powerful attributes:
- Adaptive Learning: Like humans, neural networks model non-linear and complex relationships and build on previous knowledge. For example, software uses adaptive learning to teach math and language arts.
- Self-Organization: The ability to cluster and classify vast amounts of data makes neural networks uniquely suited for organizing the complicated visual problems posed by medical image analysis.
- Real-Time Operation: Neural networks can (sometimes) provide real-time answers, as is the case with self-driving cars and drone navigation.
- Prognosis: NN’s ability to predict based on models has a wide range of applications, including for weather and traffic.
- Fault Tolerance: When significant parts of a network are lost or missing, neural networks can fill in the blanks. This ability is especially useful in space exploration, where the failure of electronic devices is always a possibility.
Use Cases of Neural networking:
Here’s a list of other neural network engineering applications currently in use in various industries:
- Aerospace: Aircraft component fault detectors and simulations, aircraft control systems, high-performance auto-piloting, and flight path simulations
- Automotive: Improved guidance systems, development of power trains, virtual sensors, and warranty activity analyzers
- Electronics: Chip failure analysis, circuit chip layouts, machine vision, non-linear modeling, prediction of the code sequence, process control, and voice synthesis
- Manufacturing: Chemical product design analysis, dynamic modeling of chemical process systems, process control, process and machine diagnosis, product design and analysis, paper quality prediction, project bidding, planning and management, quality analysis of computer chips, visual quality inspection systems, and welding quality analysis
- Mechanics: Condition monitoring, systems modeling, and control
- Robotics: Forklift robots, manipulator controllers, trajectory control, and vision systems
- Telecommunications: ATM network control, automated information services, customer payment processing systems, data compression, equalizers, fault management, handwriting recognition, network design, management, routing and control, network monitoring, real-time translation of spoken language, and pattern recognition (faces, objects, fingerprints, semantic parsing, spell check, signal processing, and speech recognition)
Improving Search Engine Functionality
During 2015 Google revealed they were working on improving their search engine. These improvements are powered by a 30 layer deep Artificial Neural Network. This depth of layers, Google believes, allows the search engine to process complicated searches such as shapes and colors.
Using an Artificial Neural Network allows the system to constantly learn and improve. This allows Google to constantly improve its search engine. Within a few months, Google was already noticing improvements in search results. The company reported that its error rate had dropped from 23% down to just 8%. Google’s application shows that neural networks can help to improve search engine functionality.
The field of neural networks and its use of big data may be high-tech, but its ultimate purpose is to serve people. In some instances, the link to human benefits is very direct, as is the case with OKRA’s artificial intelligence service.
“OKRA’s platform helps healthcare stakeholders and biopharma make better, evidence-based decisions in real-time, and it answers both treatment-related and brand questions for different markets,” emphasizes Loubna Bouarfa, CEO and Founder of Okra Technologies and an appointee to the European Commission’s High-Level Expert Group on AI. “In foster care, we apply neural networks and AI to match children with foster caregivers who will provide maximum stability. We also apply the technologies to offer real-time decision support to social caregivers and the foster family in order to benefit children,” she continues.
Like many AI companies, OKRA leverages its technology to make predictions using multiple, big data sources, including CRM, medical records, and consumer, sales, and brand measurements. Then, Bouarfa explains, “We use state-of-the-art machine learning algorithms, such as deep neural networks, ensemble learning, topic recognition, and a wide range of non-parametric models for predictive insights that improve human lives.”
Similar Artificial Neural Networks can be applied to the search feature on many e-commerce websites, product or service based organization to improve their result or performance.