Deep Learning and Neural Networks
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Deep Learning is a technical representation of how different networking systems work – it is somewhat similar to the processing of different neurons and networks in human brains. For example, when the amount of information starts increasing in networking systems, Machine Learning only copes up to it till a certain point, after which it quickly saturates. And that’s where Deep Learning comes into play. In simpler words, it can be defined as the sub-type of Machine Learning which is used to deal with comparatively larger datasets. 

Deep Learning has a strong history that looks back to 1958 – it was first discovered on the idea of how perceptron works. But here, the question arises “why was Deep Learning not practically executed during all those past decades?” For instance, currently, the DL algorithms are being researched and implemented in different areas more than ever. Well, the major reason is that the Machine Learning algorithms take up comparatively much fewer resources than Deep Learning algorithms. 

However, due to the increased growth and advancements in hardware (GPUs), software systems, and various types of data, today we need to practically execute Deep Learning to improve the networking models’ performance. These neural networks allow different machines to use algorithms and recognize data through clustering and labeling. For example, A training algorithm is particularly used to execute the neural network’s learning processes. But, as we have a large number of training algorithms for different purposes, you need to choose one for yourself according to the specifications and capabilities of your goals. 

The Engineering Applications of Neural Networks

According to the New York Times, August 2018, “The companies and government agencies that have begun enlisting the automation software run the gamut. They include General Motors, BMW, General Electric, Unilever, MasterCard, Manpower, FedEx, Cisco, Google, the Defense Department, and NASA.” Now, this could probably mean that we are just seeing the initial glimpse of neural network/AI executions transforming the world in our surroundings.  

Interestingly, neural network application has become an integral part of modern engineering, especially when it comes to high assurance networking systems like flight controls, power plants, chemical engineering, medical systems, automotive controls, etc.

Depending on the field, Deep Learning and neural networks have various applications in different industries. Here’s a quick overview of it:

  • Automotive: Neural networks help the automotive industry to improve the overall development of power trains, virtual sensors, guidance systems, various warranty activity analyzers, etc.
  • Aerospace: Deep Learning is used to detect different faults and simulations in aircraft control systems. It improves the overall auto-piloting tech and also enhances the path simulations. 
  • Manufacturing: Neural networks play an integral role in the manufacturing process of different chemical products, their chemical analysis, dynamic modeling, product designing, etc. The Deep learning algorithms allow the developers to enhance their inspection system and also the planning and management networking systems.
  • Electronics: Neural networks allow electronic experts to keep an eye on circuit chip layouts, machine vision analysis, non-linear modeling, and prediction of the code sequence. Deep learning algorithms also help with process control and overall voice synthesis.
  • Mechanics: Just like electronics, Deep Learning is also helpful in several areas of mechanics like systems modeling, condition monitoring, access controls, etc.
  • Robotics: In robotics, neural networking helps with forklift robots, trajectory controls, manipulator controllers, vision systems, etc.
  • Telecommunications: Telecommunication companies have a strong connection with neural networks, especially when it comes to providing ATM controls, and automated information services to the customers. It also helps to improve the overall payment processing and fault management system of different banks. Some other areas include handwriting, speech and voice recognition, etc.

However, you need to remember that you cannot access, or implement any of the neural networks without having stable internet connectivity at your workplace/home. So, always make sure that you are connected to a high-speed internet connection like Spectrum before you get your hands on any networking system. 

Besides its high-coverage internet connectivity, Spectrum also offers exclusive TV services to its users. So, just in case, you are looking for some reliable TV providers in town, don’t forget to check out the latest Select TV plan by Spectrum. It offers more than 125 channels to all its users at only $49.99 per month, along with a 30-day money-back guarantee. 

How cool is that?

Types of Neural Networks

Algorithm Purpose
Autoencoder (AE) AEs are used to decrease the number of random variables that are under consideration for some reason so that your networking system gets an accurate representation of data sets and processes generative data models efficiently.
Boltzmann Machine (BM) Boltzmann Machine uses a recurrent neural networking system that is strong enough to learn internal data representation and solve high-end, challenging combined problems, etc.
Convolutional Neural Network (CNN) CNN is most commonly executed in the areas of visual imagery analysis, feed-forward neural networking systems, and minimizing the overall processing.
Deconvolutional Neural Network (DNN) DNNs allow the unsupervised formation of a hierarchical representation of images. Each level of these hierarchy group data creates the preceding level to increase the complexity of the image.
Deep Residual Network (DRN) DRNs help to handle the sophisticated DL models and tasks for the users. By adding multiple layers, a Deep Residual Network can easily prevent the degradation of results, etc.
Hopfield Network (HN) HN is a sub-type of recurrent artificial neural network. It can be defined as the associative memory system that consists of binary threshold nodes. Hopfield Networks provide an accurate model for analyzing, and understanding human memory.

 

The Bottom-Line

Due to the increased implementation of Deep Learning in various industries, we can surely expect the upcoming year to be a game-changer for overall neural networking systems. 

Although we have covered pretty much all the major factors related to DL in this article, still if you wish to know more about it then do not forget to check out this detailed guide by Smartsheet. 

However, before you click on the above-mentioned link, please ensure that you connect to a fast, and reliable internet connection like Spectrum so that you can enjoy a smooth browsing experience without lagging. 

If you wish to know more about Spectrum’s latest internet bundles, deals, or plans, do check out BuyTVInternetPhone. Or, simply connect with Spectrum’s customer services representative via their official helpline.