Artificial Intelligence
Machine Deep Learning
A Few Words
Deep learning is a subset of machine learning, and machine learning is a subset of AI, which is an umbrella term for any computer program that does something smart. In other words, all machine learning is a form of AI, but not all AI is machine learning, and so forth.
GMX has executive know-how and can offer A.I as a cloud product
What does Artificial Neural Network (ANN) mean?
An artificial neural network (ANN) is a computational model based on the structure and functions of biological neural networks. Information that flows through the network affects the structure of the ANN because a neural network changes - or learns, in a sense - based on that input and output.
ANNs are considered nonlinear statistical data modeling tools where the complex relationships between inputs and outputs are modeled or patterns are found. ANN's are also known as a neural networks.
A neural network has several advantages but one of the most recognized is the fact that the model can actually learn using a range of tools from observing data sets.
In this way, ANN is used as a random function approximation tool.
These types of tools help estimate the most cost-effective and ideal methods for arriving at solutions while defining computing functions or distributions. GMX can assist you in choosing the right algorithms and use them as a way to take data samples rather than entire data sets to arrive at solutions, which saves both time and money.
Neural networks have three layers that are interconnected.
The first layer consists of input neurons.
Those neurons send data on to the second layer, which in turn sends the output neurons to the third layer.
Neural network learns from the analyzed data and does not require to reprogramming but they are referred to as black box” models, and provide very little insight into what these models really do. The user just needs to feed it input and watch it train and await the output.
Training an artificial neural network involves choosing from allowed models for which there are several associated algorithms.
Prebuilt images & containers for A.I
Use containers, machine learning to deploy portable, smart apps
The overhead of machine-learning systems can be huge. But today you have the option to place these systems in the cloud.
GMX supports machine learning compute resources from a range of GPU based hardware and a selection of supported deep learning frameworks, using these algorithms to read native data, and allowing our customers to provide AI services.
Keep in mind that machine learning technology in its current form is relatively new, and features and functions are always updated.
We can deploy and cluster machine-learning applications as containers.
That has several advantages, including:
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The ability to make machine learning applications self-contained. They can be mixed and matched on any number of platforms, with virtually no porting or testing required. Because they exist in containers, they can operate in a highly distributed environment, and you can place those containers close to the data the applications are analyzing.
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The ability to expose the services of machine learning systems that exist inside of containers as services or microservices. This allows external applications, container-based or not, to leverage those services at any time, without having to move the code inside the application.
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The ability to cluster and schedule container processing to allow the machine learning application that exists in containers to scale. You can place those applications on cloud-based systems that are more efficient, but it's best to use container management systems, such as Google's Kubernetes or Docker's Swarm.
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The ability to create machine learning systems made up of containers functioning as loosely coupled subsystems. This is an easier approach to creating an effective application architecture where you can do things such as put volatility into its own domain by using containers.
We also, provide pre-built images for several open-source operating systems.
GMX GPU compute for A.I
To make good use of neural network frameworks, it is important to have the right infrastructure backend. At GMX we have put together a compute family which has a backend that meets the requirements of most A.I frameworks. This has been designed with scalability and high availability in mind so you can rest assured that your project will run and scale without having to worry about it. We currently support Nvidia GPU's
Data scientists in both industry and academia have been using GPUs for machine learning to make groundbreaking improvements across a variety of applications including image classification, video analytics, speech recognition, and natural language processing.
In particular, Deep Learning is an area that has been seeing significant investment and research.
GPU's are used to train these deep neural networks using far larger training sets, in an order of magnitude less time, using far less data center infrastructure. GPU's are also being used to run these trained machine learning models to do classification and prediction in the cloud, supporting far more data volume and throughput with less power and infrastructure.
Multilayered Feed-Forward ANN Models
Recurrent ANN Models
Radial Basis Function ANN Models
Self-Organizing Maps
Recursive Neural Networks
Numerous successful practical applications
