Deep learning is a technique that allows computers to learn from data and make predictions. It’s been around for decades, but it has recently become popular because of its ability to solve problems that are difficult for traditional analytical methods.
It is used in areas like image recognition, speech and language processing, natural language understanding (NLU), computer vision, robotics, and many more. Deep learning can be applied to any problem where you have large amounts of data with which you want your algorithm to learn something complex about the data without being explicitly programmed by humans or having access only to some pre-defined features. The algorithms behind deep learning use neural networks as their base architecture, which are inspired by how a human brains work: they consist of multiple layers of nodes connected together through connections called synapses. Each layer learns from the information it receives using a set of rules known as an algorithm or model.
The most common type of deep learning network consists of three main parts: input layer (where we collect our training data), hidden layers (which process this data) and output layer (where we predict what will happen next). In each layer there are several nodes that do different tasks such as recognizing patterns in images or words in text documents; these nodes are called neurons because they act like individual nerve cells in a human's brain that receive inputs from other neurons and send outputs back into them via synapses. These neurons can also be connected together into groups known as convolutional layers or recurrent neural networks (RNNs).
The neurons in each layer can communicate with each other through connections called synapses, which are like electrical links between them. The strength of a synapse is determined by how much it has been activated by previous activity – if two neurons have received similar inputs then their synaptic weights will be close to zero and they won’t activate each other very strongly; however, if one neuron receives a strong input while another does not then its weight becomes stronger because it has been activated more than its neighbor. This means that different neurons in the network can learn from different parts of the data at the same time: for example, one neuron might learn about photos while another learns about text documents.
Intelligencia is partnered with such well-known software vendors as Adobe, Alteryx, Domo, SAS, Pegasystems, Qlik, Tableau, Vantana, HDS, Salesforce, and ITRS, as well as such uniquely casino-specific vendors as Casino Data Imaging, which produces a data visualization tool that helps casino executives understand a patron’s detailed activity on the casino floor. Intelligencia also understands how to integrate open source systems with commercial software, which allows our clients to cut down on data processing time as well as cost.
Intelligencia can help its clients understand the rapidly evolving analytics world, with AI and machine learning taking center stage. ROI must be considered when it comes to purchasing analytics and we can show you what's available in the market, as well as what should be avoided. Our clients call us 'trusted advisors' and we wear that moniker with great pride.
Cloud offerings today are anything but simple - private, public, hybrid, edge cloud - all of which have a unique purpose and raison d'etre. At Intelligencia, we can show you how to navigate through this tricky terrain, terrain that gets very costly very quickly if not done right. Do you go with a prepackaged option like Red Hat's Open Stack or will you build something from scratch on your own? So many choices. So many dead-ends. We're here to help.
Intelligencia can help you implement complex BI solutions from the ground up. We can help you understand your data in ways that will simplify your BI initiatives. Our data integration and BI experts can help you navigate the challenging data visualization world so that the data you're expecting to see is exactly what you see in your final dashboard.
The goal of data integration is to extract data from an operational system, transform it, merge it into new datasets, and then deliver it to an integrated data structure built for marketing, analytics, loyalty, and/or social media purposes. DI challengers are intensifying because of the increased demand to integrate machine data and support Internet of Things (IoT) and digital business ecosystem needs for analytical processes. It's a complex world. Don't go it alone.
CX solutions include aspects of CRM, loyalty, MCM, and even social. Implementations are highly complex, taking into account multiple source systems, strong data cleansing tools, detailed loyalty programs that track every dollar and every secret and non-secret loyalty point, as well as marketing systems that both send out digital content and track offers used.
Intelligencia can help businesses implement complex marketing and customer experience solutions with their legacy software or help you build them from scratch. We'll help you understand your data in ways that simplify your digital marketing initiatives. Our experts can show you how to augment your legacy data environment to add powerful real-time streaming and in-memory elements that will immediately be recognized by your customers.
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contact@intelligencia.co
505 Hennessy Road, Suite 613, Causeway Bay, Hong Kong
+852 5192 1277
Rua da Estrela, Macau
+853 6616 1033
www.intelligencia.co
Intellligencia is a Hong Kong- and Macau-based software consulting company that works specifically in the hospitality, gaming, fintech, esports, manufacturing, retail, sports betting, and travel industries. Although located in Asia, we work with clients as far away as North America, Mexico, India, Armenia, Australia, and the Philippines.
Address: 505 Hennessy Road, #613, Causeway Bay, Hong Kong
Phone: +852 5196 1277
Email: andrew.pearson@intelligencia.co
Website: www.intelligencia.co