GHEBTECH

  Machine Learning vs. Deep Learning The simplest and oldest technology is machine learning. It is built on an algorithm that changes the system based on human feedback. The use of this technology necessitates the existence of well-organized data. The system is then fed structured and categorized data in order for it to learn how to classify new data that is similar. The system then executes the predefined actions based on this classification. It may, for example, determine if a photograph depicts a dog or a cat and file the document in the appropriate folder. Structured data is not required for deep learning. The system employs multiple layers of neural networks that incorporate various algorithms inspired by the human brain. As a result, the system can work with unstructured data. This method is especially useful for difficult jobs in which not all aspects of the objects to be processed can be classified ahead of time. The discriminating traits are identified by the deep learning...

  We will discuss business strategy with Machine Learning and Deep Learning in this essay. In CRM, how are machine learning and deep learning used? Consider the following scenario: Up-selling and cross-selling will be discussed. Upselling is when you encourage a customer to purchase more of the same type of product than they had planned, whereas cross-selling is when you persuade a customer to purchase more of a different product than they had planned. In some circumstances, cross-selling is now considered part of up-selling. - Create a tagged dataset comprising up-sell, cross-sell, defer, and pull transaction information for customers. - Link the information to past conversations and online activities. - Correlate the data with other customers based on age, gender, occupation, where they live and work, and other information. Up-selling and cross-selling techniques include the following: Sell a model that is more expensive than the consumer anticipated, for as a smartphone with a l...

Artificial intelligence, machine learning, and especially deep learning technologies will contribute to the development and enhancement of all those important areas designated as a potential for the fourth industrial revolution in the future evolution of the industry. Nanotechnology, biotechnology, 3D printing, robots, autonomous cars, IoT, 5G, and smart devices, as well as cloud computing approaches, are all included. Deep Learning and Machine Learning can be very successful as a filter, as a controller, to collect information and put it into the Big Data analysis engine, because the types of data are so different. In addition, real-time estimations will be generated using Deep Learning and Machine Learning. These real-time predictions are based on data acquired in real time. Then there's Real-Time, IoT Data, Internet of Things, where global situation awareness, condition, and status of the environment are all incorporated into the system, and Deep Learning and Machine Learning ar...

Big data - Data science - Data mining - Machine learning - Deep Learning - Artificial neural networks. What are the various terminologies used in the world of data science?  Many terminologies in data science are interchangeable, let's take a look at the most prevalent ones. The term " Big Data " refers to datasets that are so large, fast-growing, and diverse that they defy typical analytical methods like those used with relational databases. Organizations now have the ability to examine these massive data sets because of the simultaneous development of immense processing capacity in dispersed networks and new data analysis tools and techniques. The five V's are frequently used to define big data: velocity, volume, variety, veracity, and value. Volume : Big Data refers to massive and ever-increasing amounts of data to store and process. Velocity : Big Data that has been optimized must deliver the right answer at the right moment and through the right channel. Variet...