Customer lifetime value is the metric that indicates the total revenue a business can reasonably expect from a single customer account. It considers a customer’s revenue value and compares that number to the company’s predicted customer lifespan. Businesses use this metric to identify significant customer segments that are the most valuable to the company.
Speech about what DL and ML are and how to use these technologies in the interests of contemporary life. And also about artificial neural networks based on CNN (Convolutional NN) and RNN (Recurrent NN) technologies, etc.
The volume of information is increasing every day. Despite this, Big Data functioning all over the world is only an insignificant part of it. According to IDC forecasts, the volume of information may reach 40 Zettabytes by 2020. From the beginning of 2010 to the present, the amount of data has increased 50-55 times. According to research, 2.8 Zettabytes of information currently exist and are processed. In his talk on Big Data, he will highlight fundamental issues in this area.
Nowadays, we can see the implementation of high-speed machine learning in all areas of society, such as trading, factoring, security, services, and others. We can see different examples of AI/ML’s implementation in journal publications, on TV shows, and on the Internet every day. We will analyze the pitfalls and possible obstacles in the implementation of AI/ML in BL through different examples.
In particular, the seminar discussed the technical parameters and trends in the production of cars driven without human intervention, the introduction of information and communication technologies in the automotive industry, in the ongoing scientific research and changes in the automotive industry. This event was organized interactively and aroused great interest among students.
The human brain is a biologically inspired class of algorithms that process the information network of primitive computational elements. Deep learning is a useful technology to build a multilayer neural network architectures and their applications in a broad class of problems. Deep learning is used for speech recognition, image analysis, video analysis, and behavior prediction text processing machine translation. Used static data processing has several disadvantages, like high labor costs, for developing specialized data processing systems. The deep learning achieves low labor costs due to the algorithms’ versatility, the ease of adaptation when you change the structure of the data, more data volume, and reliable data.