Data frame analytics lets you build your own models using built-in regression, classification, and outlier detection jobs. Now with version 7.6 we will show you how to build a model and use it for real-time streaming data via the new inference ingest pipeline.

Machine learning and neural networks are fast becoming pillars on which you can build intelligent applications. The course will begin by introducing you to Python and discussing using AI search algorithms. You will learn math-heavy topics, such as regression and classification, illustrated by Python examples. You will then progress on to advanced AI techniques and concepts, and work on real-life data sets to form decision trees and clusters. You will be introduced to neural networks, which is a powerful tool benefiting from Moore's law applied on 21st-century computing power.

We not only tackle the theory but give practical guidance and live demonstrations of the computational methods involved. After introducing the subject we cover Gaussian Process Regression and Artificial Neural Networks and show how such methods can be applied to solve option pricing problems, speed up the calculation of xVAs or apply them for hedging. We further show how to use existing pricing libraries to interact with machine learning environments often set up in Python. We explain how to set up the methods mainly in Python using Keras, Tensorflow or SciKit Learn. We give many examples which are directly related to financial mathematics and can be explored further after the course.

Data Science is about explaining the past and predicting the future by means of data analysis. Data Science is a multi-disciplinary field which combines statistics, machine learning, artificial intelligence and database technology. This course provides the essential concepts and principles in data science. Students learns commonly used classification algorithms and how to use those algorithms to solve real world problems.

Next generation networks will require a significant boost in performance, network throughput and agility to maximize the opportunities created by 5G at sustainable costs. At the virtual edge it is vital that platforms deployed today take full advantage of commercial off the shelf servers with NUMA balanced designs and multi-node scalability in order to reduce dependability on proprietary designs and lower TCO. This webinar addresses the architectural shortcomings found when disaggregating network, compute and storage from the upper-layer application function and how Intel Select Solutions can help Communication Service Providers (CommSPs) simplify network design choices with: - a balanced server reference design, where each NUMA node has identical capacity resources - a multi-node architecture which allows user-plane functions to be scaled out across multiple systems enabling a more efficient use of hardware resources for lower TCO - a commercial vBNG solution that supports increased subscriber density, while retaining highest throughput and cost efficiency per rack unit - unique remote vBNG evaluation and benchmarking services deployed in WWT's Advanced Technology Center Presented by: -Paul Stevens, Marketing Director, Cloud-IoT Group (Advantech) -David Williams, Senior VP Sales & Marketing (netElastic) -Obi Egonu, Practice Lead, Network Virtualization Systems (WWT) -Ai Bee Lim, Solution Architect (Intel)

In order to help build increasingly effective care pathways in healthcare, modern artificial intelligence technologies must be adopted and embraced. Events such as the AI & Machine Learning Convention are essential in providing medical experts around the UK access to the latest technologies, products and services that are revolutionising the future of care pathways in the healthcare industry. AI has the potential to save the lives of current and future patients and is something that is starting to be seen across healthcare services across the UK. Looking at diagnostics alone, there have been large scale developments in rapid image recognition, symptom checking and risk stratification. AI can also be used to personalise health screening and treatments for cancer, not only benefiting the patient but clinicians too – enabling them to make the best use of their skills, informing decisions and saving time.

In this tutorial you will learn how to use my pre-configured NVIDIA Jetson Nano .img If you've ever configured an NVIDIA product such as the TX1, TX2, and even the Nano, you know that working with NVIDIA's Jetpack and installing libraries is far from straightforward. It is developed and supported by my team here at PyImageSearch to save you time and bring you up to speed quickly for developing your own embedded CV/DL projects and for following along with my new book Raspberry Pi for Computer Vision. If you purchase a copy of the Complete Bundle of Raspberry Pi for Computer Vision, you'll gain access to this accompanying .img. All you have to do is (1) download the .img

Artificial Intelligence (AI) seems to be a unique technology of making a machine, a robot fully autonomous. AI is an analysis of how the machine is thinking, studying, determining and functioning when it is trying to solve problems. These so-called problems are present in all fields - the most emerging ones in 2020 and even beyond. The aim of Artificial Intelligence (AI) is to enhance machine functions relating to human knowledge, such as reasoning, learning and problems along with the ability to manipulate things. For example, virtual assistants or chatbots offer expert advice.

A comprehensive 5-Day bootcamp, delivered in person by industry experts, incorporating the essential components of data science & machine learning used in the world of business, with special focus on its application in business, selection and deployment of machine learning models and data visualisation techniques for communication and storytelling. The course introduces descriptive analytics and progresses to the use of predictive and prescriptive analytics in real world business scenarios. It is designed to prepare attendees, with or without the relevant theoretical or technical background, for the ever-changing world of business. For more information please go to links provided below.

In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After a general introduction, we motivate and contrast various graph-based data models and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs.