Become a better manager by swapping bodies with your staff to see how they see you. Immersive media and experiential learning are breaking new ground and now offer businesses and employers the ability to deploy core personnel training digitally, on demand, at scale and affordably. Companies such as London based Somewhere Else Solutions are disrupting the old corporate training market and building powerful, pioneering, soft skills training platforms powered by AI & VR. All business owners know soft skills are no longer a'nice to have' and that their staff need to be engaged, empathetic and flexible when working together and supporting customers. One-to-one professional development sessions and behavioural training used to be an expensive and time-consuming operation!

Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using model architectures. With the superb memory management and the full integration with multi-node big data platforms, the H2O engine has become more and more popular among data scientists in the field of deep learning. This book will introduce you to the deep learning package H2O with R and help you understand the concepts of deep learning. We will start by setting up important deep learning packages available in R and then move towards building models related to neural networks, prediction, and deep prediction, all of this with the help of real-life examples. After installing the H2O package, you will learn about prediction algorithms.

A common need when you are analyzing real-world data-sets is determining which data point stand out as being different to all others data points. Such data points are known as anomalies. This article was originally published on Medium by Davis David. In this article, you will learn a couple of Machine Learning-Based Approaches for Anomaly Detection and then show how to apply one of these approaches to solve a specific use case for anomaly detection (Credit Fraud detection) in part two. A common need when you analyzing real-world data-sets is determining which data point stand out as being different to all others data points.

Search, analyze, and visualize big data on a cluster with Elasticsearch, Logstash, Beats, Kibana, and more. Elasticsearch 7 is a powerful tool not only for powering search on big websites, but also for analyzing big data sets in a matter of milliseconds! It's an increasingly popular technology, and a valuable skill to have in today's job market. This comprehensive course covers it all, from installation to operations, with over 90 lectures including 8 hours of video.

Background. Most tutorial ontologies focus on illustrating one aspect of ontology development, notably language features and automated reasoners, but ignore ontology development factors, such as emergent modelling guidelines and ontological principles. Yet, novices replicate examples from the exercises they carry out. Not providing good examples holistically causes the propagation of sub-optimal ontology development, which may negatively affect the quality of a real domain ontology. Results. We identified 22 requirements that a good tutorial ontology should satisfy regarding subject domain, logics and reasoning, and engineering aspects. We developed a set of ontologies about African Wildlife to serve as tutorial ontologies. A majority of the requirements have been met with the set of African Wildlife Ontology tutorial ontologies, which are introduced in this paper. The African Wildlife Ontology is mature and has been used yearly in an ontology engineering course or tutorial since 2010 and is included in a recent ontology engineering textbook with relevant examples and exercises. Conclusion. The African Wildlife Ontology provides a wide range of options concerning examples and exercises for ontology engineering well beyond illustrating only language features and automated reasoning. It assists in demonstrating tasks about ontology quality, such as alignment to a foundational ontology and satisfying competency questions, versioning, and multilingual ontologies.

The model is significantly faster to train and to make predictions, yet still requires a set of candidate regions to be proposed along with each input image. Python and C (Caffe) source code for Fast R-CNN as described in the paper was made available in a GitHub repository. The model architecture was further improved for both speed of training and detection by Shaoqing Ren, et al. at Microsoft Research in the 2016 paper titled "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks." The architecture was the basis for the first-place results achieved on both the ILSVRC-2015 and MS COCO-2015 object recognition and detection competition tasks. The architecture was designed to both propose and refine region proposals as part of the training process, referred to as a Region Proposal Network, or RPN. These regions are then used in concert with a Fast R-CNN model in a single model design. These improvements both reduce the number of region proposals and accelerate the test-time operation of the model to near real-time with then state-of-the-art performance.

Microsoft's latest breakthrough could make DNA-based data centers possible

Columbia University is learning how to build and train self-aware neural networks, systems that can adapt and improve by using internal simulations and knowledge of their own structures. The University of California, Irvine, is studying the dual memory architecture of the hippocampus and cortex to replay relevant memories in the background, allowing the systems to become more adaptable and predictive while retaining previous learning. Tufts University is examining an intercellular regeneration mechanism observed in lower animals such as salamanders to create flexible robots capable of adapting to changes in their environment by altering their structures and functions on the fly. SRI International is developing methods to use environmental signals and their relevant context to represent goals in a fluid way rather than as discrete tasks, enabling AI agents to adapt their behavior on the go.

Regression, Classification and much more.HOT & NEW 4.8 (7 ratings) 161 students enrolled Created by Denis Panjuta What you'll learn Create machine learning applications in Python as well as R Apply Machine Learning to own data You will learn Machine Learning clearly and concisely Learn with real data: Many practical examples (spam filter, is fungus edible or poisonous etc. ...) No dry mathematics - everything explained vividly Use popular tools like Sklearn, and Caret You will know when to use which machine learning model This course contains over 200 lessons, quizzes, practical examples, ... - the easiest way if you want to learn Machine Learning. Step by step I teach you machine learning. In each section you will learn a new topic - first the idea / intuition behind it, and then the code in both Python and R. Machine Learning is only really fun when you evaluate real data. That's why you analyze a lot of practical examples in this course: Create machine learning applications in Python as well as R Apply Machine Learning to own data You will learn Machine Learning clearly and concisely Learn with real data: Many practical examples (spam filter, is fungus edible or poisonous etc. ...) No dry mathematics - everything explained vividly Use popular tools like Sklearn, and Caret You will know when to use which machine learning model Learn with real data: Many practical examples (spam filter, is fungus edible or poisonous etc. ...)

My name is Sahil Malik, and welcome to my course, Microsoft Azure Developer: Creating and Integrating AI with Azure Services. I have been talking for about 3 seconds, and in these 3 seconds YouTube has seen 15 hours of content uploaded, USPS has scanned thousands of handwritten addresses, and so many smartphones have taken pictures, cleverly adjusting contrast and brightness thanks to face recognition algorithms. The management of all this is thanks to AI in application around us. Think of how much data has your company produced in this time. Will it make you more productive? Will it make you less liable?