Asia Pacific Assistive Robotics Association (APARA), a non-profit organization founded to facilitate adoption and augmentation of Artificial Intelligence (AI) and Robotics, today announced AIBotics Go-Digital Series 2020, an AI and Robotics event themed around'Augmenting the Human Potential', will be launched between August and November 2020, aiming to facilitate the increasing dependency on AI technology into improving human lives. An international event endorsed and supported by the International Alliance of Robotics Associations (IARA) and a number of global and regional partners including the University of Oxford, the ASEAN Smart Cities Network, Japan Science & Technology Agency, the Malaysian Artificial Intelligence and Robotics Association, among others, AIBotics Go-Digital Series 2020 reviews ethical and responsible AI and robotics innovations through webinars and a virtual exhibition 24 hours a day, seven days a week from August for four months, bringing together renowned industry experts as well as a number of projects and innovative solutions from all around the world. To enable a smart, seamless and sustainable digital conferencing experience, APARA is collaborating with Tencent Cloud, the official conferencing solution provider of AIBotics Go-Digital Series 2020, to bring visitors and delegates a series of power-packed webinars and a virtual exhibition through Tencent Cloud Conference (TCC) solutions which have been widely adopted by local and overseas organizations and enterprises at online and digital business conferences, annual meetings, road shows, lectures, industry forums, among others. "As we adjust to the'new normal' brought about by the COVID-19 pandemic, AI has also become much more mainstream while allowing gatherings and business meetings to be held amid current circumstances. We are excited to present AIBotics Go-Digital Series 2020, highlighting how AI and Robotics can truly augment human potential, which is a timely message in light of the virus-related disruptions globally," said Shanlynn Lee, President of APARA.

Stacking or Stacked Generalization is an ensemble machine learning algorithm. It uses a meta-learning algorithm to learn how to best combine the predictions from two or more base machine learning algorithms. The benefit of stacking is that it can harness the capabilities of a range of well-performing models on a classification or regression task and make predictions that have better performance than any single model in the ensemble. In this tutorial, you will discover the stacked generalization ensemble or stacking in Python. Stacking Ensemble Machine Learning With Python Photo by lamoix, some rights reserved. Stacked Generalization or "Stacking" for short is an ensemble machine learning algorithm.

Created by Lazy Programmer Inc. English [Auto-generated], Portuguese [Auto-generated], 1 more Created by Lazy Programmer Inc. This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own logistic regression module in Python. This course does not require any external materials.

Throughout the rest of my high school, I learned about game development, advanced data structures and algorithms, but not much about AI. The only exposure I had to Machine Learning was this website here, which didn't make a whole lot of sense to me back then. Fast forward, I returned to India and was attending Eastern Public School, finishing up my 12th grade with an International Baccalaureate diploma. I started the Stanford University Machine Learning course taught by Dr. Andrew Ng, http://ml-class.org/. The best part is it does not use any high level libraries to teach the concepts to you, so you have to use MATLAB to answer all the programming assignments.

After attending Edureka, Tableau classes, I feel much more confident in Tableau. Such is the learning curve which gives you great confidence. Edureka is a great help, especially for working professionals. It provides flexibility in all aspects which makes the learning experience awesome! If you have been working remotely these past few months, we have an opportunity for you to learn Data Science from home.

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In this tutorial, you will learn how to perform video classification using FastAI, Python, and Deep Learning. FastAI is a Deep Learning library that is built on the top of Pytorch. There are freely available tutorials/courses for FastAI. I am also currently enrolled in Practical Deep Learning for Coders course.

OWL ontologies are nowadays a quite popular way to describe structured knowledge in terms of classes, relations among classes and class instances. In this paper, given a target class T of an OWL ontology, we address the problem of learning fuzzy concept inclusion axioms that describe sufficient conditions for being an individual instance of T. To do so, we present Fuzzy OWL-BOOST that relies on the Real AdaBoost boosting algorithm adapted to the (fuzzy) OWL case. We illustrate its effectiveness by means of an experimentation. An interesting feature is that the learned rules can be represented directly into Fuzzy OWL 2. As a consequence, any Fuzzy OWL 2 reasoner can then be used to automatically determine/classify (and to which degree) whether an individual belongs to the target class T.

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