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Data Science complete guide on Linear Algebra - DeepLearning


Mathematical intuition required for Data Science and Machine Learning. The linear algebra intuition required to become a Data Scientist. Then, this course is for you. The Common mistake by a data scientist is Applying the tools without the intuition of how it works and behaves. Having the solid foundation of mathematics will help you to understand how each algorithms work, its limitations and its underlying assumptions.

Deep Learning A-Z : Hands-On Artificial Neural Networks


Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind's AlphaGo beat the World champion at Go - a game where intuition plays a key role. But the further AI advances, the more complex become the problems it needs to solve.

The Beginner's Guide to Artificial Intelligence in Unity.


Created by Penny de Byl, Penny English, Portuguese [Auto-generated], 1 more Students also bought A Beginner's Guide To Machine Learning with Unity Build A Multiplayer Kart Racing Game In Unity V.2019 Introduction To Unity For Absolute Beginners 2018 ready Learn Unity's Entity Component System to Optimise Your Games Git Smart: Enjoy Git in Unity, SourceTree & GitHub Preview this course GET COUPON CODE Description Do your non-player characters lack drive and ambition? Are they slow, stupid and constantly banging their heads against the wall? Then this course is for you. Join Penny as she explains, demonstrates and assists you in creating your very own NPCs in Unity with C#.

Allen School News » Allen School professor Dieter Fox receives RAS Pioneer Award from IEEE Robotics & Automation Society

University of Washington Computer Science

The IEEE Robotics & Automation Society has announced Allen School professor Dieter Fox as the recipient of a 2020 RAS Pioneer Award in recognition of his "pioneering contributions to probabilistic state estimation, RGB-D perception, machine learning in robotics, and bridging academic and industrial robotics research." The society will formally honor Fox, director of the University of Washington's Robotics and State Estimation Laboratory and senior director of robotics research at NVIDIA, during the International Conference on Robotics and Automation (ICRA 2020) next week. The RAS Pioneer Award honors individuals who have had a significant impact on the fields of robotics and automation by initiating new areas of research, development, or engineering. Fox's contributions have focused on enabling robots to interact with people and their environment in an intelligent way, with an emphasis on state estimation and perception problems such as 3D mapping, object detection and tracking, manipulation, and human activity recognition. "We are extremely proud that Dieter has been recognized with this prestigious award. It is truly deserved," said professor Magdalena Balazinska, director of the Allen School.

'Passive' visual stimuli is needed to build sophisticated AI

Daily Mail - Science & tech

'Passive' visual experiences play a key part in our early learning experiences and should be replicated in AI vision systems, according to neuroscientists. Italian researchers argue there are two types of learning – passive and active – and both are crucial in the development of our vision and understanding of the world. Who we become as adults depends on the first years of life from these two types of stimulus – 'passive' observations of the world around us and'active' learning of what we are taught explicitly. In experiments, the scientists demonstrated the importance of the passive experience for the proper functioning of key nerve cells involved in our ability to see. This could lead to direct improvements in new visual rehabilitation therapies or machine learning algorithms employed by artificial vision systems, they claim.

Build 2020: Avoiding AI problems


I have been asked many times during the past month whether the heightened pressure that enterprises are now facing as a result of the Covid-19 pandemic will cause them to short-cut aspects such as responsible machine learning in order to get pilots into production more quickly. This is certainly a possibility, but in my opinion, people's memories of the actions that enterprises are taking now will run much deeper than many of the better-planned projects that came before the pandemic or have yet to start. More organisations will therefore aim to get artificial intelligence (AI) right during the crisis as well. As practitioners get going in this area, here are a few things to consider. One global bank I spoke to recently has just put in place a policy that no AI model can move into production without some interpretability and bias controls built into the lifecycle of the application.

IIT-Ropar and TSW Launch a PG Programme in Artificial Intelligence


IIT-Ropar, one of the eight new IITs established by the Ministry of Human Resource Development (MHRD), Government of India, and TSW, the executive education division of Times Professional Learning (a part of The Times of India Group), have launched a Post Graduate Certificate Programme in Artificial Intelligence & Deep Learning. The programme will be coordinated by The Indo-Taiwan Joint Research Centre (ITJRC) on Artificial Intelligence (AI) and Machine Learning (ML), at IIT-Ropar. Supported by the Ministry of Science and Technology, Taiwan, ITJRC is a bilateral centre for collaborative research in disruptive technologies like AI and ML. The programme, with its focus on Artificial Intelligence and Deep Learning, has an eligibility criterion of a minimum of 2 years of work experience in the IT industry. Though an engineering degree is a desirable prerequisite for this programme, one does not need a coding or mathematics background to be eligible.

How Easy Is It To Deploy Explainable Machine Learning Methods?


There has been a lot of talk about making machine learning more explainable so that the stakeholders or the customers can shed the scepticism regarding the traditional black-box methodology. So, in order to find out how it is being implemented, a group of researchers conducted a survey. In the next section, we look at a few findings and practices for deploying as recommended by the researchers at Carnegie Mellon University, who published a work in collaboration with top institutes. During their survey, the researchers have come across some concerns such as model debugging, model monitoring and transparency among many others during the interviews that they have conducted with organisations as part of their work. The study found that most data scientists struggle with debugging poor model performance.

New application of machine learning and image analysis to help distinguish a rare subtype of kidney cancer: US researchers collaborate with scientist quarantined in China during COVID-19 outbreak


Despite those obstacles, Indiana University School of Medicine faculty and Regenstrief Institute research scientists had their research published in Nature Communications on April 14, which is an even more significant feat considering one of the leading authors has been quarantined in Wuhan, China for the last two months of their work. The team consists of Affiliated Scientist Jie Zhang, PhD, Regenstrief Institute Research Scientist Kun Huang, PhD, both Indiana University School of Medicine faculty members, Jun Cheng, PhD, of Shenzhen University and colleagues including Liang Cheng, M.D. of IU School of Medicine. The study was led by Dr. Zhang, an assistant professor of medical and molecular genetics at IU School of Medicine. The work focuses on the application of machine learning and image analysis to help researchers distinguish a rare subtype of kidney cancer (translocational renal cell carcinoma, or tRCC) from other subtypes by examining the features of cells and tissues on a microscopic level. Dr. Zhang said the structural similarities have caused a high rate of misdiagnosis.

Wild cockatoos excel in intelligence tests, countering theory living with humans makes birds smarter

Daily Mail - Science & tech

A longheld theory that animals raised in captivity perform better in cognitive testing may need to be rethought. A new study organized by the University of Veterinary Medicine in Vienna found evidence that wild animals perform just as well at intelligence tests as their lab-raised counterparts. To test the theory, researchers compared two groups of Goffin's cockatoos, a species often found in the tropical jungles of Singapore, Indonesia, and Puerto Rico. The team compared a lab-raised'colony' of 11 cockatoos at their lab in Vienna to eight wild cockatoos recently taken into captivity at a field laboratory in Indonesia. The researchers compared the performance of both groups in a series of simple problem solving tests and found the wild cockatoos were just as clever as the lab-raised ones.