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Online Meta-Learning

arXiv.org Artificial Intelligence

A central capability of intelligent systems is the ability to continuously build upon previous experiences to speed up and enhance learning of new tasks. Two distinct research paradigms have studied this question. Meta-learning views this problem as learning a prior over model parameters that is amenable for fast adaptation on a new task, but typically assumes the set of tasks are available together as a batch. In contrast, online (regret based) learning considers a sequential setting in which problems are revealed one after the other, but conventionally train only a single model without any task-specific adaptation. This work introduces an online meta-learning setting, which merges ideas from both the aforementioned paradigms to better capture the spirit and practice of continual lifelong learning. We propose the follow the meta leader algorithm which extends the MAML algorithm to this setting. Theoretically, this work provides an $\mathcal{O}(\log T)$ regret guarantee with only one additional higher order smoothness assumption in comparison to the standard online setting. Our experimental evaluation on three different large-scale tasks suggest that the proposed algorithm significantly outperforms alternatives based on traditional online learning approaches.


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Mashable

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The Growing Impact of AI in Financial Services: Six Examples

#artificialintelligence

Just as many other technological advancements, Artificial Intelligence came to our lives from the pages of fairy tales and fiction books (think of the Tinman from The Wizard of Oz or Maria from Metropolis). People dreamt about machines able to solve problems and release some of the fast-compounding pressure of the 21st century. Less than 70 years from the day when the very term Artificial Intelligence came into existence, it's become an integral part of the most demanding and fast-paced industries. Forward-thinking executive managers and business owners actively explore new AI use in finance and other areas to get a competitive edge on the market. More often than not, we don't realize how much Artificial Intelligence is involved in our day-to-day life.


Random Forest Algorithm in Machine Learning

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Random forest algorithm is a one of the most popular and most powerful supervised Machine Learning algorithm in Machine Learning that is capable of performing both regression and classification tasks. As the name suggest, this algorithm creates the forest with a number of decision trees. Random Forest Algorithm in Machine Learning: Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making.


Google Developers brings its Machine Learning Bootcamp to Indonesia

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Last October, Google Developers brought their Machine Learning Bootcamp to Jakarta, Indonesia! ML Bootcamp is a one-stop solution to learn about Google's latest machine learning offerings from both Googlers and other industry experts. The 4-day intensive bootcamp consists of instructor-led trainings, hands-on codelabs, and saw 35 companies, as well as 12 startups represented from across Indonesia. If you're an aspiring ML developer, be sure to check out the following online courses: ML crash course with TensorFlow APIs http://bit.ly/2MLUDkU


6 concepts of Andrew NG's book: "Machine Learning Yearning"

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Andrew NG is a computer scientist, executive, investor, entrepreneur, and one of the leading experts in Artificial Intelligence. He is the former Vice President and Chief Scientist of Baidu, an adjunct professor at Stanford University, the creator of one of the most popular online courses for machine learning, the co-founder of Coursera.com At Baidu, he was significantly involved in expanding their AI team into several thousand people. The book starts with a little story. Imagine, you want to build the leading cat detector system as a company.


Introduction to KNIME: Pre-processing and visualizing data

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Hands-on exercises deeply focused on the pre-processing (manipulation/wrangling) and visualizing phase - KNIME We will focus on the most time-consuming part of the machine learning process which is the data exploration consisting from data visualisation and data wrangling which serves for data transformation to get well prepared data. We will use open-source, highly intuitive and effective analytics platform KNIME where we will read the data, transform them and visualise them by using KNIME nodes. What you'll learn Pre-process the data (data wrangling) by using Knime analytics platform Model and transform data in KNIME Visualise the data in charts and plots in KNIME Work with the KNIME nodes focused on data wrangling and visualisation Read data and work with more and different file types at one place Join and merge different data Modify, filter, resort, split, filter data, handle with missing values Group and pivot data Use basic math formulas in KNIME Visualise data by using different plots and charts (box plot, pie chart, scatter plot, line plot, histogram) Handle with KNIME knwf files (create, save, move, rename, delete, export, import) Understand the KNIME environment Who this course is for: data analysts, data scientists and those of you willing to learn new things anyone searching open-source, user-friendly, easily understandable and highly effective SW for data analyzing and machine learning tasks without necessity to have programming skills people working with data (also with big data) Course Info: Title: Introduction to KNIME: Pre-processing and visualizing data Description Course: Hands-on exercises deeply focused on the pre-processing (manipulation/wrangling) and visualizing phase - KNIME Instructor: Barbora Stetinova, MBA Duration: 2.5 hours on-demand video Online Classes Platfrom: Udemy GET Udemy Discount Introduction to KNIME: Pre-processing and visualizing data


Educating the Next Generation of Leaders

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Traditional approaches to leadership development no longer meet the needs of organizations or individuals. There are three: (1) Organizations, which pay for leadership development, don't always benefit as much as individual learners do. A growing assortment of online courses, social platforms, and learning tools from both traditional providers and upstarts is helping to close the gaps. The need for leadership development has never been more urgent. Companies of all sorts realize that to survive in today's volatile, uncertain, complex, and ambiguous environment, they need leadership skills and organizational capabilities different from those that helped them succeed in the past. There is also a growing recognition that leadership development should not be restricted to the few who are in or close to the C-suite. With the proliferation of collaborative problem-solving platforms and digital "adhocracies" that emphasize individual initiative, employees across the board are increasingly expected to make consequential decisions that align with corporate strategy and culture.


The Next Frontier: Healthcare Artificial Intelligence Consulting

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There is an undeniable truth that Artificial Intelligence, which we will refer to simply as AI, is the next frontier for the healthcare industry. Several sources have already pegged the market to be worth $36.1 billion by 2025. For those of you who like simple language; the way AI works is by having it developed through machine learning, natural language processing, and deep learning. This process is controlled by programmers, who in a lot of cases are independent contractors. Regulatory frameworks will soon be created to govern this new boom, with consulting and online training courses becoming the next cash cows milking this industry for profits.


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Mashable

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