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Keeping Up with Deep Learning -- 26 Nov 2020

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This is the second edition of my weekly update on deep learning. Every Thursday, I'll release a new batch of research papers, blog posts, Github repos, etc. that I liked over the past week. Links are provided for each featured project, so you can dive in and learn about whatever catches your eye. If you missed last week's edition, you can find it here. All thoughts and opinions are my own.


Reinforcement Learning Explained Visually (Part 4): Q Learning, step-by-step

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My goal throughout will be to understand not just how something works but why it works that way. If you haven't read the earlier articles, particularly the second and third ones, it would be a good idea to read them first, as this article builds on many of the concepts that we discussed there. Q-Learning is the most interesting of the Lookup-Table-based approaches which we discussed previously because it is what Deep Q Learning is based on. The Q-learning algorithm uses a Q-table of State-Action Values (also called Q-values). This Q-table has a row for each state and a column for each action. Each cell contains the estimated Q-value for the corresponding state-action pair. We start by initializing all the Q-values to zero. As the agent interacts with the environment and gets feedback, the algorithm iteratively improves these Q-values until they converge to the Optimal Q-values.


Kamua's AI-powered editor helps marketers embrace vertical video

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A new AI-powered video-editing platform is preparing for launch, designed to help businesses, marketers, and creators automatically transform landscape-shot videos into a vertical format suitable for TikTok, Instagram, Snapchat, and all the rest. Founded out of London in 2019, Kamua wants to be aligned with tools such as Figma, a software design and prototyping tool for product managers who lack certain technical skills. For Kamua, the goal is democratizing the creative and technical processes in video editing. "Kamua makes it possible for non-editors to directly control how their videos look in any format, on any screen, in multiple durations and sizes, without the steep and long learning curves, hardware expense, and legacy workflows associated with editing software suites," Kamua CEO and cofounder Paul Robert Cary told VentureBeat. Kamua, which was available as an alpha release since last year before launching in invite-only beta back in September, is now preparing for a more extensive roll-out on December 1, when a limited free version will be made available for anyone without any formal application process.


How secure are your AI and machine learning projects?

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When enterprises adopt new technology, security is often on the back burner. It can seem more important to get new products or services to customers and internal users as quickly as possible and at the lowest cost. Good security can be slow and expensive. Artificial intelligence (AI) and machine learning (ML) offer all the same opportunities for vulnerabilities and misconfigurations as earlier technological advances, but they also have unique risks. As enterprises embark on major AI-powered digital transformations, those risks may become greater.


Amazon Black Friday deals: Echo, Kindle, Fire TV, and more

ZDNet

Here's a great lineup of gift ideas and resources to get you started. Black Friday is here, and we have rounded up the best deals on Amazon devices. Amazon has an ever-expanding list of devices. With tablets, smart speakers, streaming devices, and much more, it's bound to have something for everyone on your shopping list. As usual, most of the best prices on Amazon's devices can be found on Amazon's website.


AI/Machine Learning Market Research Report 2020 – Global Forecast till 2025

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The ' AI/Machine Learning market' study now available at MarketStudyReport.com, is a detailed sketch of the business sphere in terms of current and …


Metal Geochemistry Meets Machine Learning in the North Atlantic

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… the findings from the photo mosaic maps will be extrapolated to the regions covered by the echo-sounder mapping by means of machine learning.”.


Beginner's Guide to K-Nearest Neighbors in R: from Zero to Hero

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In the world of Machine Learning, I find the K-Nearest Neighbors (KNN) classifier makes the most intuitive sense and easily accessible to beginners even without introducing any math notations. To decide the label of an observation, we look at its neighbors and assign the neighbors' label to the observation of interest. Certainly, looking at one neighbor may create bias and inaccuracy, and the KNN method has a set of rules and procedures to determine the best number of neighbors, e.g., examining k 1 neighbors and adopt majority rule to decide the category. "To decide the label for new observations, we look at the closest neighbors." To choose the nearest neighbors, we have to define what distance is.


Global Big Data Conference

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We've been seeing the headlines for years: "Researchers find flaws in the algorithms used…" for nearly every use case for AI, including finance, health care, education, policing, or object identification. Most conclude that if the algorithm had only used the right data, was well vetted, or was trained to minimize drift over time, then the bias never would have happened. There are several practical strategies that you can adopt to instrument, monitor, and mitigate bias through a disparate impact measure. For models that are used in production today, you can start by instrumenting and baselining the impact live. For analysis or models used in one-time or periodic decision making, you'll benefit from all strategies except for live impact monitoring.


Enhanced Planning Capability Enables Continuous Supply Chain

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The future of planning is connected, intelligent, and continuous. Yet many companies remain so far away from this vision; it often seems unachievable. With many planning processes being so siloed and disconnected from execution, they can feel ineffective. Fortunately, evaluations of the planning landscape reveal many organizations are adopting technologies that move towards a de-siloed, network-based approach to planning. To optimize planning capabilities, it crucial to achieve this connection at the enterprise level as well as into the broader supply network.