This post will cover how testing is done for the coronavirus, why it's important in battling the pandemic, and how deep learning tools for medical imaging can help us improve the quality of COVID-19 testing.

An introduction to the DBSCAN algorithm and its Implementation in Python.

How can you keep your focus and drive during a global crisis? Take on a 90-day learning challenge for data science and check out this list of books and courses to follow.

Also: Should Data Scientists Model COVID19 and other Biological Events; 5 Papers on CNNs Every Data Scientist Should Read; 24 Best (and Free) Books To Understand Machine Learning; Mathematics for Machine Learning: The Free eBook; Find Your Perfect Fit: A Quick Guide for Job Roles in the Data World

Check out these 10 books that can help data scientists and aspiring data scientists learn machine learning today.

Also: Should Data Scientists Model COVID19 and other Biological Events; Learning during a crisis (Data Science 90-day learning challenge); Data Transformation: Standardization vs Normalization; DBSCAN Clustering Algorithm in Machine Learning; Find Your Perfect Fit: A Quick Guide for Job Roles in the Data World

Also: A Concise Course in Statistical Inference: The Free eBook; ML Ops: Machine Learning as an Engineering Discipline; Learning during a crisis (#DataScience 90-day learning challenge) ; Free High-Quality Machine Learning & Data Science Books & Courses: Quarantine Edition

This article demonstrates how building a smarter API serving Deep Learning models minimizes the response time.

"Deep Learning" is the quintessential book for understanding deep learning theory, and you can still read it freely online.

So, you are interested in machine learning? Here is your complete learning path to start your career in the field.

Train a "blackbox" GBM model on a real dataset and make it explainable with SHAP.

What are Some 'Advanced ' #AI and #MachineLearning Online Courses?; 24 Best (and Free) Books To Understand Machine Learning; Top 5 must-have #DataScience skills for 2020

Check out this comprehensive guide to model optimization techniques.

As can be common in many technical fields, the landscape of specialized roles is evolving quickly. With more people learning at least a little machine learning, this could eventually become a common skill set for every software engineer.

Also: Introducing MIDAS: A New Baseline for Anomaly Detection in Graphs; The Super Duper NLP Repo: 100 Ready-to-Run Colab Notebooks; Five Cool Python Libraries for Data Science.

David Garvin and Amy Edmonson, Harvard Business School professors and coauthors of the HBR article "Is Yours a Learning Organization?"

Colonel Tom Kolditz, professor and head of the department of Behavioral Sciences and Leadership at the U.S. Military Academy at West Point.

Bill Sahlman, Harvard Business School professor and Senior Associate Dean for External Relations.

Frank Barrett, jazz pianist and author of "Yes to the Mess: Surprising Leadership Lessons from Jazz."

The founder of the Khan Academy talks with HBR senior editor Alison Beard.

Dorothy Leonard, author of "Critical Knowledge Transfer" ​and Harvard Business School professor, on retaining organizational expertise.

Andrew Innes, game designer, product manager, and author of "What Board Games Can Teach Business."

Whitney Johnson, an executive coach, argues that on-the-job learning is the key to keeping people motivated. When managers understand that, and understand where the people they manage are on their individual learning curve — the low end, the sweet spot, or the high end — employees are engaged, productive, and innovative. Johnson is the author of the book “Build an A-Team: Play to Their Strengths and Lead Them Up the Learning Curve.”

Roger Martin, a professor at the University of Toronto’s Rotman School of Management, offers two main reasons General Electric has lost its competitiveness. GE’s stock has been removed from the Dow Jones Industrial Average. Martin blames pressures from activist investors as well as a short-sighted mergers and acquisitions strategy. He’s the author of “GE’s Fall Has Been Accelerated by Two Problems. Most Other Big Companies Face Them, Too.”

Scott Young, who gained fame for teaching himself the four-year MIT computer science curriculum in just 12 months, says that the type of fast, focused learning he employed is possible for all of us -- whether we want to master coding, become fluent in a foreign language, or excel at public speaking. And, in a dynamic, fast-paced business environment that leaves so many of us strapped for time and struggling to keep up, he believes that the ability to quickly develop new knowledge and skills will be a tremendous asset. After researching best practices and experimenting on his own, he has developed a set of principles that any of us can follow to become "ultralearners." Young is the author of the book "Ultralearning: Master Hard Skills, Outsmart the Competition, and Accelerate Your Career."

Stefan Thomke, professor at Harvard Business School, says running experiments can give companies tremendous value, but too often business leaders make decisions based on intuition. While A/B testing on large transaction volumes is common practice at Google, Booking.com, and Netflix, Thomke says even small firms can get a competitive advantage from experiments. He explains how to introduce, run, and learn from them, as well as how to cultivate an experimental mindset at your organization. Thomke is the author of the book "Experimentation Works: The Surprising Power of Business Experiments" and the HBR article "Building a Culture of Experimentation."

NECA is excited to announce the launch of the NECA Educational Advancement Resource Network (EARN), an initiative designed to facilitate relationships and learning between individuals in electrical construction firms and institutions of higher education.

In this GDC 2020 virtual talk NetEase's Renjie Li discusses the application of reinforcement learning in NetEase games, including problems encountered and how the solutions impacted the final product. ...

Both AI and ML are touted to give businesses the edge they need, improve efficiencies, make sales and marketing better and even help in critical HR functions.

“Our government believes artificial intelligence, in different forms, can help us achieve the $5 trillion benchmark over the next five years, but also help us do it effectively and efficiently,” Goyal said while inaugurating the NSE Knowledge Hub here. The hub is an AI-powered learning ecosystem for the banking, financial services and insurance sector.

High costs, poor job prospects and wrangles over work permits are persuading a host of Indian students with foreign degrees to return home.

David Rohde points us to this new seminar series that has the following description: Machine learning is changing the world we live in at a break neck pace. From image recognition and generation, to the deployment of recommender systems, it seems to be breaking new ground constantly and influencing almost every aspect of our lives. […]

The Northwest Forest Plan (the Plan) identifies adaptive management as a central strategy for effective implementation. Despite this, there has been a lack of any systematic evaluation of its performance.

The American Recovery and Reinvestment Act of 2009 (the Recovery Act) aimed to create jobs and promote economic growth while addressing the Nation's social and environmental needs. The USDA Forest Service received $1.15 billion in economic recovery funding. This report contains key findings and lessons learned from a socioeconomic assessment of Forest Service Recovery Act projects. The assessment examines how Forest Service economic recovery projects at eight case-study locations around the United States are contributing to socioeconomic well-being in rural counties affected by the economic recession of 2007-2009. It also investigates how Forest Service mission-related work can be accomplished in a manner that creates local community development opportunities. This report is a companion to general technical report PNW-GTR-831, which contains the full case-study reports.

Blyth schoolboy Ryan Office has recently returned from receiving proton beam therapy in Florida after being diagnosed with a very rare brain tumour

In early 2014 I had just gotten married and recently moved into a new home. With two major life events out of the way, I decided I was ready to lead a WordCamp. I originally planned to organize WordCamp Detroit. I was an organizer twice before and the event had missed a year and I […]

The post 5 Critical Lessons Learned Organizing WordCamp Ann Arbor for the Third Time appeared first on Psychology of Web Design | 3.7 Blog.

Deep Learning is a branch of artificial intelligence and a subset of machine learning that focuses on networks capable of, usually, unsupervised learning from unstructured and other forms of data. It is also known as deep structured learning or differential programming. Architectures inspired by deep learning find use in a range of fields, such as...

Whether you’re into photography, there are so many basic skills that you can learn when it comes to photo editing that can make a huge difference in your photos and selfies. Between brightening up a photo, changing the size, or cutting something out, there’s always a small thing you wish you could change. In order to do that, you should learn these basic photo editing tools so that you can adjust your photos in the simplest manner. Adobe photoshop If you were to use only one software for photo editing, then it should be none other than Adobe Photoshop. With

The post Learning the Basics of Photo Editing appeared first on Photoshop Lady.

This trip solidified my conviction to learning photography. A lot has happened since this shot was taken.
Can you pinpoint the moment you decided to pursue photography? (at Toronto, Ontario)

https://thenextweb.com/growth-quarters/2020/05/08/should-designers-learn-how-to-code-syndication/

“It will get easier after you adjust."After receiving a traumatic brain injury from a car crash two years ago, the Los Angeles-based journalist Amanda Chicago Lewis has lived in social isolation. Because of stay-at-home orders to reduce the spread of COVID-19, more people are now living in similar circumstances. Below, Lewis shares how she’s adapted her apartment, her routine, and her habits to cope with being at home for extended periods of time.

Many photographers think that learning how to find the ideal location and take a picture at the right time is all they need to know. However, this isn’t the case, and in a world where CGI rivals reality and touch-ups via photo editing software are now seen as a necessity to customers, relying on point and click will kill your photography business. Here are seven reasons every photographer should learn how to use Photoshop.   Royalty Free Photo Touch-Ups Are Essential When a family orders school photos, they pay a flat fee for copies of the school pictures and a little more if the child’s name is embossed on the picture. They pay a separate fee if the picture is touched up, whether it is hiding acne or reducing glare on the child’s glasses. Photographers who know how to touch up photos without making it look artificial or cartoonish can ... Read more

The post 7 Reasons Every Photographer Should Learn How to Use Photoshop appeared first on Digital Photography Tutorials.

In early 2014 I had just gotten married and recently moved into a new home. With two major life events out of the way, I decided I was ready to lead a WordCamp. I originally planned to organize WordCamp Detroit. I was an organizer twice before and the event had missed a year and I […]

The post 5 Critical Lessons Learned Organizing WordCamp Ann Arbor for the Third Time appeared first on Psychology of Web Design | 3.7 Blog.

During an interview on "Fox and Friends," Trump explained why he chose not to go on a firing spree amid Special Counsel Robert Mueller's Russia investigation a la Nixon's Saturday Night Massacre during the Watergate scandal. "I learned a lot from Richard Nixon: Don't fire people," the President said. "I learned a lot. I study history, and the firing of everybody ... .I should've, in one way," he continued. "But I'm glad I didn't because look at the way it turned out."

Predicting the spatial configuration of gas molecules in nanopores of shale formations is crucial for fluid flow forecasting and hydrocarbon reserves estimation. The key challenge in these tight formations is that the majority of the pore sizes are less than 50 nm. At this scale, the fluid properties are affected by nanoconfinement effects due to the increased fluid-solid interactions. For instance, gas adsorption to the pore walls could account for up to 85% of the total hydrocarbon volume in a tight reservoir. Although there are analytical solutions that describe this phenomenon for simple geometries, they are not suitable for describing realistic pores, where surface roughness and geometric anisotropy play important roles. To describe these, molecular dynamics (MD) simulations are used since they consider fluid-solid and fluid-fluid interactions at the molecular level. However, MD simulations are computationally expensive, and are not able to simulate scales larger than a few connected nanopores. We present a method for building and training physics-based deep learning surrogate models to carry out fast and accurate predictions of molecular configurations of gas inside nanopores. Since training deep learning models requires extensive databases that are computationally expensive to create, we employ active learning (AL). AL reduces the overhead of creating comprehensive sets of high-fidelity data by determining where the model uncertainty is greatest, and running simulations on the fly to minimize it. The proposed workflow enables nanoconfinement effects to be rigorously considered at the mesoscale where complex connected sets of nanopores control key applications such as hydrocarbon recovery and CO2 sequestration.

Temporal event segmentation of a long video into coherent events requires a high level understanding of activities' temporal features. The event segmentation problem has been tackled by researchers in an offline training scheme, either by providing full, or weak, supervision through manually annotated labels or by self-supervised epoch based training. In this work, we present a continual learning perceptual prediction framework (influenced by cognitive psychology) capable of temporal event segmentation through understanding of the underlying representation of objects within individual frames. Our framework also outputs attention maps which effectively localize and track events-causing objects in each frame. The model is tested on a wildlife monitoring dataset in a continual training manner resulting in $80\%$ recall rate at $20\%$ false positive rate for frame level segmentation. Activity level testing has yielded $80\%$ activity recall rate for one false activity detection every 50 minutes.

Differential machine learning (ML) extends supervised learning, with models trained on examples of not only inputs and labels, but also differentials of labels to inputs.

Differential ML is applicable in all situations where high quality first order derivatives wrt training inputs are available. In the context of financial Derivatives risk management, pathwise differentials are efficiently computed with automatic adjoint differentiation (AAD). Differential ML, combined with AAD, provides extremely effective pricing and risk approximations. We can produce fast pricing analytics in models too complex for closed form solutions, extract the risk factors of complex transactions and trading books, and effectively compute risk management metrics like reports across a large number of scenarios, backtesting and simulation of hedge strategies, or capital regulations.

The article focuses on differential deep learning (DL), arguably the strongest application. Standard DL trains neural networks (NN) on punctual examples, whereas differential DL teaches them the shape of the target function, resulting in vastly improved performance, illustrated with a number of numerical examples, both idealized and real world. In the online appendices, we apply differential learning to other ML models, like classic regression or principal component analysis (PCA), with equally remarkable results.

This paper is meant to be read in conjunction with its companion GitHub repo https://github.com/differential-machine-learning, where we posted a TensorFlow implementation, tested on Google Colab, along with examples from the article and additional ones. We also posted appendices covering many practical implementation details not covered in the paper, mathematical proofs, application to ML models besides neural networks and extensions necessary for a reliable implementation in production.

With the increase in use of Unmanned Aerial Vehicles (UAVs)/drones, it is important to detect and identify causes of failure in real time for proper recovery from a potential crash-like scenario or post incident forensics analysis. The cause of crash could be either a fault in the sensor/actuator system, a physical damage/attack, or a cyber attack on the drone's software. In this paper, we propose novel architectures based on deep Convolutional and Long Short-Term Memory Neural Networks (CNNs and LSTMs) to detect (via Autoencoder) and classify drone mis-operations based on sensor data. The proposed architectures are able to learn high-level features automatically from the raw sensor data and learn the spatial and temporal dynamics in the sensor data. We validate the proposed deep-learning architectures via simulations and experiments on a real drone. Empirical results show that our solution is able to detect with over 90% accuracy and classify various types of drone mis-operations (with about 99% accuracy (simulation data) and upto 88% accuracy (experimental data)).

A standard approach to evaluating language models analyzes how models assign probabilities to valid versus invalid syntactic constructions (i.e. is a grammatical sentence more probable than an ungrammatical sentence). Our work uses ambiguous relative clause attachment to extend such evaluations to cases of multiple simultaneous valid interpretations, where stark grammaticality differences are absent. We compare model performance in English and Spanish to show that non-linguistic biases in RNN LMs advantageously overlap with syntactic structure in English but not Spanish. Thus, English models may appear to acquire human-like syntactic preferences, while models trained on Spanish fail to acquire comparable human-like preferences. We conclude by relating these results to broader concerns about the relationship between comprehension (i.e. typical language model use cases) and production (which generates the training data for language models), suggesting that necessary linguistic biases are not present in the training signal at all.