Matrix decomposition, also called matrix factorization is the process of splitting a matrix into multiple pieces. In the context of data science, you can for instance use it to select parts of the data, aimed at reducing dimensionality without losing much information (as for instance in Principal Component Analysis, as you'll later in this post). Some operations are also more easily computed on the matrices resulting from the decomposition. In this article, you'll learn about the eigendecomposition of a matrix. One way to understand it is to consider it as a special change of basis (more details about change of basis in my last post).

The deep learning model demonstrates that one's baseline well-being is not the determining factor of future well-being, as posited by what's known as the hedonic treadmill theory -- which posits that people are doomed to quickly return to a relatively stable level of happiness despite major positive or negative events or life changes,

All the Data Scientists hide at least on Data Science idea in their heart. However, due to time constraints or lack of money, they do not transform their ideas into a project. In this article, I propose a strategy to turn your idea into a Data Science project. The first step towards funding your project involves writing a first draft your project. The summary is an overview of the project.

As more machine learning tools reach patients, developers are starting to get smart about the potential for bias to seep in. But a growing body of research aims to emphasize that even carefully trained models -- ones built to ignore race -- can breed inequity in care. Researchers at the Massachusetts Institute of Technology and IBM Research recently showed that algorithms based on clinical notes -- the free-form text providers jot down during patient visits -- could predict the self-identified race of a patient, even when the data had been stripped of explicit mentions of race. It's a clear sign of a big problem: Race is so deeply embedded in clinical information that straightforward approaches like race redaction won't cut it when it comes to making sure algorithms aren't biased. "People have this misconception that if they just include race as a variable or don't include race as variable, it's enough to deem a model to be fair or unfair," said Suchi Saria, director of the machine learning and health care lab at Johns Hopkins University and CEO of Bayesian Health.

Dr. PKS Prakash is a Data Scientist and an author. He has spent last 12 years in developing many data science solution to solve problems from leading companies in healthcare, manufacturing, pharmaceutical and e-commerce domain. He is working as Data Science Manager at ZS Associates. ZS is one of the world's largest business services firms helping clients with commercial success, by creating data-driven strategies using advanced analytics that they can implement within their sales and marketing operations to make them more competitive, and by helping them deliver impact where it matters.

The sketch is configured to update the metrics every eight seconds. You can see the Arduino sketch below and in the project's GitHub, NILM⁹ The actual energy disaggregation computations are hosted on a Raspberry Pi 4 which is connected over USB to the Arduino to fetch the aggregate metrics. The computations are comprised of running the tflite appliance inference models, trained and quantized per the steps described above, with pre- and post-processing steps. The inference models output predicted energy for each appliance from 599-sample sized windows of the aggregate apparent power input signal. These predictions are stored in a local CSV file and made available for downstream reporting and analysis.

SpaceX Starship's Super Heavy Rocket is ready for what could be its final launch pad test before a likely orbital test flight in July. The massive Super Heavy Booster 7, which has 33 Raptor engines, was transported to its orbital launch pad on June 23. An enormous robotic arm mounted the rocket to the launch pad. A huge amount of work was required for the company to reach this point due to the large number of Raptor rocket engines in the Super Heavy. Elon Musk has said that SpaceX's Starship Super Heavy Booster 7 will most likely be ready for an orbital test flight in July One difference between SpaceX's rockets and all the ones that came before it - theirs are reusable, which is a huge cost savings. As Ars Technica notes, Aerojet Rocketdyne, which also makes propulsion rockets, has a goal of building just four RS-25 rocket engines for NASA this year.

A tiny turbine made from DNA looks like a windmill and is hundreds of times smaller than most bacteria. It rotates when immersed in salty water and could be used as a molecular machine for speeding up chemical reactions or transporting particles inside cells. Cees Dekker at Delft University of Technology in the Netherlands and his colleagues created the turbine after being inspired by a rotating enzyme that helps catalyse energy-storing molecules in our cells.

Robot vacuum deals have always been a pillar of Prime Day. Thanks to competing summer sales from Best Buy and Walmart, you have a handful of options to find the model that best suits your budget. BEST SELF-EMPTYING DEAL: The Shark IQ RV1002AE(opens in a new tab) cleans competently, self-empties, and offers zone cleaning -- $299.99 $599.99 (save $300) BEST ROBOT VACUUM/MOP DEAL: The Roborock S6 Pure(opens in a new tab) can sweep and scrub specific rooms for less than $400 -- $359.99 $599.99 (save $240) Roombas generally don't have the ability to map and clean specific rooms until around the $500 price point. That's what makes the Wyze vacuum so impressive. Its LiDAR tower emits 2,016 laser pointers every second to generate a floor plan of your home.

Daniel Faggella is Head of Research at Emerj. Called upon by the United Nations, World Bank, INTERPOL, and leading enterprises, Daniel is a globally sought-after expert on the competitive strategy implications of AI for business and government leaders. The company that would become eBay was founded as a sole proprietorship under the name AuctionWeb in September 1995 by Pierre Omidyar. The company changed its name to eBay in September 1997. Today, eBay is a global e-commerce leader in more than 190 markets throughout the world.