Zillow Surfing Is the Escape We All Need Right Now Scrolling through real estate listings in far-flung destinations is a
With mortgage rates at historic lows and hordes of mostly urban, stir-crazy Americans desperate for more space, The Great Housing Boom of 2020 has been one of the hottest pandemic-fueled market trends of the year — and that’s good news for Zillow. In fact, an entire digital community of aspirational home buyers, or Zillow surfers, are flocking to popular home buying sites as a form of escapism, reports Taylor Lorenz of the New York Times. “People bond over listings on Discord servers, group chats, and ‘Zillow Twitter,’” she writes, “and their obsession has made many strange and obscure listings go viral.” It sure beats doom scrolling.
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it’s just another example that we should not place our personal finance and investing focus on traditional conceptions of retirement. If I am pretty confident, I’ll receive, say, $250,000 by the time I’m 65 via inheritance, I should work this assumption into how I spend, save, and invest over the next 20 years.By exposure to training data, modern deep learning neural networks sometimes have well beyond 10, 100, or even more layers — all learned automatically! The results are astonishing, blowing away years of traditional Machine Learning research on difficult problems!
Since it is a single executable file, this could be implemented as a payload in a Microsoft Word document exploit or similar styled attack and easily have access to an entire system.
In my opinion, a lot of the information you can find on the internet these days about this topic is either very mathematical, very technical, or just completely wrong. The basic idea behind Deep Learning is actually pretty simple and intuitive! Are you curious?
In this article, you’ll learn what Deep Learning is. It will answer two basic questions that come up a lot when people hear me talk enthusiastically about Artificial Intelligence. Why is Deep Learning “deep” and how is it related to the human brain?
It’s easy for us to explain that a smarter person is more likely to win from a dumb person in a game of chess. Well, guess what. If a computer learns every path to victory, he’s likely to be the better person. Deep learning tries to remember every path to victory, allowing it to compete and even beat world-class players in games like chess and go.
View the field as a mathematical framework that allows us to learn representations and insights from data. It is a very promising area because it offers a lot of opportunities for automation, based on historical data, which could not be done before by using an algorithm.
This article explained why it’s worth having a look at Deep Learning and move away from traditional Machine Learning methods, especially when you’re dealing with a lot of data.
This article also puts an end to the myth that we are modeling the human brain. While it’s certainly possible to build interesting applications that outperform humans in certain tasks, it won’t be possible to build better, robot-like, humans anytime soon.
This article also puts an end to the myth that we are modeling the human brain. While it’s certainly possible to build interesting applications that outperform humans in certain tasks, it won’t be possible to build better, robot-like, humans anytime soon.
In traditional Machine Learning, this is a simple problem with an input (a picture) and output layer (cat or dog). Because it has a direct mapping from input to output, it basically predicts a function that separates all cats and dogs. Historically, Machine Learning did this very well by extracting characteristics (feature engineering) like the length of the cat, hair, color, etc. Instead of using the raw image, we used these features to predict the cat or dog. You can imagine, this is quite a task.
Deep Learning is a subfield of Machine Learning. Given a certain input, we predict an output based on the statistics we derive from data. Sounds vague, maybe, but it will become clear soon with an example.
As the amount of data increases, predicting an outcome becomes increasingly more difficult. The difference between Machine Learning and Deep Learning is the ability to detect multiple patterns. It involves layers, hence it becomes “deep”.
Deep Learning provided us with a way to do things differently. They introduced the concept of adding layers! By pushing the raw data through multiple layers, every layer extracts features. The first layer extracts some low-level patterns, while later layers extract more and more high-level features.