Geoff Hinton - Recent Developments in Deep Learning - YouTube
Geoff Hinton - Recent Developments in Deep Learning - YouTube: ""'via Blog this'
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Geoff Hinton - Recent Developments in Deep Learning - YouTube: ""Naive Bayes classifier - Wikipedia, the free encyclopedia
Naive Bayes classifier - Wikipedia, the free encyclopedia: "In simple terms, a naive Bayes classifier assumes that the presence or absence of a particular feature is unrelated to the presence or absence of any other feature, given the class variable. For example, a fruit may be considered to be an apple if it is red, round, and about 3" in diameter. A naive Bayes classifier considers each of these features to contribute independently to the probability that this fruit is an apple, regardless of the presence or absence of the other features."bextract - Google Search
bextract - Google Search: "If you find yourself using bextract, you probably have done something wrong"How to Get Startup Ideas
How to Get Startup Ideas: "When a startup launches, there have to be at least some users who really need what they're making—not just people who could see themselves using it one day, but who want it urgently."How to Get Startup Ideas
How to Get Startup Ideas: "Why do so many founders build things no one wants? Because they begin by trying to think of startup ideas. That m.o. is doubly dangerous: it doesn't merely yield few good ideas; it yields bad ideas that sound plausible enough to fool you into working on them."Approximating Images With Random Lines - SickSad
Approximating Images With Random Lines - SickSad: "The algorithm works by randomly placing 40 black lines on 40 copies of a blank source image, each of those 40 is then compared to the goal image using SSIM to measure similarity. If any of the 40 are more similar than the source image that image is used as the source for the next iteration of the algorithm. If none of the 40 are more similar then the algorithm repeats with the original source image until a closer similarity is found."Structural similarity - Wikipedia, the free encyclopedia
Structural similarity - Wikipedia, the free encyclopedia: "The structural similarity (SSIM) index is a method for measuring the similarity between two images. The SSIM index is a full reference metric; in other words, the measuring of image quality based on an initial uncompressed or distortion-free image as reference. SSIM is designed to improve on traditional methods like peak signal-to-noise ratio (PSNR) and mean squared error (MSE), which have proven to be inconsistent with human eye perception."Content-based image classification in Python
Content-based image classification in Python: "Since the goal of this exercise is to be able to classify new images without requiring visual inspection by a human, we're going to need to train a predictive model that can tag images automatically."Effectively managing memory at Gmail scale - HTML5 Rocks
Effectively managing memory at Gmail scale - HTML5 Rocks: "The young generation heap in V8 is split into two spaces, named from and to. Memory is allocated from the to space. Allocating is very fast, until, the to space is full at which point a young generation collection is triggered. Young generation collection first swaps the from and to space, the old to space (now the from space) is scanned and all live values are copied into the to space or tenured into the old generation. A typical young generation collection will take on the order of 10 milliseconds (ms)."New HTML Parser: The long-awaited libxml2 based HTML parser code is live. It needs further work but already handles most markup better than the original parser.
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