As explained in 00-irisxl Deep Learning has 2 pilars : Neural Network and Convolution -This appli still demonstates a Neural Network. Method is exactly similar to the one used in 00-irisxl. -But now we deal with an Input of 784 pos (=28x28) to represent one hand-written digit - Incidentally this example is directly derived from the first application which launched the Deep Learning In addition this application shows how to use powerful GRID and PIXEL functions. Much grain to grind 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:image/png;base64,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endy0Settings : RD=1000, AC=1,ACD=1,TARGET=1

3

Update of a matrix by a "modification" vector defined by bwd 2 for w2 and bwd 3 for w3

Add numbers to the right side of matrix. Use RDLIS to create a random vector + filter ADR

234

234

reports a list of all pixels in the given costume. Each pixel is represented by a 4-item sublist of RGBA values

pixelspixels width heightheightcostume var i, pixels = [], src = costume.contents.getContext('2d').getImageData( 0, 0, costume.width(), costume.height() ); for (i = 0; i < src.data.length; i += 4) { pixels.push(new List([ src.data[i], src.data[i + 1], src.data[i + 2], src.data[i + 3] ])); } return new List(pixels);overwrite the pixels in the given costume with another list of pixels. Caution! This operation directly changes the costume. Make sure to use a copy in case you would like to revert changes later.

reports the sprite's actual current costume, which may or may not be part of its wardrobe e.g. if it is a copy

de:für _ = _ bis _ _ 110i

newProvides LOOP as a function of one input that runs the body of the LET with A set to the function input, so the body can run itself recursively. See COPY block in Variables for an example of use.

new valuecopy VALUE N times reports a list containing N (identical) copies of VALUE

ypncnl

xy

w-2h-200

hsclpa

display the given bitmap in a copy of the current costume (so you can switch back to the original costume again)

pt:mostra a imagem _ takes a snapshot with the webcam and reports it as a new costume, or zero if the user cancels

pt:$camera a imagem actual do vídeo

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