Dataflyp

@TheDataflyp

Reverse Engineering Machine Learning, One Picture at a Time 🤣

Joined October 2017

8Posts 0Followers 0Following

Dataflyp

Oct 14, 2017

In #learninAlgorithms, the #gradient most often required is that of the #costfunction with respect to #parameters.

TheDataflyp's tweet image. In #learninAlgorithms, the #gradient most often required is that of the #costfunction with respect to #parameters.

Dataflyp

Oct 13, 2017

#CurseOfDimensionality = High Data Dimensions. The# of input X is much larger than the number of #TrainingExamples.#hardtotrain

TheDataflyp's tweet image. #CurseOfDimensionality = High Data Dimensions. The# of input X is much larger than the number of #TrainingExamples.#hardtotrain

Dataflyp

Oct 13, 2017

#Fullyconnected later implies that every #neuron in the previous #layer is connected to every #neuron in the next layer. #cnn #pooling

TheDataflyp's tweet image. #Fullyconnected later implies that every #neuron in the previous #layer is connected to every #neuron in the next layer. #cnn #pooling

Dataflyp

Oct 13, 2017

These 4 main operations are basic blocks for any #convolutional #NeuralNetwork: #convolution, #NonLinearity,#Pooling,#Classification

TheDataflyp's tweet image. These 4 main operations are basic blocks for any #convolutional #NeuralNetwork: #convolution, #NonLinearity,#Pooling,#Classification

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