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Asked by Ankit Last Modified
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BEPEC
The general impression is that there is a heavy maths and statistics in data science. That is not true. The secret is that on application side the maths and statistics are minimal. Don't believe me.
Look at the following code for digit classification. There is zero maths.
from keras import models
from keras import layers
network = models.Sequential()
network.add(layers.Dense(16, activation=’relu’, input_shape=(28 * 28,)))
network.add(layers.Dense(16, activation=’relu’))
network.add(layers.Dense(10, activation=’softmax’))
network.compile(optimizer=’rmsprop’,loss=’categorical_crossentropy’, metrics=[‘accuracy’])
This is an example of deep learning code in Keras. All the maths is being taken care by Keras API. The point is that one needs maths to get a intuition of what is happening inside the algorithm but one would not need to code the mathematical parts. To draw parallels one uses indexes in database but one does not code the index logic.
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