Deep learning Introduction
Table of Content:
Deep learning Introduction
Welcome to the first of many courses in deep learning. In this course, you will learn:
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The basic concepts used in building a neural network
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Implementing logistic regression using Single Neuron Network (SNN)
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Concepts of forward propagation and backward propagation
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Building shallow neural network using TensorFlow
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Building a deep neural network using TensorFlow.
Normalising the Data
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Since the data is in terms of length we need to scale to data to have normal distribution.
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For this we take maximum and minimum of each feature.
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Subtract each feature by its minimum value.
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Finally, divide the result by difference of maximum and minimum value
import numpy as np def normalize(data): col_max = np.max(data, axis = 0) col_min = np.min(data, axis = 0) return np.divide(data - col_min, col_max - col_min) X_norm = normalize(X)
What Will You Learn?
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Building a neural network requires a good solid foundation in working with matrices and manipulating them.
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This topic will help you build that fundamental knowledge. With this knowledge, you can build and understand various neural network architectures.