Introduction to Deep Learning: A Beginner's Guide

Rumman Ansari   Software Engineer   2024-07-28 04:39:17   291  Share
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Table of Content:

Deep learning Introduction

Welcome to the first of many courses in deep learning. In this course, you will learn:

  • The basic concepts used in building a neural network

  • Implementing logistic regression using Single Neuron Network (SNN)

  • Concepts of forward propagation and backward propagation

  • Building shallow neural network using TensorFlow

  • Building a deep neural network using TensorFlow.

Normalising the Data
  • Since the data is in terms of length we need to scale to data to have normal distribution.

  • For this we take maximum and minimum of each feature.

  • Subtract each feature by its minimum value.

  • 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?
  • Building a neural network requires a good solid foundation in working with matrices and manipulating them.

  • This topic will help you build that fundamental knowledge. With this knowledge, you can build and understand various neural network architectures.