Table of Contents

    Introduction to Deep Learning: A Beginner's Guide

    Rumman Ansari July 28, 2024 613 views

    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.