Download and Read the Data
You can use any dataset you want, here I have used the red-wine quality dataset from Kaggle. This is a classification problem, of course, you can learn to apply the concept to other problems. First, download the dataset in your working directory. Now that the data is downloaded let’s load the data as data frame.
Python3
import numpy as npimport pandas as pd# be sure to change the file path# if you have the dataset in another# directly than the working folderdf = pd.read_csv('winequality-red.csv')df.head() |
Output:
Implementing Neural Networks Using TensorFlow
Deep learning has been on the rise in this decade and its applications are so wide-ranging and amazing that it’s almost hard to believe that it’s been only a few years in its advancements. And at the core of deep learning lies a basic “unit” that governs its architecture, yes, It’s neural networks.
A neural network architecture comprises a number of neurons or activation units as we call them, and this circuit of units serves their function of finding underlying relationships in data. And it’s mathematically proven that neural networks can find any kind of relation/function regardless of its complexity, provided it is deep/optimized enough, that is how much potential it has.
Now let’s learn to implement a neural network using TensorFlow
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