Note: The code files will be available at : https://github.com/ashwinhprasad/Tensorflow-2.0/blob/master/TF2-(4)-ANN/TF2-ANNs.ipynb

Introduction

Artificial Neural Networks are the traditional neural networks which means that there are more than or equal to one layer between the input and the output layer. This allows for the model to adapt for the non linearity and form complex functions which makes it useful in real life.

This blog post does not covers only the implementation of feed forward or artificial neural network with tensorflow 2 and not the theory part of the artificial neural network.

Note : The Program files for tensorflow 2 can be found on - https://github.com/ashwinhprasad/Tensorflow-2.0

Logistic Regression is used for Classification tasks and This Blog will take you through the implementation of logistic regression using Tensorflow 2. This blog post won’t be covering about the theories regarding logistic regression and theory is a pre-requisite.

Let’s jump to the code part :

1. Importing The Dataset

The Dataset that is used in this example is iris dataset from the sklearn library.
we are importing the dataset and storing it in the form of a pandas dataframe

#importing the libraries import numpy as np import tensorflow as tf import pandas as pd import matplotlib.pyplot…

Code files will be available at : https://github.com/ashwinhprasad/Tensorflow-2.0

What is Linear Regression ?

Linear regression is basically using a equation of a line to find out the linear relationship between 2 variables. By finding the linear relationship, I mean finding the parameters ( Slope and Intercept).

y = m*x + c
y : dependent variable
x : independent variable
m : slope
c : intercept

Note : All code files will be available at https://github.com/ashwinhprasad/Tensorflow-2.0
This blog post will cover some basic functions that will be repeatedly used a lot in tensorflow 2.

Random

random.normal generates random values of the given shape, which follow normal distribution
and random.uniform generates random values in such a way that probability of choosing any number from the random bunch is almost uniform

#normal distribution
x1 = tf.random.normal(shape=(5,5),mean=0,stddev=1) 
#normal distribution
print(x1)output: tf.Tensor( [[-1.1473149e+00 5.1616412e-01 -2.8656033e-01 -1.4161720e-03 -6.7782238e-02] [ 1.5549400e-01 -1.8609362e+00 7.8299832e-01 -7.3712116e-01 -3.0330741e-01] [ 5.6524660e-02 1.0138390e-01 1.2218195e+00 1.2505690e+00 3.0457941e-01] [ 3.6436683e-01 -8.6699528e-01 1.5152076e+00 7.8330201e-01 -1.4127023e+00] [-1.2999429e+00 1.3505920e+00 1.0376108e+00 -1.5029492e+00 9.7778231e-01]]…

What is Tensorlfow ?

Tensorflow is a DeepLearning library which has a lot of inbuilt classes and functions which allow you to perform these complex deep learning matrix multiplications and gradient calculations easily. The main Goal behind tensorflow is to make developing machine learning models easier and get it to a production environment.

Advantages of Tensorflow 2 over The first Version

As everyone already know, The updated version of tensorflow allows the user to create models easily whereas it was quite difficult with tensorflow’s first version.

Installation

You could directly use tensorflow from google colab (I prefer this) or type
“pip install tensorflow” for windows users and
“pip3 install tensorflow2” for linux users

Tensor Basics

…

Note

sorry for misspelling network , lol.
All the code files will be available at : https://github.com/ashwinhprasad/PyTorch-For-DeepLearning

What are RNNs and LSTMs ?

Recurrence Neural Network are great for Sequence data and Time Series Data. Long short-term memory is an artificial recurrent neural network architecture used in the field of deep learning. LSTMs and RNNs are used for sequence data and can perform better for timeseries problems.

An LSTM is an advanced version of RNN and LSTM can remember things learnt earlier in the sequence using gates added to a regular RNN. Both LSTM’s and RNN’s working are similar in PyTorch. So, once we coded the Lstm…

Note : All the code files will be available at : https://github.com/ashwinhprasad/SentimentAnalysis

What is Sentiment Analysis ?

Sentiment analysis in simple words is basically analysing how an user feels about an item or any other thing from the user’s activity such as reviews , tweets, etc.

Jumping to Code

1. Importing the Libraries

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import nltk

2. Downloading NLTK

Use the nltk shell to download the english stopwords.

3. Importing the dataset

df = pd.read_csv('IMDB Dataset.csv')
df.head()

output :

Introduction

Recommender systems are the systems that are designed to recommend things to the user based on many different factors

Pearson’s Correlation Coefficient is a very simple yet effective way to find how 1 variable linearly changes with respect to another. we can use this to our advantage and build a recommender system with this concept

Working of correlation coefficient