Machine Learning | Deep Learning | Data Science | Web Dev

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

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.

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*

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 + cy : dependent variablex : independent variablem : slopec : 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.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]], …

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.

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.

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

Tensors are simply n-dimensional arrays. …

sorry for misspelling network , lol.

All the code files will be available at : https://github.com/ashwinhprasad/PyTorch-For-DeepLearning

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 Part, RNNs will also be easier to understand. …

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

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.

**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 :**

**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

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

**Going Merry** is a chatbot that I created for a **pirate recruitment process**. It helps in recruitment of pirates all around the world. this answer user’s simple questions regarding the recruitment process, pre-requisites, etc.This same model can also be used for creating chatbots for any organization

A chatbot is a software application used to conduct an on-line chat conversation via text . In this blog post, I will show how to create a Simple Chatbot with tensorflow 2 for your organization.

once, the dataset is built . half the work is already done. the way we structure the dataset is the main thing in chatbot. I have used a json file to create a the dataset. …

*All the code files will be available at : **https://github.com/ashwinhprasad/Outliers-Detection/blob/master/Outliers.ipynb*

Anything that is unusual and deviates from the standard “normal” is called an **Anomaly **or an** Outlier.**Detecting these anomalies in the given data is called as anomaly detection.

For more theoretical information about outlier or anomaly detection, Check out :** How Anomaly Detection Works ?**

**Case 1 : **Consider a situation where a big manufacturing company is manufacturing an airplane. An airplane has different parts and we don’t want any parts to behave in an unusual way. these unusual behaviours might be because of various reasons. …