The below citation is a glimpse of our experiment using Thompson Sampling and Apriori Algorithm

In AI, the “exploration vs. exploitation tradeoff” applies to learning calculations that need to procure new information and expand their reward in the meantime — what are alluded to as Reinforcement Learning issues. In this setting, lament is characterized as you may expect: a reduction in remuneration because of executing the learning calculation as opposed to carrying on ideally from the earliest starting point. Calculations that streamline for investigation will in general acquire more lament.

We use the multi arm bandit concept in Machine learning and applied to our data set to find out a set of people in mall their, purchasing behaviour.With combination of Apriori algorithm and Thomson sampling we determined which way the the consumers will go in a mall after purchasing a product. With data analytics and physical experimentation we found that the arrangement of certain products resulted in increased sales.

The base code that we used for making changes in our logic is provided below.

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

dataset = pd.read_csv('Malldataset.csv')

# Implementing Thompson Sampling import random N = 250000 d = 700 ads_selected = [] numbers_of_rewards_1 = [0] * d numbers_of_rewards_0 = [0] * d total_reward = 0 for n in range(0, N): ad = 0 max_random = 0 for i in range(0, d): random_beta = random.betavariate(numbers_of_rewards_1[i] + 1, numbers_of_rewards_0[i] + 1) if random_beta > max_random: max_random = random_beta ad = i ads_selected.append(ad) reward = dataset.values[n, ad] if reward == 1: numbers_of_rewards_1[ad] = numbers_of_rewards_1[ad] + 1 else: numbers_of_rewards_0[ad] = numbers_of_rewards_0[ad] + 1 total_reward = total_reward + reward

plt.hist(ads_selected) plt.title('Histogram of Dataset') plt.xlabel('Ads') plt.ylabel('Number of users chooses a lane')

If you are taking your initial steps in Machine learning and have understood the the concepts and want to see it in a nice Gui based interface, you have come to right article.

Explore the Machine learning studio from Microsoft. The Gui based interface is very intuitive and structured and completed the entire life cycle from data preparation to model selection and deployment

The machine learning studio can be access from this link.  Once can start with Free Workspace plan which is  free plan till the usage does not exceed 10GB.

Microsoft – the GUI specialist company has did lot of work in building a studio which has easy features to drag and drop and offers several options to train and test your model. It has pre-built features to handle pre-processing task like missing value, categorical data, feature scaling and so on.

Users can also include there custom Python or R code and can specify the splits or regression models.

The below screenshot shows the workflow of machine learning cycle to be carried out using ML studio.

Machine Learning Studio from Microsoft

Understanding neural networks and machine learning concepts you might want to see them in action. An interactive browser based simulator from google Tensor Flow Playground.

This helps to  know what exactly is a neural network.

You can find it here

Written in Typescript d3.js  it contains  a neural network library that meets the demands of this educational visualization.

A very unique way to learn about neural network.   Instead of setting up so much infrastructure which is time consuming you can try this browser based application which has  the model is prebuilt and can be configured and trained to see the training curve.

This is like running your own machine learning instance for understanding the various aspects of parameters which affects performance.

One can change the parameters. The time taken for training the model is the crux of the application.

To see it in action one can add layers and neurons, change the data set , ratio and other parameters to see the effect of the parameters on the training of the model.

Neural Network in Browser


Neural Network in Browser