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<!DOCTYPE html>
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<title>Movie Review | About</title>
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<h1 class="site-title">Ripe Cucumbers</h1>
<small class="site-description">Smell What is Being Cooked</small>
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<li class="menu-item current-menu-item"><a href="about.html">About</a></li>
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<span>About us</span>
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<div class="col-md-4">
<figure><img src="dummy/figure.jpg" alt="figure image"></figure>
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<p class="leading"> How we discovered the greatest picke recipe to satisfy all your movie cravings...</p>
<p style="font-family:verdana;">At the dawn of time, and some billion years after that, 3 travelers set out on the C.F.D pathway to discover the most pickilish ML recipe of all to satiate the hunger of movies... Staring at the visual basic valley,full of tickilish streams and gullies, traversing the long and tiring desert of Azure ML studio. Facing the ferocious winds of HTML and the tricky passes of CSS and bootstrap, guided by the nodeJSbeacon at all times and facing the toughest trek known to mankind, Mt. Prose. Never did they falter, nay all challenges that stood before them, coding and coding through to emerge VICTORIOUS!
</p style="font-family:verdana;">
</div>
</div>
<div class="row">
<div class = "col-md-12">
<thead>
<p style="font-family:verdana;">The story is about a small town in Shire, where movies flowed in abundance, and which had a very good Internet connection to watch these movies. Newcomers marvelled at the variety of storylines that were just 1 click away, but no one understood the suffering of the inhabitants.
To come up with a wise and just magician who could predict the success of tide after tide of new films. A most magnificent review system was the most deep-rooted desire of one and all.</p><p style="font-family:verdana;"> So 3 common folks (with those wide grins at the end of this page) took it upon themselves to builds and train such a magician.
The elves of microsoft, in the wise council of Elrond a.k.a C.F.D bootcamp laid down the pathway and assisted with tools for what would become the greatest construct of human endeavours. The first challenge that presented itself was to find the natural herbs and shrubs (more commonly known as data), its scrapping, extraction and transformation.
From far and distant lands of the computer center, permission was granted to build a website to store data which could be accessed for hosting the data. But the short victory was short-lived, as the thunder clouds brought an ominous feeling, the data was in the form of a JSON!!</p><p style="font-family:verdana;"> Such a data could not be trained, it could have a counter-effect on
model.They could get data that is hosted on a web URL that uses HTTP and that has been provided in the CSV, TSV, ARFF, or SvmLight formats. There was only 1 viable option in sight, Mt Prose.. The 3 wise people spent night and days preparing for prose, downloading nuget package managers, running sample codes but all in vain. Then there was discovered the short pass of word plug-in. In the bootcamp, there was a mention of a special of the ancient technique to code by example to transform text
as one wished, surely it was dark magic! The 3 found the code in the github forest, and in correspondence with the wise creator of this magic.</p><p style="font-family:verdana;">As the plans to metamorphise data progressed, the playing fields of visual basic was unfurled and the packages installed from the package manager console. On running the project, the plug-in in word was utilized for the seemingly magical metamorphosis as the public witnessed coding without actually coding! Just through examples. Codes were downloaded, e-messages were shared, prose packages were downloaded, components of UI and XmlHttpResponse were called to an api that uses prose nuget packages to convert the json to normal string to be used as as feature.</p><p style="font-family:verdana;">
With the data now available for use, it was time to make features.With the data converted as an Azure model, it became imperative to handle the loops and holes in the data. The clean missing data module was resurrected to open its third eye to discover the missing data and replace it. Whoever had some knowledge about the magic of predicting knew how important it was to own the lands of normalization. Whole predictive battles could be reversed with normalization, so a data normalization module was
brought from the dungeons of Mordaur. The Zsquare method of normalization was employed which uses the mean and the standard deviation.</p><p style="font-family:verdana;"> It was imperative to select only the proper columns for the magician, for wrong choices could backfire and break hell upon the model. The select column module, which could include and exclude columns at will was called upon from the land of dwarves. After a heated discussion on which features were important, a final dataset was agreed upon which would form the basis of the future pickle recipe. This pickle recipe would one-day predict wonders. With the best cucumbers picked in the cradle of visual basic and strengthened with prose, next step was normalization.
The magician only required same size ripe cucumbers so to satiate these demands, normalization technique was employed. The rotten cucumbers were cleaned, replaced using missing columns. It identifies the inputs with missing data and substitutes it with wither pre-decided values or with statistical functions.</p><p style="font-family:verdana;"> The most important part of any pickle recipe is the spices and their quantity. A long and draining discussion about the quantity of the spices, which led to no consensus. The recipe seemed to be coming to stagnation, when the wise wizard Gandalf took the job. A hyper-parameter tuning module was set up. Hyper-parameters are the parameters that are given to the program pre-execution, which affect the learning of the model
he module builds and tests models multiple models, using different combinations of settings, and compares metrics over all models to get the combination of settings.The terms parameter and hyperparameter can be confusing. The model's parameters are what you set in the properties pane. Basically, this module performs a parameter sweep over the specified parameter settings, and learns an optimal set of hyperparameters, which might be different for each specific decision tree, dataset, or regression method. The process of finding the optimal configuration is sometimes called tuning. Hyper-parameter tuning using something called parameter-sweep. When you set up a parameter sweep, you define the scope of your search, to use either a finite number of parameters selected randomly, or an exhaustive search over a parameter space you define.
Accuracy is used as a parametric for tuning.</p><p style="font-family:verdana;">What makes hyper-parameter tuning possible is partition and sample. Sampling is an important tool in machine learning because it lets you reduce the size of a dataset while maintaining the same ratio of values. It works by dividing your data into multiple subsections of the same size, separating data into groups and then working with data from a specific group., sampling (You can extract a percentage of the data, apply random sampling, or choose a column to use for balancing the dataset and perform stratified sampling on its values) and finally creating a smaller dataset for testing.</p><p style="font-family:verdana;">
The most advanced training algo was decide upon, the neural network. A neural network is a set of interconnected layers. The inputs are the first layer, and are connected to an output layer by an acyclic graph comprised of weighted edges and nodes.Between the input and output layers you can insert multiple hidden layers. Most predictive tasks can be accomplished easily with only one or a few hidden layers. However, recent research has shown that deep neural networks (DNN) with many layers can be very effective in complex tasks such as image or speech recognition. The successive layers are used to model increasing levels of semantic depth.The relationship between inputs and outputs is learned from training the neural network on the input data. The direction of the graph proceeds from the inputs through the hidden layer and to the output layer. All nodes in a layer are connected by the weighted edges to nodes in the next layer.To compute the output of the network for a particular input, a value is calculated at each node in the hidden layers and in the output layer. The value is set by calculating the weighted sum of the values of the nodes from the previous layer. An activation function is then applied to that weighted sum.
Scoring and evaluation are done by score model and evaluate model. Thus the model was trained and deployed as a web service, destined to change anything and everything.
</thead>
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<h2 class="section-title">Vision & Mission</h2>
<p>Think of a situation. You have been caught stealing candles by a police officer. He takes you in his custody and you beg for mercy. Then he receives a call from his wife who has become very bored. You overhear their conversation and the open our application. You recommend a film to the officer and his wife. Both enjoy the film and your life is saved. We want to save your life. That is our sole purpose. </p>
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<h2 class="section-title">Useful Links</h2>
<ul class="arrow">
<li><a href="https://azure.microsoft.com/en-in/services/machine-learning-studio/">The studio we used</a></li>
<li><a href="https://microsoft.github.io/prose/">Data extraction</a></li>
<li><a href = "https://home.iitk.ac.in/~kushgpt"> Our DataSource</a></li>
<li><a href="https://github.com/Microsoft/prose">Info regarding data extraction</a></li>
<li><a href="https://www.visualstudio.com/">IDE</a></li>
<li><a href="https://www.coursera.org/learn/machine-learning">Learn ML</a></li>
</ul>
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<h2 class="section-title">Our Team</h2>
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<h2 class="team-name">Kushagra Gupta</h2>
<small class="team-title">CFO</small>
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<h2 class="team-name">Aditya Mishra</h2>
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<p>We are the newest idea in the cinema bizz and you have to visit us. That is all.</p>
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