Forem: Souvik Roy

AI Art Generation Using Modern Age AI

Souvik Roy — Fri, 14 Apr 2023 10:04:12 +0000

AI Art Generation Overview
By : Souvik Roy
College: RCCIIT
University : MAKAUT

Introduction:
Art has always been an essential aspect of human culture and history. Over the years, various art movements and styles have emerged, driven by advancements in technology and cultural influences. The advent of Artificial Intelligence (AI) has brought a new dimension to art creation and curation. In recent times, AI art generation has gained significant attention, prompting researchers and artists to explore its potential. This report provides an overview of AI art generation, its techniques, and applications.

AI Art Generation Techniques:
AI art generation involves the use of machine learning algorithms to generate artwork automatically. The techniques employed in AI art generation vary, but they all rely on training algorithms on large datasets of existing art. Some of the techniques used include:

Style Transfer:
This technique involves using a pre-trained neural network to transfer the style of one image onto another image. The algorithm extracts the content from the first image and merges it with the style of the second image, producing a unique artwork.

Generative Adversarial Networks (GANs):
GANs consist of two neural networks, a generator, and a discriminator. The generator creates new images, while the discriminator evaluates the images' authenticity. The two networks are trained together until the generator produces images that are indistinguishable from the real images.

Variational Autoencoder (VAE):
VAEs are neural networks that learn to compress and decompress data. In the context of AI art generation, VAEs learn to encode and decode images, allowing artists to generate new images by manipulating the encoding.

Applications of AI Art Generation:
AI art generation has several applications, including:

Commercial Art:
AI art generation has the potential to revolutionize the commercial art industry by providing a cost-effective and time-efficient way of producing art. It can be used to create unique pieces for commercial spaces, hotels, and restaurants.

Personalized Art:
AI art generation can be used to create personalized art for individuals. It can be used to generate artworks based on a person's preferences, interests, and characteristics.

Artistic Exploration:
AI art generation can be used to explore new art forms and styles that were previously impossible to create. Artists can use AI-generated art as a source of inspiration to create new art forms and push the boundaries of traditional art.

Conclusion:
AI art generation is a rapidly evolving field with immense potential. Its application in commercial and personalized art can revolutionize the art industry, making it more accessible and affordable. It also provides artists with new avenues to explore and experiment with new art forms and styles. While AI-generated art has received criticism from some quarters for lacking the authenticity and emotional depth of human-created art, it is clear that AI art generation is here to stay and will continue to impact the art world in significant ways.

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makaut

How the Y2K Problem Led to Growth of NLP Development In the World

Souvik Roy — Fri, 14 Apr 2023 06:07:11 +0000

Author:
Souvik Roy,
College: RCC INSTITUTE OF INFORMATION TECHNOLOGY
University: MAKAUT

The Y2K problem, also known as the Millennium Bug, was a computer programming issue that arose in the late 1990s. At that time, most computer systems used two digits to represent the year in date values. As a result, there were concerns that when the year 2000 arrived, these systems would interpret the year as 1900 instead of 2000, leading to a variety of problems.

To understand how the Y2K problem developed and its implications for NLP today, it is helpful to examine the historical context. In the early days of computing, memory and storage were expensive, and programmers sought ways to save space. One solution was to use two digits to represent the year, rather than four. This approach worked well until the year 2000 approached, and computer systems would have to determine whether a date value was referring to the year 1900 or the year 2000.

The potential consequences of the Y2K problem were significant. If systems interpreted the year as 1900, it could lead to incorrect calculations and data corruption, potentially affecting critical infrastructure like power grids, financial systems, and transportation networks. Governments and businesses around the world invested billions of dollars in Y2K remediation efforts to update their systems and avoid these issues.

In the field of NLP, the Y2K problem demonstrated the importance of accurate data processing and analysis. Language models and other NLP tools rely on accurate data to generate insights and predictions. If the data is corrupted or inaccurate, it can lead to flawed results and unreliable recommendations. The Y2K problem also highlights the importance of testing and validation in software development, as well as the need for ongoing maintenance and updates to ensure that systems remain functional and secure.

In conclusion, the Y2K problem was a significant event in the history of computing, highlighting the potential consequences of programming errors and the importance of accurate data processing. While the Y2K problem has largely been resolved, its legacy continues to inform best practices in software development and NLP today.

Natural Language Processing (NLP) is a field of artificial intelligence that focuses on enabling machines to understand, interpret, and generate human language. NLP has made tremendous progress in recent years, with advancements in machine learning, deep learning, and other technologies.

One area where NLP has seen significant growth is in chatbots and virtual assistants. These technologies use NLP to understand and respond to user requests and inquiries, providing a more personalized and interactive experience. Companies are increasingly leveraging chatbots and virtual assistants to improve customer service, automate tasks, and enhance engagement with their users.

Another area where NLP is making an impact is in sentiment analysis. Sentiment analysis uses NLP to analyze the emotions and opinions expressed in text data, such as social media posts, customer feedback, and reviews. This information can help companies better understand their customers and their needs, enabling them to make more informed business decisions.

NLP is also being used in healthcare, where it is helping to improve patient outcomes and reduce costs. NLP is used to extract relevant information from electronic health records, such as patient histories and medical diagnoses, to help doctors make more accurate diagnoses and treatment plans. It is also being used to automate administrative tasks, such as billing and insurance claims.

In conclusion, NLP is an exciting field that is driving innovation and impacting many industries. From chatbots and virtual assistants to sentiment analysis and healthcare, NLP is transforming the way we interact with machines and making our lives easier and more productive.

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makaut_mar

Streamlit: The Best Way to Begin(Free and Paid Resources)

Souvik Roy — Sun, 03 Jul 2022 18:24:49 +0000

About Streamlit :

Streamlit is a library/framework in python that helps you make interactive data driven web application without writing any complicated codes.

Special Advantages:

Streamlit can help you achieve complicated workings with minimalistic (few lines) of code. More than that there are other advantages of using streamlit like :

Connecting it with react framework is lucid and a piece of cake.
The in-built framework is completely responsive.
Integrating ML models had never been easier.
You can also make CRUD (Create Read Update Delete) applications using streamlit and it also support data base connectivity.
Facilitates interfaces to drive data uploads in various file formats.

Resources To Use:

Books and Projects

Apart from the official page of streamlit recently I just received a copy of a book from Shifa Ansari and this book is your one stop solution for learning python. The link to the book is here :

Getting Started with Streamlit for Data Science

I'm also going to share a few project links to my gitHub repos that have data science based apps created using streamlit. Go through the codebases and I'm sure it will be helpful for you all.

Online Tutorials:

Beginner Framework Familiarity Stuff
Underrated Channel With end to end tutorials
Advanced and Moderate Data Science Stuff

By the way an important announcement, In case this blog generates a good amount of views then I'm sharing a free copy of the above mentioned book to the first 100 comments and likes.

Deployment of ML and Data Science Apps

Souvik Roy — Sat, 05 Feb 2022 18:41:23 +0000

So here comes the most awaited part of the most talked about yet the most undisclosed knowledge in the field of ML or Data Science.

Well that is the deployment of the applications as web app and other integrated forms. Now Python being the programming tool for both the communities has a lot to play here. As we mentioned that Tableau and Excel are some platforms to perform data science, they holdback in terms of deployment as an application or something that can hold its own as a standalone web app.

Streamlit is the best and the most easy to implement framework for web deployment for data scientists with a little kink for coding. You can customize the UI but its still limited and you need to force in some hybrid frontend tech stack to do so.

Flask on the other hand can help you get the best out of the UI part. Both the libraries are easy to be implemented and a lot fun to be used.

Here, I will link some projects of the same genre that I had posted.

Movie Recommendation System - Flask

Laptop Price Predictor

Data Science Classifier App

Well all the things that you need to add and maintain are there, mentioned in the repositories.

Here is a link to one blog that can help you further.

https://towardsdatascience.com/3-ways-to-deploy-machine-learning-models-in-production-cdba15b00e#:~:text=The%20simplest%20way%20to%20deploy,classifier%20built%20with%20scikit%2Dlearn.

https://towardsdatascience.com/building-a-machine-learning-web-application-using-flask-29fa9ea11dac

Now, I have also created a PWA that has all the major ML apps available for viewing the code and the deployed versions.

Streamlit - Hub

The above app also showcases the wonders that Streamlit library can do. However, that topic requires a dedicated blog that is soon to be released.
Recently there has been another method added to the arsenal.

Read about this thing called : Mercury

Read about it here :
https://towardsdatascience.com/create-a-web-app-from-your-jupyter-notebook-with-mercury-21239b7abb37

Those who are willing to learn ML and Data Science in depth, I have some good news to share, my book on the same topic is releasing in two months

Stay-Tuned

Segregating The Career Path For ML Engineer and Data Scientist - Part I

Souvik Roy — Sun, 30 Jan 2022 19:56:19 +0000

So literally everyone in the freshers community with a zeal for Machine Learning and Data Science tend to make one common mistake. They tend to consider the two fields Data Science and Machine Learning as one and same. What's worse is that they think that the two of them are :

building blocks to each other that one needs to master data science to become a machine learning master and vice versa.

Well let me explain the key difference between the two and then we would move on with some resources that I would personally recommend for each. For the link and guidance to the projects that you can undertake to affirm your chances in both the career paths you would have to follow my tailfeed blog whose link will be provided by me at the end of this article.

Let's try to grab the scenario with a little pic first.

Now let me explain this in a layman manner before you start to process a plethora of articles to curb your thirst for knowledge.

What is Data?

Data is any meaningful information that can serve as an insight for any particular topic and whose relevance can help predict further sets of data classifying the same in *category, origin and mannerism. *

Pretty long of a definition right? Well, as a veteran data scientist data is something you need to value and therefore to value something you must understand its worth.

So What is Data Science now?

Well, Data Science is the ology behind understanding, manipulating and reshaping data to suck the information shown, provided, concealed and obscured from normal eyes and turn it into a plot, an idea and an insight and tell its incomplete story.

And what is Machine Learning?

Well Machine Learning is the mathematical inference being drawn on the manipulated or pre-mutated data to derive or fetch an outcome. Yes, both fields have a connection. That connection is the Outcome of the processes but what they do not have in common is the process or path the two methodologies will follow to achieve the outcome.

Moreover Machine Learning is all about mathematical functions and coding and Data Science is about statistical inference that may or may not need coding all the times.

So, the next question that is definitive to arise in your minds is which of the two has better job perspectives. Well data scientists are hard to be found and are usually the ones with high degrees which in general means a lot more years of dedicated studies.
This may or may not be the case with the introduction of open source contributions and a number of organizations and competitions announcing and developing the same. I have myself landed offers in three startups as a chief data scientist merely due to my open source contributions.

Machine Learning is technical and the mathematical concepts here require knowledge and practice and not some high level of creativity. A Machine Learning Engineer is a man in demand but definitely his job won't even start without the data in hand.

Now, here comes the next perspective that with APIs and Real Time Data Fetching libraries at the lease you barely need to focus on the deployment of a data set to begin with for a small company. ML internships have personally provided me with very boring roles as a fresher compared to what I have been offered as a data scientist. But I have to tell you that Saurabh Moody, the Chief Data Scientist at Alpha AI had mentioned that he had chosen me for my varied knowledge in both the fields and also due to my projects being full-stack applications deploying ML on mutated data.

So, in the IT world you do need to be a Jack Of All in order to land a great role at the beginning of your career.

Now let me do one thing to sweeten your life. I'll just plug in the links to all the free resources you need to evolve into your data science career, followed by the ML career and on the way I would drop in the links to the paid courses here on Udemy and my company The Divine Academy.

Free :

For theoretical knowledge read the following :

https://www.tutorialspoint.com/python_data_science/index.htm

https://realpython.com/tutorials/data-science/

https://www.w3schools.com/python/python_ml_getting_started.asp

https://www.tutorialspoint.com/machine_learning_with_python/index.htm

For paid courses :

https://www.udemy.com/course-dashboard-redirect/?course_id=1754098

https://www.udemy.com/course-dashboard-redirect/?course_id=3518544

Free Videos :

https://www.youtube.com/watch?v=H4YcqULY1-Q

https://www.youtube.com/watch?v=-ETQ97mXXF0

https://m.youtube.com/c/DataProfessor

You can also learn Flask and Streamlit for deployment purpose.

I will be sharing the links to all the projects that I had made and about web integrated frameworks and the JS and Python libraries in the Tailfeed Blog of mine.

The link to that PART - II will be updated here on this very same blog in a few days so please make sure to check back, and do let me know if I should also add a full vide explanation for detailed career path reveal or something.

Part II - Done : Follow here

A Development Project With Awesome Graph Algorithm Implementation And PWA

Souvik Roy — Wed, 20 Oct 2021 20:17:16 +0000

So in this single project I chose to sharpen my skills both as a developer and a coder and what better way could be there than to create a website to showcase easy but cool games made from scratch. In this one project I learned javaScript from basic to advanced level and also mastered the art of creating PWA.

The fact that I had challenged myself to learn graph algorithms took another level of a turn when I was able to understand the minimax algorithm and use beginner friendly JavaScript code to make it come to life.

The code for the project is in the repo link given below, be sure to download and clone and edit it and commit necessary UI changes as per your need I so wanted to do this in react but then again I wanted a project to be created in native JavaScript and focus more on logic building. Therefore the UI is obviously not great for that completely beats the purpose of the project. The App has a link to redirect you to my blog's PWA and that thing has my front-end skills showcased well. Let me link down both of them below:

This PWA Gaming App Code : https://github.com/Souvik1406/gaming-project-minimaxalgo-setservicePWA-implementaion-L_Heaven

My Portfolio Page Code : https://github.com/Souvik1406/advancedblog

You can find the hosted versions of the project on the following links :

Blog : https://souvikblog2-0new.netlify.app/
Game : https://minimax-based-game-pwa-by-souvik-roy.netlify.app/

Sparks Foundation Banking Project - Converted to Full-Stack Mode With PHP and MySQL

Souvik Roy — Wed, 20 Oct 2021 13:10:49 +0000

The Project that they had assigned was quite simple. As a good developer I did what they asked me to, however the researcher in me aroused the gig for more and I decided to convert this basic project into a complete full stack project with a login,signUp system and three admin panels. This project uses the traditional PHP and MySQL framework and the files to the can be downloaded along with the source code through the following link :
https://github.com/Souvik1406/The-Sparks-Foundation-Basic-Banking-System-Internship-Project

To Read more about the project go to my DevPost Portfolio : https://devpost.com/software/complete-banking-system-full-stack