Deekshant Yadav
Data Science Intern
d33kshant¶
Hey, there! I am Deekshant, a data science intern and a master's student in artificial intelligence. I have a background in computer science and past experience as a full-stack web developer, mainly in backend. I enjoy working with Python, AI-ML and open-source projects. I also like writing about my experiences and sharing what I learn.
Projects¶
I prefer project based learning thats why I always try to apply my learning on small or big projects. Over the years I've worked on many projects related to Web, AI and Open Source.
Pinned Projects¶
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Stock Price Prediction with LSTM
A Long Short-Term Memory (LSTM) neural network to predict stock prices
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HMM A Minimal Language Model
Probability based language modeling to create a very minimal language model
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Store w/ Recommendation System
An online commerece platform with content based recommendation system
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Snake Game Bot
A bot that plays classic snake game with reinforcement learning
Blogs¶
I do not write alot but often share my learning, opinions or experience time to time. I use AI to rectify and summaries the content to make it more expressive
Recent Blogs¶
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Introduction to Machine Learning
Have you ever wondered how Netflix knows exactly what show you might want to watch next? Or how your email automatically filters spam messages? Or maybe how your phone recognizes your face? All of these technological wonders are powered by machine learning.
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Implementing Convolutional Neural Networks using Tensorflow
Convolutional Neural Networks (CNNs) are a class of deep learning models that excel at working with image data. Instead of processing each pixel independently (like in a fully connected neural network), CNNs use filters (or kernels) to scan across the image, capturing spatial hierarchies and local patterns like edges, textures, and shapes.
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Implementing Principal Component Analysis (PCA) from Scratch
Principal Component Analysis (PCA) is a widely used technique for reducing the dimensionality of datasets while retaining the most important information. It does this by transforming correlated variables into a smaller set of uncorrelated variables called principal components.