<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="3.10.0">Jekyll</generator><link href="https://ritikakumari.in/feed.xml" rel="self" type="application/atom+xml" /><link href="https://ritikakumari.in/" rel="alternate" type="text/html" /><updated>2026-09-07T17:00:28+00:00</updated><id>https://ritikakumari.in/feed.xml</id><title type="html">Ritika Kumari</title><subtitle>Ritika Kumari&apos;s academic portfolio</subtitle><author><name>Ritika Kumari</name><email>ritikakumari1302@gmail.com</email></author><entry><title type="html">Brief Introduction to ML for Trading</title><link href="https://ritikakumari.in/Ml-for-trading/" rel="alternate" type="text/html" title="Brief Introduction to ML for Trading" /><published>2025-09-02T00:00:00+00:00</published><updated>2025-09-02T00:00:00+00:00</updated><id>https://ritikakumari.in/Ml-for-trading</id><content type="html" xml:base="https://ritikakumari.in/Ml-for-trading/"><![CDATA[<p>This summer I took a fascinating course — Machine Learning for Trading. For those who don’t know me, I’m pursuing my Master’s in Computer Science with a specialization in Machine Learning. Every semester, I like to reflect on what I’ve learned and share it in an abstract yet informative way. If you have even a little familiarity with Machine Learning and math concepts, you’ll find this article easy to follow.</p>]]></content><author><name>Ritika Kumari</name><email>ritikakumari1302@gmail.com</email></author><category term="machine learning" /><category term="trading" /><category term="statistics" /><summary type="html"><![CDATA[This summer I took a fascinating course — Machine Learning for Trading. For those who don’t know me, I’m pursuing my Master’s in Computer Science with a specialization in Machine Learning. Every semester, I like to reflect on what I’ve learned and share it in an abstract yet informative way. If you have even a little familiarity with Machine Learning and math concepts, you’ll find this article easy to follow.]]></summary></entry><entry><title type="html">Serving, Smashing and Starting Over</title><link href="https://ritikakumari.in/serving-smashing/" rel="alternate" type="text/html" title="Serving, Smashing and Starting Over" /><published>2025-04-04T00:00:00+00:00</published><updated>2025-04-04T00:00:00+00:00</updated><id>https://ritikakumari.in/serving-smashing</id><content type="html" xml:base="https://ritikakumari.in/serving-smashing/"><![CDATA[<p>I’m 25 now. Looking back, my sense of sports didn’t develop particularly early in my life. There were, of course, childhood games such as hide-and-seek, seven stones, blindfold, and many more. But these won’t be counted as real sports. At least they weren’t for me. Then, after a few years, we started playing gully cricket, badminton, and some basic version of football, all perhaps until 4th-5th standard.</p>]]></content><author><name>Ritika Kumari</name><email>ritikakumari1302@gmail.com</email></author><category term="sports" /><category term="badminton" /><category term="volleyball" /><summary type="html"><![CDATA[I’m 25 now. Looking back, my sense of sports didn’t develop particularly early in my life. There were, of course, childhood games such as hide-and-seek, seven stones, blindfold, and many more. But these won’t be counted as real sports. At least they weren’t for me. Then, after a few years, we started playing gully cricket, badminton, and some basic version of football, all perhaps until 4th-5th standard.]]></summary></entry><entry><title type="html">Computational Graphs and Automatic Differentiation</title><link href="https://ritikakumari.in/computational-graphs/" rel="alternate" type="text/html" title="Computational Graphs and Automatic Differentiation" /><published>2024-09-02T00:00:00+00:00</published><updated>2024-09-02T00:00:00+00:00</updated><id>https://ritikakumari.in/computational-graphs</id><content type="html" xml:base="https://ritikakumari.in/computational-graphs/"><![CDATA[<p>If you’ve ever worked with deep learning models and, like me, wondered how machine learning libraries like TensorFlow and PyTorch handle large neural networks so efficiently, this blog is for you.</p>]]></content><author><name>Ritika Kumari</name><email>ritikakumari1302@gmail.com</email></author><category term="Deep Learning" /><category term="Machine Learning" /><category term="Differentiation" /><category term="Graphs" /><summary type="html"><![CDATA[If you’ve ever worked with deep learning models and, like me, wondered how machine learning libraries like TensorFlow and PyTorch handle large neural networks so efficiently, this blog is for you.]]></summary></entry><entry><title type="html">Knowledge Graphs: How to build from text</title><link href="https://ritikakumari.in/knowledge-graphs/" rel="alternate" type="text/html" title="Knowledge Graphs: How to build from text" /><published>2023-02-05T00:00:00+00:00</published><updated>2023-02-05T00:00:00+00:00</updated><id>https://ritikakumari.in/knowledge-graphs</id><content type="html" xml:base="https://ritikakumari.in/knowledge-graphs/"><![CDATA[<p>First things first, have you heard of Knowledge Graphs before? Not asking if you know what it is, just if you overheard someone talking about it or casually read the term somewhere. If yes, amazing! If no then also amazing because we will go through it in as demystified way as possible.</p>]]></content><author><name>Ritika Kumari</name><email>ritikakumari1302@gmail.com</email></author><category term="Knowledge Graph" /><category term="NLP" /><category term="Search" /><summary type="html"><![CDATA[First things first, have you heard of Knowledge Graphs before? Not asking if you know what it is, just if you overheard someone talking about it or casually read the term somewhere. If yes, amazing! If no then also amazing because we will go through it in as demystified way as possible.]]></summary></entry></feed>