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<!DOCTYPE html>
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<p style="padding-top: 1%; font-size:xx-large">BLOGS</p>
<!-- <hr style="background-color: black; width: 50%;"> -->
<p style="font-size: large;" class="ill-text-b">“Don’t focus on having a great blog. Focus on producing a blog that’s great for your readers.”</p>
<p></p>
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<div class="blg" style="margin-top: 10%;">
<h2 class="text-center" style="margin-bottom: 4%;">BLOGS BY SMLRA</h2>
<div class="card-deck " style="padding-left: 10%;padding-right: 10% ">
<div class="card shadow c1" style="border-width: 0px; width:27rem; margin-bottom: 5%;">
<a href="https://smlra-kjsce.medium.com/vectorized-view-of-svms-6a85dae8ca72">
<img class="card-img-top" src="./Images/blog4_img.PNG" alt="blog">
<div class="card-body text-dark">
<h5 class="card-title">The Secrets of SVMs </h5>
<p class="card-text">This article is continuation of 2nd part in “ML-DL-101” series -
“Supporting SVMs (Support Vector Machines)”. You want to give it a read before going
through this one. In this 4th blog, we will dive deeper into the intricacies of SVMs
and their working.It is one of the most widely used algorithms today every ML Engineer should
have it in it’s toolbox of ML algorithms. You will also get to know about Quadratic Programming which is
widely used in solving optimization problems. Without further ado,
let’s get started in this amazing journey to understand SVM's and explore it to
the fullest. Let's begin this journey. </p>
<p class="card-text"><small class="text-muted">30th April 2020</small></p>
</div>
</a>
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<div class="card shadow c1" style="height:max-content;border-width: 0px; width:27rem; margin-bottom: 5%;">
<a href="https://smlra-kjsce.medium.com/back-to-basics-with-linear-regression-78dd7106f7bd">
<img class="card-img-top" src="./Images/blog3_img.PNG" alt="blog" >
<div class="card-body text-dark">
<h5 class="card-title">Back to Basics with Linear Regression</h5>
<p class="card-text">
In this 3rd article of the “ML-DL-101” series we introduce you to the most basic
and simple yet powerful machine learning algorithm that almost every newbie to ML
starts off with - “Linear Regression”. We will cover linear regression with one
variable and also show how linear regression can be extended to accommodate
multiple input variables, namely “Multivariate Linear Regression”. Let’s Begin.
</p>
<p class="card-text"><small class="text-muted">25th March 2020</small></p>
</div>
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<div class="card shadow c1" style="height:max-content;border-width: 0px;width:27rem; margin-bottom: 5%; ">
<a href="https://smlra-kjsce.medium.com/supporting-svms-support-vector-machines-bf5bfba88a0b">
<img class="card-img-top" src="./Images/blog2_img.PNG" alt="blog">
<div class="card-body text-dark">
<h5 class="card-title">Supporting SVMs (Support Vector Machines)</h5>
<p class="card-text">
Welcome back to the 2nd part of “ML-DL-101” series.
In this article we will uncover the details of one of the most powerful “black-box”
learning algorithm - “Support Vector Machines”.
It is one of the most widely used algorithms today and every ML Engineer should
have it in it’s toolbox. But before getting started, make
sure you have a fine grasp of these 2 ML models - Linear and Logistic Regression.
</p>
<p class="card-text "><small class="text-muted">20th March 2020</small></p>
</div>
</a>
</div>
<div class="card shadow c1" style="border-width: 0px; width:27rem; margin-bottom: 5%;">
<a href="https://smlra-kjsce.medium.com/understanding-decision-trees-8a3a78494836">
<img class="card-img-top" src="./Images/blog1_img.PNG" alt="blog">
<div class="card-body text-dark">
<h5 class="card-title">Understanding Decision Trees</h5>
<p class="card-text">In this first article of “ML-DL-101” series we introduce
you to one of most common and easy to use/interpret machine learning
algorithm called “Decision Trees”. We will look under the hood and understand
it’s working and gain insight on the math involved . We will also touch upon
“Random Forests” which will involve creating a forest of multiple decision trees.
So, let’s get started.</p>
<p class="card-text"><small class="text-muted">10th Feb 2020</small></p>
</div>
</a>
</div>
</div>
<div class="card-deck " style="padding-left: 10%;padding-right: 10% ">
<div class="card shadow c1" style="height:max-content;border-width: 0px; width:27rem; margin-bottom: 5%; ">
<a href="https://smlra-kjsce.medium.com/whats-the-res-in-resnet-part-ii-96612490cb8e">
<img class="card-img-top" src="./Images/blog6_img.PNG" alt="blog">
<div class="card-body text-dark">
<h5 class="card-title">What’s the “Res” in “ResNet”? [Part II]</h5>
<p class="card-text">Welcome back to the 2nd and last part of the blog - What’s the “Res” in
“ResNet”? In this blog we will mainly talk about how the issue of degradation is addressed by
ResNet and how Residual Learning eases the training of deep networks which are easier to
optimize and can gain accuracy from considerably increased depth.</p>
<p class="card-text"><small class="text-muted">16th December 2019</small></p>
</div>
</a>
</div>
<div class="card shadow c1" style="border-width: 0px; width:27rem; margin-bottom: 5%;">
<a href="https://smlra-kjsce.medium.com/whats-the-res-in-resnet-part-i-389116531796">
<img class="card-img-top" src="./Images/blog5_img.PNG" alt="blog">
<div class="card-body text-dark">
<h5 class="card-title">What’s the “Res” in “ResNet”? [Part I]</h5>
<p class="card-text">This blog talks about ResNet - A Deep Residual learning framework inspired
from the research paper: “Deep Residual Learning for Image Recognition”. In this 1st Part,
you will get to know about ResNet’s success story and the “Degradation problem” -
A common issue when dealing with deep neural networks having hundreds of layers.
Do give a brief read to the research paper before you begin.</p>
<p class="card-text"><small class="text-muted">6th September 2019</small></p>
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