ResNet(Residual Networks) Explained – Deep Learning

In this blog post, we will explore the concept of residual networks in deep learning. Residual networks, also known as ResNets, have revolutionized the field of deep learning by enabling the training of extremely deep neural networks. We will discuss the motivation behind ResNets, their architecture, and how they address the challenges of training deep…

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Sigmoid Activation Function in Detail Explained

When it comes to artificial neural networks, the Sigmoid activation function is a real superstar! It might sound like a fancy term, but don’t worry; we’re going to break it down in a way that even your grandma would understand. What’s the Buzz About Activation Functions? Before we zoom in on the Sigmoid activation function,…

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Efficient Data Manipulation with Apply() Function in Pandas

If you’re a data enthusiast like me, you’ve probably dabbled in the world of Python and Pandas, the go-to library for data manipulation and analysis. Now, imagine you have a massive dataset with thousands of rows, and you want to perform some custom operations on it. Well, don’t fret! Pandas has your back, and it…

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Implementing Linear Regression from Scratch with Python

Linear regression is a fundamental machine learning algorithm that allows us to predict numerical values based on input data. In this article, we will see how to implement linear regression from scratch using Python. We will break down the code into simple steps, explaining each one along the way. So, let’s understand how linear regression…

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Understanding the Softmax Activation Function: A Detailed Explanation

The Softmax activation function is one of the most important activation function in artificial neural networks. Its primary purpose is to transform a vector of real numbers into a probability distribution, enabling us to make informed decisions based on the output probabilities. In this article, we will figure out the workings of the Softmax activation…

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Enhancing Image Visibility with Adaptive Thresholding

In this article we will be working on Enhancing Image Visibility with Adaptive Thresholding. You must have come across images that appear too dark or lack the necessary contrast to make out important details? Whether it’s a poorly lit photograph or a scanned document, such images can be challenging to interpret. Fortunately, there’s a powerful…

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Parkinson’s Disease Detection using Machine Learning Algorithm

In this step-by-step tutorial we will walk through the step-by-step process of building Parkinson’s Disease detection using machine learning. Parkinson’s Disease is a neurodegenerative disorder that affects millions of people worldwide. Early detection of the disease is crucial for effective management and treatment. In this article, we will explore how machine-learning techniques can be employed…

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Day 5: Everything you need to know about Activation Functions in Deep learning

Deep learning is a powerful area of ​​artificial intelligence that has received a lot of attention in recent years. One of the main components of deep learning models is the activation function. Activation functions play a crucial role in determining the output of a neural network. In this article, we will dive deep into understanding…

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How to get internship into Data Science

In today’s IT industry, data science has become a prominent buzzword. With many job opportunities and lucrative salaries, this is an exciting field to explore. If you want to start your career in data science, securing an internship is a great starting point. In this article, I’ll cover some valuable tips and tricks to help…

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Precision, Recall & F1: Understanding the Differences Easily Explained for ML

When it comes to evaluating the performance of our machine learning models, two main metrics are considered: precision and recall. These metrics are the basis for evaluating the effectiveness and reliability of the model’s prediction. In this article, we will discuss the complexities of precision and recall, their significance, and how they are used in…

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