Author: Arunangshu Das

Debugging is an essential skill for every developer, and when it comes to JavaScript, Chrome DevTools is one of the most powerful tools at your disposal. Whether you’re dealing with unexpected errors or trying to optimize your code, DevTools provides a suite of features that can make your life easier. Why Use Chrome DevTools? Pros: Free and Built-In: Chrome DevTools is available in the Chrome browser, eliminating the need for additional software. Feature-Rich: It offers a variety of tools for debugging, performance analysis, and even design tweaks. Live Editing: You can make changes directly in the browser and see the…

Chrome DevTools is an indispensable tool for web developers, offering far more than just the basic console log outputs and DOM inspection. Beneath its surface lies a treasure trove of hidden features that can dramatically boost your productivity and debugging prowess. Let’s uncover 10 lesser-known gems that every developer should know. 1. Command Menu for Quick Access Press Ctrl + Shift + P (Windows) or Cmd + Shift + P (Mac) to open the Command Menu. This acts as a search bar for DevTools, allowing you to access nearly any tool or feature by typing its name. Whether it’s enabling…

Node.js has been a favorite in the tech community for over a decade, known for its ability to efficiently manage concurrent connections and power high-performance applications. However, one pressing question persists: Can Node.js handle millions of users? The short answer is yes, but the reality is nuanced. While Node is inherently scalable, its performance at such a scale depends on how well the application is designed, optimized, and managed. Why Node.js Excels at Handling Traffic Node architecture is its superpower. Unlike traditional server frameworks that create a new thread for each connection, Node employs an event-driven, non-blocking I/O model. This…

Comparing VGG and LeNet-5 Architectures: Huge importance to solving complex problems such as image recognition, object detection, and many more are held by convolutional neural networks (CNNs) in the modern fast-developing directions of deep learning. From a vast family of CNN structures, LeNet-5 and VGG can be considered the most enshrine examples. All these differences are due to a fundamental difference in the design philosophy and performance between these classes of amplifiers. In this blog post, the author reviews the characteristics and the best applications of these architectures. Overview of LeNet-5   LeNet-5 is one of the first CNN patterns, and…

Deep Learning Regression: Regression is one of the most important ML concepts on which DL relies while solving problems that demand output in continuous values. Unlike other models of learning that aim at making classifications of certain labels, Regression is centered on numerical values. This article offers an understanding of Regression in deep learning, some of the most common uses of the algorithm, and various important approaches that can assist you in harnessing the power of the algorithm successfully.   What does Regression mean in Deep Learning?   In its essence, Regression in deep learning can be understood as the ability to…

Convolutional Neural Networks: Convolutional Neural Networks (CNNs) have revolutionized the field of deep learning, particularly in areas like image recognition and computer vision. Among the pioneering architectures, LeNet-5 holds a special place as the foundation upon which modern CNNs have been built. Proposed by Yann LeCun and his team in 1998, LeNet-5 marked a significant milestone in machine learning, demonstrating the potential of neural networks to classify handwritten digits. This article delves into the LeNet-5 architecture, exploring its components, significance, and role in shaping the development of advanced CNNs. The Genesis of LeNet-5 LeNet-5 was developed during a time when…

Deep Layers Revolutionize Image Recognition: The VGG architecture, introduced by the Visual Geometry Group (VGG) at the University of Oxford, has significantly influenced the field of image recognition. Renowned for its simplicity and efficiency, this convolutional neural network (CNN) architecture has set benchmarks in computer vision tasks. This article delves into the core aspects of VGG architecture, exploring its layers, contributions, and role in revolutionizing image recognition. Introduction to VGG Architecture The VGG architecture gained prominence with the VGGNet models, particularly VGG-16 and VGG-19, named after their respective layer depths. These networks were introduced in the ImageNet Large Scale Visual…

As we approach 2025, I’ve been reflecting on the programming habits I’ve built over the years. Some have propelled me forward, while others… well, let’s just say they’ve cost me hours I’ll never get back. This year, I decided to tackle these bad habits head-on with 10 challenges that pushed me out of my comfort zone. Why am I sharing this? Because I know I’m not alone. If you’re a developer looking to grow and work smarter in 2025, I invite you to join me in taking on these challenges. Challenge #1: Master the Debugger “Console.log” was my lifeline. Every…

Introduction Natural Language Processing (NLP) and Artificial Intelligence (AI) have fundamentally reshaped the way humans interact with machines. As we witness rapid technological advancements, these fields are increasingly converging, creating more seamless, accurate, and intuitive communication methods between humans and machines. This article explores how the merging of NLP and AI is enhancing human-machine communication, its applications, and the potential it holds for the future. Understanding NLP and AI: Core Concepts To appreciate the convergence of NLP and AI, it’s essential to understand each field individually. What is Natural Language Processing (NLP)? Natural Language Processing (NLP) is a branch of…

Introduction In recent years, deep learning has made tremendous strides in transforming various industries, and one of the most remarkable fields it has impacted is image processing. Image processing traditionally relied on techniques like filtering, thresholding, and segmentation. However, with the advent of deep learning, especially through neural networks, the process has become significantly more sophisticated, leading to groundbreaking results in areas such as computer vision, medical imaging, and even facial recognition technology. We’ll explore the key deep learning techniques that are revolutionizing image processing and delve into the transformative breakthroughs in this field. 1. Understanding Deep Learning in Image…