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Working on a deep learning project where I needed to deploy an image classification model on a mobile device. The challenge was clear: I needed something lightweight yet powerful enough to handle complex image data efficiently. After exploring several architectures, I came across MobileViT, a mobile-friendly Transformer-based model that combines the strengths of Convolutional Neural … Read more >>
Recently, I was working on a computer vision project where I had to classify hundreds of product images like electronics, clothing, and groceries. While convolutional neural networks (CNNs) are the usual choice, I wanted to explore something different, modern MLP (Multi-Layer Perceptron) models that have been making waves in deep learning research. In this Python … Read more >>
When I first started working with deep learning models in Python, I often faced challenges when a single label represented multiple instances, like a bag of medical images or document patches. That’s when I discovered Attention-Based Deep Multiple Instance Learning (MIL) in Keras, and it completely changed how I approached such problems. In this tutorial, … Read more >>
I was working on a Python project where I needed to classify thousands of images quickly and accurately. At first, I tried using a traditional Convolutional Neural Network (CNN), but the accuracy plateaued after a point. That’s when I decided to explore Vision Transformer (ViT), a model that adapts the Transformer architecture (originally used for … Read more >>
Recently, while mentoring a group of data science students, I was asked how to quickly build a Convolutional Neural Network (CNN) in Python Keras to classify handwritten digits using the MNIST dataset. I realized that although the MNIST dataset is one of the most common starting points for deep learning, many beginners still find it … Read more >>
I worked on a Python project where I needed to classify thousands of product images for an e-commerce client based in the USA. The challenge was to achieve high accuracy without spending days training a deep learning model from scratch. After trying several pre-trained models, I found that EfficientNet gave me the best balance between … Read more >>
Recently, I was working on a project where I had to store multiple pieces of data for each customer, their city, orders, and loyalty points, all in one place. The challenge was simple: how do I store multiple values for a single key in a Python dictionary? If you’ve ever tried to assign multiple values … Read more >>
Recently, I was working on a Python project where I had to merge multiple data sources into a single dictionary. The challenge was to extend an existing dictionary with new key-value pairs efficiently without losing any data. If you’ve ever worked with large datasets in Python, you know how important it is to manage dictionaries … Read more >>
While working on a data analytics project for a U.S.-based retail client, I needed to calculate the total daily sales from a list of transactions. Each value represented the sales amount for a day, stored neatly in a Python list. At first, I thought of using the built-in sum() function, but then I realized there … Read more >>
I was working on a data-cleaning project where I needed to filter a large Python dictionary based on specific conditions. The challenge was simple: I had a dictionary with hundreds of key-value pairs, but I only needed a subset that met certain criteria. If you’ve ever worked with Python dictionaries, you know how powerful and … Read more >>
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