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 >>
Working on a Python project where I needed to classify human emotions from facial expressions. The goal was simple, build a deep learning model that could recognize emotions like happiness, sadness, anger, and surprise from images. The challenge, however, was figuring out the best way to do this efficiently using Python. After some trial and … Read more >>
When I first started exploring deep learning projects in Python, one of the most fascinating ideas I came across was traffic sign recognition. It felt rewarding to see a model correctly identify road signs, something that’s crucial for modern driver-assistance systems and autonomous vehicles. In this tutorial, I’ll walk you through how I built a … Read more >>
Working on a project where I needed to classify thousands of product images automatically. The challenge? Building a reliable and accurate image classification model using Python and Keras. I’ve been using Keras for over four years now, and it remains one of my favorite deep learning frameworks. It’s simple, powerful, and integrates beautifully with TensorFlow. … Read more >>
I was working on a deep learning project where I needed to build a neural network model in Python using TensorFlow Keras. But when I tried to import the library, I ran into the dreaded error, “ModuleNotFoundError: No module named ‘tensorflow.keras’.” If you’ve ever faced this issue, don’t worry. I’ve been developing in Python for … Read more >>
When I first started working with deep learning in Python, one of the biggest challenges I faced was figuring out how to properly save and reload my Keras models. After spending hours training a neural network, the last thing you want is to lose your progress or have to retrain your model from scratch. Over … Read more >>
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