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Achievements

Achievement: Arctic Code Vault Contributor

Achievements

Achievement: Arctic Code Vault Contributor

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khanabdulmajid/README.md

About Me

My Projects

Here are some of the projects I'm proud of:

1. Dog Breed Classifier using Convolutional Neural Network(CNN)

Project Overview: Developed a machine learning model to classify dog breeds from images with high accuracy. The project involved data preprocessing, model building, and deployment.
  • Collected and preprocessed a dataset of dog images from various sources.
  • Built and fine-tuned a convolutional neural network (CNN) using TensorFlow and Keras.
  • Implemented data augmentation techniques to enhance the model's performance.
  • Evaluated the model using accuracy, precision, recall, and F1-score metrics.
  • Deployed the model using Flask for creating a user-friendly web application interface.
Technologies and Tools: Python, TensorFlow, Keras, OpenCV, Flask.

2. Vehicle Detection Using OpenCV's Haar Casdcade

Project Overview: Developed a real-time car detection system using OpenCV and cascade classifiers. The project aimed to detect cars in images and video streams with high accuracy.

Key Responsibilities:

  • Gathered and labeled a dataset of car images for training the cascade classifier.
  • Created and trained the cascade classifier using OpenCV.
  • Implemented a real-time car detection system that processes video streams.
  • Optimized the detection algorithm to improve speed and accuracy.
  • Evaluated the system's performance using precision and recall metrics.

3. Generative Adversarial Networks for Handwritten Digit Synthesis

Project Overview: Designed and implemented a GAN architecture using Convolutional Neural Networks (CNNs) to synthesize realistic handwritten digit images based on the MNIST dataset.

Key Achievements:

  • Successfully trained the GAN to generate high-quality digit images, following best practices outlined in Soumith Chintala's research paper. The project demonstrated a robust understanding of GAN architecture and its application in image generation.

4. Deep Convolutional GANs for Colour Image Generation

Project Overview: Developed a Deep Convolutional GAN (DCGAN) architecture to generate realistic color photographs using the CIFAR-10 dataset.

Key Achievements:

  • Implemented advanced training techniques such as batch normalization and leaky ReLU activations, significantly enhancing the stability and output quality of the GAN. This project further honed skills in handling complex color data in GANs.

5. Image Classification for food: Built a deep learning model that can classify food images (101 diff. types).

You can explore more of my work on https://github.com/khanabdulmajid.

My Skills

  • Programming Languages: My Skills
  • Technologies: Technologies

Feel free to reach out if you have any questions or want to collaborate on a project. I'm always open to new opportunities and connections.

Pinned

  1. CNN CNN Public

    Jupyter Notebook

  2. Genrative-Adversarial-Networks-101 Genrative-Adversarial-Networks-101 Public

    Jupyter Notebook