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MohammedSaqibMS/README.md

Hi, I'm Muhammad Saqib 👋

I'm a Deep Learning Specialist with a passion for developing cutting-edge AI models and solutions. My expertise lies in deep learning, computer vision, and generative AI. I have extensive experience in building and deploying machine learning models that solve real-world problems, and I am always excited to push the boundaries of AI technology.

🚀 About Me

  • 🎓 Educational Background: I hold a BS and MS in Software Engineering, with my MS thesis focusing on Deep Learning and Computer Vision.
  • 🧠 Specializations:
    • Deep Learning: Neural networks, autoencoders, and model optimization.
    • Computer Vision: Human action recognition, object detection, and suspicious activity recognition in RGB-D images.
    • Generative AI: Large Language Models (LLMs), including GPT, prompt engineering, and custom AI systems.
  • 💼 Professional Experience: I've worked on multiple freelance projects related to deep learning and computer vision, focusing on building custom AI models for Upwork clients.

🛠️ Skills & Tools

  • Programming: Python, C++, Java
  • Machine Learning: TensorFlow, PyTorch, scikit-learn
  • Cloud Platforms: AWS Sagemaker, GCP Vertex AI
  • Specializations: Generative AI, Deep Learning, Human Action Recognition, Computer Vision
  • Data Science: Data preprocessing, feature engineering, model evaluation

🌟 Featured Projects

  • Suspicious Activity Recognition in RGB-D Images: Developed a methodology for fusing deep features using autoencoders to detect suspicious activities from video footage.
  • Human Action Recognition: Proposed and implemented a deep learning-based system for identifying human actions in real-time.

Check out more of my projects here.

📫 How to reach me

I'm always open to collaborating on AI projects or discussing exciting opportunities in deep learning. Feel free to connect with me:

Popular repositories Loading

  1. Logistic-Regression-as-a-Neural-Network Logistic-Regression-as-a-Neural-Network Public

    Logistic Regression with Neural Network Principles: This repository implements logistic regression for classifying cat vs. non-cat images, incorporating neural network concepts like sigmoid activat…

    Jupyter Notebook

  2. Gradient-Checking Gradient-Checking Public

    Gradient Checking: Demonstrates 1D and ND gradient checking techniques to verify the accuracy of gradients in neural networks. Inspired by DeepLearning.AI's Deep Learning Specialization.

    Jupyter Notebook

  3. Initialization Initialization Public

    This repository explores the impact of various weight initialization methods on a neural network's performance, comparing zero, random, and He initialization. It includes visualizations of cost fun…

    Jupyter Notebook

  4. Regularization Regularization Public

    This repository implements a 3-layer neural network with L2 and Dropout regularization using Python and NumPy. It focuses on reducing overfitting and improving generalization. The project includes …

    Jupyter Notebook

  5. MohammedSaqibMS MohammedSaqibMS Public

  6. Planar_data_classification_with_onehidden_layer Planar_data_classification_with_onehidden_layer Public

    This repository implements a simple neural network for binary classification of 2D planar data using Python and NumPy. It compares logistic regression with neural networks and includes code for for…

    Jupyter Notebook