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SalehAhmedShafin/Readme.md

salehahmedshafin

Hello 👋, I'm Saleh Ahmed Shafin (Nickname: Shafin)

I am from Dhaka, Bangladesh. Currently working as an AI Engineer at Codemen Solutions Limited, where I focus on LLMs, AI Agents, RAG-based document search, and workflow automation. I hold a B.Sc. in Computer Science and Engineering from Ahsanullah University of Science and Technology.

I am passionate about Artificial Intelligence, Machine Learning, and Deep Learning, with hands-on experience in developing intelligent systems, research projects, and real-world applications.
My research interests include Explainable AI (XAI), NLP, and Computer Vision, and I have published work on explainable lung disease classification using deep learning.

I enjoy participating in data science and machine learning competitions (Kaggle, BUET DL Sprint, Robi Datathon, DU DataVerse, Bengali.AI) and have achieved notable positions.

Core Skills: LLM, LangChain, PyTorch, TensorFlow, Keras, Scikit-learn, NLP, Computer Vision, FastAPI, Django, Docker, VectorDB, Neo4j, Firebase, and more.
Beyond coding, I take pride in problem-solving, adaptability, continuous learning, and effective communication.

Check out my repositories for project details!

  • 🌱 I’m currently learning Framework and Reinforcement Learning

  • 💬 Ask me about Machine Learning and Deep Learning

  • 📫 How to reach me salehahmedshafin7@gmail.com

Connect with me:

salehashafin   saleh-ahmed-shafin-0773b0193   22437643   positivesas   sa_shafin   sa_shafin  

Known Languages and Tools:

android   arduino   aws   c   cplusplus css3 dotnet firebase git html5 java javascript matlab mssql mysql nodejs opencv oracle pandas photoshop python pytorch scikit_learn seaborn sqlite   tensorflow

Language Stat:

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  1. Multimodal-Disaster-Event-Identification-from-Social-Media-Posts Multimodal-Disaster-Event-Identification-from-Social-Media-Posts Public

    We have proposed a multimodal approach. Where we first took the best unimodal for textual and visual data classification by testing and automation process. Then we fusion of the two models which ca…

    Jupyter Notebook 5

  2. XAI-and-Deep-Neural-Networks-for-Crop-Disease-Detection-and-Interpretability XAI-and-Deep-Neural-Networks-for-Crop-Disease-Detection-and-Interpretability Public

    Three different DNN models Xception, In- ceptionV3, and VGG19 were used for the classification of crop disease from the image dataset, and explainable AI XAI was used to evaluate their performance.…

    Jupyter Notebook 10

  3. Pytorch-Custom-CNN-model Pytorch-Custom-CNN-model Public

    The architecture is a deep neural network for image processing, composed of convolutional layers capturing features, followed by pooling and fully connected layers. It uses ReLU activation, dropout…

    Jupyter Notebook 1