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Down the Rabbit hole
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Down the Rabbit hole

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NajdBinrabah/README.md

Hello, World! 👋🏻 I'm Najd

LinkedInGitHubKaggleWebsite


A Data Scientist chasing the AI Rabbit 🐇, armed with a B.Sc. in Management Information Systems earned while hopping between rabbit holes.

I’m more of a model destroyer than a builder, like a kid smashing sandcastles, always chasing the thrill of building one that finally stands unshaken.


Favourite Tools

ML / DL

Python Google Colab Jupyter TensorFlow PyTorch Keras Captum scikit-learn SHAP Surprise pandas NumPy SciPy FactorAnalyzer statsmodels Matplotlib seaborn Plotly librosa NLTK Pillow NetworkX PySpark Kafka

Other

SQL Power BI Alteryx GitHub Kaggle Markdown Google Workspace Microsoft Office Suite macOS Windows


Header


While you’re here, why not let John M. Coetzee take you to a "peculiar headspace"?


“And for whom, anyway, do we do the things that lead to Nobel Prizes if not for our mothers?“
– J. M. Coetzee


Banquet.Speech.by.J.M.Coetzee.mp4

J. M. Coetzee's speech at the Nobel Banquet, 10 December 2003.
Sveriges Television AB (production)
Copyright © Sveriges Television AB 2003


If you’re wondering, this speech always leaves me caught up in a moment like:

Headspace


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  1. Deep-Learning-with-PyTorch-and-Captum Deep-Learning-with-PyTorch-and-Captum Public

    This project classifies smoking images using VGG19 with data augmentation, and Captum for model explainability, identifying key features per prediction.

    Jupyter Notebook

  2. Deep-Learning-with-TensorFlow-and-Keras Deep-Learning-with-TensorFlow-and-Keras Public

    This project explores emotion recognition in audio data, focusing on feature extraction techniques while also comparing the performance of LSTM and 1D CNN models.

    Jupyter Notebook

  3. Music-Recommendation-System Music-Recommendation-System Public

    This project explores diverse "Recommendation Techniques", each offering a distinct approach to predicting user preferences.

    Jupyter Notebook 1