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README.md

AI Security Research Resources

Langchain Resources

LLM Frameworks

Collection of Jupyter Notebooks

This is (by far) the best collection of colab notebooks I have found. A bit overwhelming and I wish I had time to go through most of them… Tons of links to papers, videos, etc.: https://github.com/amrzv/awesome-colab-notebooks

AI Security Best Practices and Tools

AI Security Resources from Omar's Training Sessions

AI Ethics and Privacy Resources

Tools & Methods for Data Collection, Cleaning, and Analysis:

  • Data Collection:
    • APIs and SDKs
    • Wireless transmission

Data Cleaning:

  1. Pandas:

    • Example: Cleaning a dataset with missing values using Pandas before training a machine learning model.
    • Relevant Link: Pandas Documentation
    • Usage in HAR and AI: Pandas can be used to structure and clean sensor data, making it suitable for training AI models capable of recognizing complex patterns in human activity data.

  2. Sci-kit learn:

    • Example: Using Sci-kit learn for feature selection and removing irrelevant features from a dataset.
    • Relevant Link: Sci-kit learn Documentation
    • Usage in HAR and AI: Sci-kit learn offers various tools for data preprocessing, which is a vital step in preparing data for AI algorithms, enhancing the performance of the models in HAR applications.

Data Analysis:

  1. TensorFlow:

    • Example: Developing a deep learning model using TensorFlow to classify different activities based on sensor data.
    • Relevant Link: TensorFlow Documentation
    • Usage in HAR and AI: TensorFlow provides a comprehensive platform for developing and training AI models capable of analyzing and recognizing patterns in human activity data.

  2. Keras:

    • Example: Using Keras to create a convolutional neural network (CNN) for image recognition, an essential task in AI.
    • Relevant Link: Keras Documentation
    • Usage in HAR and AI: Keras simplifies the process of building and optimizing neural networks, a crucial component in AI, to analyze human activity data more effectively and make predictions.

Visualization and Further Analysis:

  1. Matplotlib:

    • Example: Using Matplotlib to visualize the distribution of different activities within a dataset.
    • Relevant Link: Matplotlib Documentation
    • Usage in HAR and AI: Visualization of data is essential in AI to understand underlying patterns and trends in data, aiding in the better development and tuning of models for HAR.

  2. Seaborn:

    • Example: Creating a heatmap using Seaborn to visualize the correlation between different features in a dataset.
    • Relevant Link: Seaborn Documentation
    • Usage in HAR and AI: Seaborn can enhance data visualization in AI, assisting in identifying relationships and patterns in data which can influence the development and performance of HAR models.