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AI Security Research Resources
Langchain Resources
AI Security Best Practices and Tools
- High-Level AI Security Best Practices
- Homomorphic-Encryption
- AI Security Tools and Frameworks
- AI Secure Deployment Tips
- AI Secure Design Tips
- Threat Modeling
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:
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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.
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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:
-
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.
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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:
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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.
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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.