mirror of
https://github.com/The-Art-of-Hacking/h4cker.git
synced 2024-10-01 01:25:43 -04:00
65 lines
3.9 KiB
Markdown
65 lines
3.9 KiB
Markdown
# AI Security Research Resources
|
|
|
|
## Langchain Resources
|
|
- [Tools, Learning, Notebooks, Bots, Agent examples, etc.](https://github.com/The-Art-of-Hacking/h4cker/blob/master/ai_research/LangChain/README.md)
|
|
|
|
|
|
## AI Security Best Practices and Tools
|
|
- [High-Level AI Security Best Practices](<AI Security Best Practices/AI-security-tools-and-frameworks.md>)
|
|
- [Homomorphic-Encryption](<AI Security Best Practices/homomorphic-encryption.md>)
|
|
- [AI Security Tools and Frameworks](<AI Security Best Practices/AI-security-tools-and-frameworks.md>)
|
|
- [AI Secure Deployment Tips](<AI Security Best Practices/secure-deployment.md>)
|
|
- [AI Secure Design Tips](<AI Security Best Practices/secure-design.md>)
|
|
- [Threat Modeling](<AI Security Best Practices/threat-modeling.md>)
|
|
|
|
## AI Security Resources from Omar's Training Sessions
|
|
- [Cybersecurity Learning Prompts](https://github.com/santosomar/chatgpt-cybersecurity-prompts)
|
|
- [Networking Prompts](https://github.com/santosomar/chatgpt-networking-prompts)
|
|
- [Programming Learning Prompts](https://github.com/santosomar/chatgpt-programming-prompts)
|
|
|
|
## AI Ethics and Privacy Resources
|
|
- [AI Ethics and Privacy Resources](https://github.com/The-Art-of-Hacking/h4cker/tree/master/ai_research/ethics_privacy)
|
|
|
|
## Tools & Methods for Data Collection, Cleaning, and Analysis:
|
|
|
|
- **Data Collection**:
|
|
- APIs and SDKs
|
|
- Wireless transmission
|
|
|
|
### Data Cleaning:
|
|
|
|
3. **Pandas**:
|
|
- **Example**: Cleaning a dataset with missing values using Pandas before training a machine learning model.
|
|
- **Relevant Link**: [Pandas Documentation](https://pandas.pydata.org/pandas-docs/stable/index.html)
|
|
- **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.
|
|
|
|
4. **Sci-kit learn**:
|
|
- **Example**: Using Sci-kit learn for feature selection and removing irrelevant features from a dataset.
|
|
- **Relevant Link**: [Sci-kit learn Documentation](https://scikit-learn.org/stable/)
|
|
- **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:
|
|
|
|
5. **TensorFlow**:
|
|
- **Example**: Developing a deep learning model using TensorFlow to classify different activities based on sensor data.
|
|
- **Relevant Link**: [TensorFlow Documentation](https://www.tensorflow.org/learn)
|
|
- **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.
|
|
|
|
6. **Keras**:
|
|
- **Example**: Using Keras to create a convolutional neural network (CNN) for image recognition, an essential task in AI.
|
|
- **Relevant Link**: [Keras Documentation](https://keras.io/getting_started/intro_to_keras_for_engineers/)
|
|
- **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:
|
|
|
|
7. **Matplotlib**:
|
|
- **Example**: Using Matplotlib to visualize the distribution of different activities within a dataset.
|
|
- **Relevant Link**: [Matplotlib Documentation](https://matplotlib.org/stable/contents.html)
|
|
- **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.
|
|
|
|
8. **Seaborn**:
|
|
- **Example**: Creating a heatmap using Seaborn to visualize the correlation between different features in a dataset.
|
|
- **Relevant Link**: [Seaborn Documentation](https://seaborn.pydata.org/)
|
|
- **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.
|
|
|