From 5f93524e90dfbe36743350403ddeaf7250a9424a Mon Sep 17 00:00:00 2001 From: Omar Santos Date: Mon, 23 Oct 2023 22:16:33 -0400 Subject: [PATCH] Update README.md --- ai_research/README.md | 52 ++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 51 insertions(+), 1 deletion(-) diff --git a/ai_research/README.md b/ai_research/README.md index aff7395..2686ad7 100644 --- a/ai_research/README.md +++ b/ai_research/README.md @@ -1,4 +1,8 @@ -# AI Security Best Practices and Resources +# 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]() @@ -12,3 +16,49 @@ - [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. +