This repository contains examples of various AI techniques and models implemented using Python. Each example provides a practical demonstration of a specific concept, helping you understand and apply AI algorithms in your own projects.
The Perceptron example showcases a simple implementation of the Perceptron algorithm for multi class classification. It demonstrates how to train a Perceptron model on a synthetic dataset and visualize the decision boundary.
The Neural Network example demonstrates the implementation of a basic feedforward neural network using a popular deep learning library. It covers the process of building the network architecture, training the model on a dataset, and evaluating its performance.
The VGG-16 Inference and Feature Maps example focuses on utilizing a pre-trained VGG-16 model for image classification and extracting feature maps. It provides code to perform inference on images, obtain predictions, and visualize the intermediate feature maps generated by the model.
The LSTM example illustrates the implementation of a Long Short-Term Memory (LSTM) neural network for time series forecasting. It includes steps for loading and preprocessing time series data, building and training the LSTM model, and evaluating its performance.
The Hugging Face Models example demonstrates how to leverage the Hugging Face Transformers library to utilize pre-trained language models for different tasks. It covers loading a pre-trained model, performing tasks such as question answering and sentiment analysis, and accessing the model's predictions.
To run these examples on your local machine, follow the instructions below:
- Clone the repository:
git clone https://github.com/AMasetti/AI-Examples.git- Install the necessary dependencies. You can use the following command to install the required packages using pip:
pip install -r requirements.txt- Navigate to the specific example directory and run the corresponding Notebook file:
cd 1. Perceptron
python perceptron_example.pyFeel free to explore the code, modify the examples, and adapt them to suit your needs. Each example includes detailed comments to help you understand the implementation and experiment with different configurations.
Contributions to this repository are welcome! If you have any AI examples or improvements to the existing ones, feel free to submit a pull request. Please ensure that your code adheres to the repository's coding guidelines.
This repository is licensed under the MIT License. You are free to use, modify, and distribute the code for personal or commercial purposes.