humphd/have-fun-with-machine-learning
Have Fun With Machine Learning
This project is a neural network image classifier and a set of tools for building and training convolutional neural networks to recognize and categorize images. It serves as a machine learning educational guide, providing a practical resource for learning neural network fundamentals through an onboarding process.
The system includes a dedicated workflow for pretrained model fine-tuning, allowing existing network weights to be adapted to new image categories. This is supported by a transfer learning pipeline that replaces final classification layers and adjusts weights through targeted retraining.
The project covers the end-to-end machine learning lifecycle, including image dataset preparation for training and validation sets, epoch-based iterative training, and weight-based model persistence. It also provides capabilities for neural network deployment via local execution or an API interface.
Features
- Convolutional Neural Networks - Implements a convolutional neural network architecture to automatically extract spatial features for image classification.
- Convolutional Neural Network Training - Provides the capability to build and train convolutional neural networks over multiple epochs to recognize image categories.
- Feature Extraction - Processes raw pixels through sequential neural transformations to identify visual patterns and reduce dimensionality.
- Image Classification - Trains models to map labeled image datasets to specific category probabilities using supervised learning.
- Classification Training - Implements workflows for building and training convolutional neural networks to recognize custom image categories.
- Image Classifiers - Provides a complete system for building and training convolutional neural networks to categorize images.
- Transfer Learning Pipelines - Includes a pipeline to adapt pretrained networks to new tasks by replacing classification layers and fine-tuning weights.
- Iterative Training - Optimizes model accuracy through epoch-based iterative training on the image dataset.
- Vision Dataset Preparation - Provides tools for organizing and normalizing raw images into categorized training and validation sets.
- Vision Model Fine-Tuning - Adapts pretrained vision models to recognize new categories by adjusting final layers and training on specific data.
- Machine Learning Implementations - Serves as a practical guide for learning neural network fundamentals and image classification through a beginner-friendly process.
- Preparation Tools - Ships a set of processes for organizing and normalizing raw images into valid training and validation sets.
- Weight Persistence - Saves and loads trained network states as binary weight files to enable model reuse without retraining.
- Neural Network Deployment - Provides runtimes for executing trained neural networks to predict image categories via local code or APIs.
- Pretrained Weight Initializers - Supports uploading model definitions and binary weight files to initiate classification tasks without initial learning.
- Image Classification APIs - Provides a RESTful API endpoint for predicting image category probabilities using a trained network.
- Local Execution - Enables image category prediction by loading trained networks and weights into a local execution environment.
- AI & Machine Learning Education - Provides an educational resource for learning image classification and neural network fundamentals through practical examples.
- Machine Learning Courses - Beginner-friendly resources for learning machine learning concepts.
Star history
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