This repository provides a comprehensive set of resources for deep learning workshops, targeting participants who need a pre-configured environment for hands-on learning. It offers a VirtualBox appliance with Jupyter, TensorFlow, PyTorch, and pre-trained models, enabling users to explore applications like style transfer, image classification, and natural language processing without complex setup.

How It Works

The core of the project is a VirtualBox appliance that bundles a Fedora 25 installation with a Python 3.x virtual environment. This environment includes Jupyter for interactive notebooks, TensorFlow, TensorBoard, and PyTorch (CPU version). The appliance is designed to be easily shared and run, providing immediate access to pre-trained models (e.g., GoogLeNet, GloVe) and datasets (e.g., MNIST, ImageNet) for various deep learning applications.

Quick Start & Requirements

• Install/Run: Download and import the VirtualBox appliance. Scripts in ./local/ can set up the environment and run Jupyter, TensorBoard, and SSH to the host machine.
• Prerequisites: VirtualBox.
• Resources: The VM includes datasets and pre-trained models, implying a significant disk footprint.
• Links: Slides and blog posts for each workshop are linked throughout the README.

Highlighted Details

• Supports multiple deep learning frameworks: Theano/Lasagne, TensorFlow/Keras, and PyTorch (CPU).
• Includes notebooks for diverse applications: style transfer, image classification, anomaly detection, reinforcement learning, and NLP tasks like Named Entity Recognition.
• Features demonstrations of gradient descent, CNN filters, and OpenAI's "Reptile" meta-learning algorithm.
• Provides guidance on making Jupyter notebooks Git-friendly using nbstripout to manage output.

Maintenance & Community

The repository appears to be a collection of materials from various workshops hosted by the author at different events (FOSSASIA, PyCon-SG, DataScienceSG, etc.) between 2016 and 2018. There are no explicit links to community channels or recent maintenance updates.

Licensing & Compatibility

The README does not explicitly state a license. The inclusion of various frameworks and tools suggests potential licensing considerations for commercial use depending on the specific licenses of the bundled software.

Limitations & Caveats

The project materials are from 2016-2018, meaning the included software versions (Theano, TensorFlow, PyTorch) are likely outdated and may not be compatible with current hardware or best practices. The Fedora 25 base OS is also end-of-life.