This repository provides materials for the "Deep Learning for Us" (DL4US) practical development course, aimed at engineers seeking an introduction to deep learning. It utilizes Keras, a high-level API for TensorFlow, to cover a comprehensive curriculum from basic machine learning concepts to advanced topics like CNNs, RNNs, Seq2Seq models, GANs, VAEs, and reinforcement learning.

How It Works

The course is structured into 7 lessons, each containing Jupyter Notebooks with explanations and Python implementations. Each lesson is divided into four sections: topic overview, implementation, advanced techniques for accuracy improvement, and implementation of advanced techniques. The notebooks are designed to be executed sequentially, building knowledge from one lesson to the next.

Quick Start & Requirements

• Install dependencies: pip install -r requirements.txt
• Recommended environment: Google Colaboratory.
• Note: Some lessons may require installing additional packages within Colab. Refer to the "Google Colaboratory Execution Notes" for details on mounting Google Drive, handling large datasets (e.g., 13GB for Lesson 5), and potential instance resets.

Highlighted Details

• Covers a broad range of deep learning architectures and applications.
• Includes practical implementation examples for each topic.
• Structured learning path from fundamental concepts to advanced models.
• Provides guidance for using Google Colaboratory, including file handling and potential limitations.

Maintenance & Community

• Content updates stopped in 2019.
• Copyright belongs to the University of Tokyo (Matsuo Lab).
• For inquiries or permission for lectures/seminars, contact the University of Tokyo.

Licensing & Compatibility

• Licensed under CC-BY-NC-ND (Attribution-NonCommercial-NoDerivatives).
• Strictly for personal learning purposes.
• Commercial use, including lectures, workshops, or corporate training, is prohibited without explicit permission.

Limitations & Caveats

The content is from 2019 and may not reflect the latest views or be directly executable due to updates in Python and libraries. Commercial use is not permitted.