dldna 0.1.1

Deep Learning DNA: Surviving Architectures and Profound Principles

Deep Learning DNA: Surviving Architectures and Profound Principles

A journey into the core technologies and key architectures of deep learning.

This package, dldna, provides code and resources accompanying the book "Deep Learning DNA: Surviving Architectures and Profound Principles". It delves into the essential techniques and pivotal architectures that have shaped the field of deep learning, offering a practical, hands-on complement to the book's conceptual explanations.

Key Features:

• Code Examples: Explore implementations of fundamental deep learning concepts and architectures.
• Focus on Efficiency: Learn how to build and train models with an emphasis on computational efficiency.
• Accompanying Material: This package is designed to be used alongside the book "Deep Learning DNA", providing interactive code to enhance your learning experience.

Installation:

pip install dldna

Optional Dependencies

pip install dldna[visualization] # Install with manim
pip install dldna[dev]  # Install with development tools

Requirements:

• Python >= 3.7
• NumPy >= 1.22
• Pandas >= 1.5
• Matplotlib >= 3.5
• scikit-learn >= 1.0
• SciPy >= 1.8
• Transformers >= 4.30
• Datasets >= 2.10
• tqdm >= 4.60
• Pillow >= 9.0
• OpenCV-Python >= 4.5
• TensorBoard >= 2.8
• PyTorch >= 1.13
• torchvision >= 0.14
• torchaudio >= 0.13
• (Optional) Manim (for visualization)

Usage:

# Example usage (replace with a simple, illustrative example)
import dldna

# ... your code here ...

print(dldna.__version__)  # Check the installed version

License:

This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License - see the LICENSE file for details.

For more information, including the full book content and detailed explanations, please visit the GitHub repository:

https://github.com/Quantum-Intelligence-Frontier/dldna

Contact: For inquiries, please contact: [e-mail]