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:

• Minimal Installation (Recommended for Colab):

  pip install dldna[colab]
  

  This option installs only the packages that are not typically pre-installed in Google Colab.

• Standard Installation (All Dependencies):

  pip install dldna
  

  install_requires

• Full Installation (Including Optional and Development Dependencies):

  pip install dldna[all]
  

  This installs all dependencies, including manim (for visualization) and development tools.

Requirements:

The specific requirements depend on the installation option you choose. See setup.py for the complete list of dependencies. The minimal installation for Colab requires:

• Python >= 3.7
• transformers
• datasets
• tqdm
• pillow
• opencv-python
• sentencepiece

Usage:

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

# ... your code here ...

print(dldna.__version__)  # Check the installed version