Tutorial_04_Basics_of_Vision_CNN

Vision CNN Tutorial #4

This tutorial provides an introduction to the basics of Vision Convolutional Neural Networks (CNNs). It includes two separate implementations using TensorFlow and PyTorch to cover the foundational aspects of CNNs in computer vision tasks.

Contents

1. TensorFlow Implementation (tutorial_04_tf_basics_of_vision_cnn.ipynb)

   • Introduces the basics of CNNs using TensorFlow.
   • Covers model creation, training, and evaluation for image classification tasks.

2. PyTorch Implementation (tutorial_04_torch_basics_of_vision_cnn.ipynb)

   • Provides an equivalent introduction to CNNs using PyTorch.
   • Demonstrates the creation of CNN models, training loops, and evaluation strategies.

How to Use

1. Open the notebooks in Jupyter or any compatible environment.
2. Follow the step-by-step instructions provided in the markdown cells.
3. Modify and experiment with the code to deepen your understanding of Vision CNNs.

Requirements

• Python 3.x
• TensorFlow (for the TensorFlow notebook)
• PyTorch (for the PyTorch notebook)
• Jupyter Notebook or Jupyter Lab

Feel free to explore the implementation in both frameworks and compare their approaches to building and training Vision CNN models.