PyTorch Seminar 2025

Lecturing PyTorch training code structure and Neural Style Transfer

Overview 📝

This seminar was designed to develop practical deep learning coding skills using PyTorch. Participants learn progressively from Python and PyTorch fundamentals to production-level code structures, image classification/generation problems, and advanced applications including Neural Style Transfer.

Lecture Videos: PyTorch Seminar 2025 🎬

Course Structure 📚


Day 1: Training Code Structure 🏗️

  • Understanding overall deep learning code architecture (data loaders, models, loss functions, optimizers)
  • PyTorch class design and custom dataset/model implementation
  • Project structure examples and modularization approaches

Day 2: CIFAR-10 Implementation & Model Optimization 🖼️⚡

  • Image classification implementation using CIFAR-10 dataset
  • Data augmentation techniques, neural network architectures, and optimizer applications
  • Performance improvement strategies with practical code examples

Day 3: Training Best Practices 🏆

  • Practical tools for efficient training (tqdm, tensorboard, argparse)
  • Learning rate schedulers, checkpoint management, and loss tracking
  • Model initialization strategies and code management techniques

Day 4: Neural Style Transfer (NST) 🎨

  • Gatys et al. style transfer paper implementation using VGG-19 feature extraction
  • Content/Style Loss implementation and optimization methods
  • Hands-on style transfer application on real images

Orientation 🚀

  • Seminar objectives, machine learning framework overview, and learning goals
  • Comparison between PyTorch, TensorFlow, and JAX with fundamental concepts

Technical Focus Areas 🛠️

  • End-to-end PyTorch workflow: data processing, model design, training, and evaluation
  • Code structuring and modularization best practices
  • CNN and MLP architecture implementations
  • Data augmentation, learning rate scheduling, and checkpoint management
  • Advanced deep learning applications including Neural Style Transfer

Implementation & Assignments

  • Session-specific Jupyter notebooks and assignments provided
  • Tasks include custom dataset/model implementation, performance optimization, and style transfer results
  • All practical code submitted in Jupyter Notebook (.ipynb) format

Technical Stack

  • Framework: PyTorch
  • Languages: Python
  • Tools: Jupyter Notebook, TensorBoard, tqdm
  • Applications: Image Classification, Neural Style Transfer
  • Datasets: CIFAR-10

Video 🎥