L14.6.2 Transfer Learning in PyTorch Code Example @SebastianRaschka

L14.6.2 Transfer Learning in PyTorch Code Example @SebastianRaschka

Uploaded April 2021 | Updated September 2026, 2 weeks ago

Sebastian's books: sebastianraschka.com/books

Slides: sebastianraschka.com/pdf/lecture-notes/stat453ss21/L14_cnn-architectures_slides.pdf

Link to code:
github.com/rasbt/stat453-deep-learning-ss21/blob/main/L14/5-transfer-learning-vgg16_small.ipynb

github.com/rasbt/stat453-deep-learning-ss21/blob/main/L14/5-transfer-learning-vgg16_large.ipynb

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This video is part of my Introduction of Deep Learning course.

Next video: youtu.be/q5YxK17tRm0

The complete playlist: youtube.com/playlist?list=PLTKMiZHVd_2KJtIXOW0zFhFfBaJJilH51

A handy overview page with links to the materials: sebastianraschka.com/blog/2021/dl-course.html

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L14.6.2 Transfer Learning in PyTorch Code Example

L3.5 The Geometric Intuition Behind the Perceptron

L2.5 Current Trends in Deep Learning

L2.2 Multilayer Networks

L10.5.2 Dropout Co-Adaptation Interpretation

L5.8 Adaline Code Example

L8.2 Logistic Regression Loss Function

L17.1 Variational Autoencoder Overview

L13.1 Common Applications of CNNs

L13.5 Cross-correlation vs. Convolution (Old)

L10.5.1 The Main Concept Behind Dropout

L18.3: Modifying the GAN Loss Function for Practical Use

Sebastian Raschka |

L14.6.2 Transfer Learning in PyTorch -- Code Example