Transfer Learning: Choose What Learns
Freeze a real ResNet, choose the fine-tuning depth and see every trainable parameter. Turn your choices into PyTorch code.
Explore the labPRACTICAL, PROJECT-BASED LEARNING
Computer Vision
with PyTorch.
Build and improve image classifiers with hands-on projects, convolutional networks, transfer learning, and selective fine-tuning.
Lessons, current pricing, and available previews are on Udemy.
WHAT YOU’LL BE ABLE TO DO
Build a model.
Understand what it learned.
Prepare image datasets and create a clear training workflow.
Build and train a convolutional neural network in PyTorch.
Evaluate predictions with a confusion matrix and inspect errors.
Apply transfer learning with a pretrained ResNet-18.
Fine-tune selected layers and compare model behavior.
Save checkpoints and predict on your own images.
THE LEARNING PATH
From first steps
to a complete workflow.
A high-level guide to the course. The complete lecture list is available on Udemy.
01
Get your first classifier working
Set up your environment, get familiar with notebooks, and connect images, labels, and predictions.
02
See how neural networks learn
Connect the intuition behind learning to manual calculations and then to PyTorch.
03
Build and evaluate a baseline CNN
Train a convolutional model, examine its confusion matrix, and identify what needs improvement.
04
Transfer learning and selective fine-tuning
Adapt a pretrained ResNet-18, choose which layers to train, and compare meaningful experiments.
05
Bring it together: Rock–Paper–Scissors
Establish a baseline, improve it, compare your models, and explain the mistakes that remain.
LEARNING SUPPORT
A little less guessing.
A lot more practice.
Work with downloadable Jupyter notebooks, practice tasks, hints, and completed solutions. Setup guidance and a notebook introduction help you get ready to follow along.
The focus is on reasoning: what changed, why it might help, and what the results actually support. You’ll leave with a workflow you can adapt to your own image-classification tasks.
Try the free model evaluation checklist →EXPLORE BEFORE YOU CODE
Make the ideas
visible.
Free interactive companions for the course: explore the architecture, choose what learns, vary the data and investigate generalization.
ResNet: Inside the Residual Block
Open the stages of ResNet-18, 34 and 50. Follow tensor shapes, compare shortcuts and make the residual addition visible.
Explore the labData Augmentation: Build the Variations
Transform an image, generate a repeatable batch and inspect each crop, rotation and color adjustment. Decide what should stay invariant.
Explore the labRegularization: Learn Beyond the Sample
Train paired neural networks in your browser. Compare measured learning curves, validation errors and the predictions that remain.
Explore the labBEFORE YOU BEGIN
A few useful details.
Who is this course for?
Python learners who want a practical introduction to computer vision and PyTorch. You should be comfortable with variables, loops, and functions.
Do I need to know deep learning already?
No. The course introduces the ideas and connects them to working examples. Basic Python is the starting point.
What will I build?
Image classifiers using a baseline CNN, transfer learning, and selective fine-tuning, culminating in a Rock–Paper–Scissors project.
Where do I enroll and access the materials?
The course, its downloadable materials, available previews, and current purchase options are hosted on Udemy.
Are the free resources the complete course materials?
The free resources on this site are standalone guides and educational examples. The full guided lessons and course notebooks are available with the course on Udemy.
YOUR NEXT PROJECT STARTS HERE
Turn an idea into a working classifier.
Learn with Ackley Will, Associate Professor of Computing at Andrews University.