Look inside
In this liveProject, you’ll use the popular deep learning framework PyTorch to train a supervised learning model on a dataset of melanoma images. Your final product will be a basic image classifier that can spot the difference between cancerous and non-cancerous moles. You’ll create a custom dataset class and data loaders that can handle image preprocessing and data augmentation, and even improve the accuracy of your model with transfer learning.
This project is designed for learning purposes and is not a complete, production-ready application or solution.
book resources
When you start your liveProject, you get full access to the following books for 90 days.
project author
Olga Petrova
Olga Petrova is a machine learning engineer at Scaleway, a French cloud provider, where her focus lies on deep learning R&D. Previously, she has worked as a researcher in theoretical physics, looking into the applications of artificial intelligence to quantum systems. Olga has a Ph.D. from Johns Hopkins University, and a B.S. from Worcester Polytechnic Institute. She enjoys blogging about the latest advancements in AI.
prerequisites
This liveProject is for intermediate Python programmers with some machine learning experience. To begin this liveProject, you will need to be familiar with:
TOOLS
- Intermediate Python
- Basics of PIL
- Basics of Matplotlib
- Basics of NumPy
- Beginner PyTorch
TECHNIQUES
Classification as a machine learning task
Basics of model training, validation and testing
Monitoring training and spotting overfitting/underfitting
Basics of neural networks
you will learn
In this liveProject, you will learn important deep learning tools and techniques that are highly transferable to a wide range of machine learning roles, especially in the field of computer vision.
- Pytorch for deep learning on the GPU
- Setting up an image preprocessing pipeline to feed data to a PyTorch model
- Data augmentation built into the image preprocessing pipeline
- Training a supervised learning classifier on labeled data
- Image classification using deep convolutional neural networks
- Testing a supervised learning model
- Transfer learning for improving model accuracy
features
- Self-paced
- You choose the schedule and decide how much time to invest as you build your project.
- Project roadmap
- Each project is divided into several achievable steps.
- Get Help
- While within the liveProject platform, get help from other participants and our expert mentors.
- Compare with others
- For each step, compare your deliverable to the solutions by the author and other participants.
- book resources
- Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.