Full Stack Deep Learning: Free Course

About this course

Study the complete lifecycle of a deep learning product, from deciding whether machine learning is appropriate to deploying and operating the result. This free Full Stack Deep Learning course includes all nine lecture topics, the lab overview and eight lab videos from the completed 2022 teaching sequence.

Course at a glance

What you’ll learn

• Decide when a machine learning approach is suitable and connect the decision with a product requirement.
• Relate development infrastructure, experiment management, troubleshooting and testing to a deep learning project.
• Understand how data annotation, deployment and monitoring contribute to the operation of an ML-powered application.
• Consider continual learning, foundation models, team responsibilities and ethical concerns within a product workflow.

Skills you’ll gain

• Deep learning engineering
• Experiment management
• ML testing
• Data annotation
• Model deployment
• Product lifecycle

Course Overview

The lectures move through project vision, development infrastructure, troubleshooting, data management, deployment, continual learning, foundation models, team organisation and ethics. The labs connect those ideas with neural networks, convolutional models, transformers, experiment management, testing, annotation, web deployment and monitoring. Follow the paired lecture and lab sequence to connect a design decision with a working example. These are recorded course materials from 2022: named libraries, models and hosting interfaces may have changed, while the broader engineering questions remain valuable. The course concerns the full product lifecycle rather than only model training.

How to study the course

• Begin with the course vision and introductory labs. Use them to connect the intended application with the modelling and tooling choices.
• Work through each lecture and its associated lab where one is provided. Explain the problem a tool solves before reproducing the implementation.
• Complete the later foundation-model, team and ethics lectures. Use current documentation when adapting recorded code or services to a present-day environment.

Use the searchable classroom lesson list to revisit a specific explanation. Pause after a useful demonstration, reproduce a small example and explain the result in your own words. Keep a short note of what worked, what changed and what you still need to understand before moving on.

Who is this for?

This course is for developers and machine learning practitioners who have built or studied models and want to understand the surrounding engineering work. Familiarity with Python and basic deep learning makes the labs easier to follow.

Before you start

Python programming and introductory machine learning knowledge are recommended. A suitable development environment is needed for the labs. Compute, hosting and other services are separate from free viewing; no current university enrollment or credit is included.

Put the lessons into practice

Choose a small document or image-processing application and describe the user need before selecting a model. Plan the data, evaluation and deployment approach, then identify a test that could reveal an implementation fault. Record an experiment and outline what you would monitor once the application is used. Keep the exercise small and use the lab material as a reference, checking current dependencies before running it. Finish with a short review of limitations, responsibilities and the feedback needed to improve the product.

This optional activity is provided by WikiFree for independent practice. It is not a graded assignment from the instructor. Keep your working notes, check the result against the task you defined and return to the relevant lesson when an assumption or technique is unclear.

Certificate / Assessment

Watching the public videos and using WikiFree progress tracking does not award a certificate, exam voucher, academic credit or instructor assessment.

Use your practice output to review what you can now explain and repeat independently. Completion tracking records your study progress; it does not replace an assessed qualification or professional experience.

Frequently asked questions

Provider: Full Stack Deep Learning. Videos play from the The Full Stack channel on YouTube.