Assumed prior knowledge
Elementary calculus and linear algebra; basics of probability theory and statistics; basics of machine learning. Fluency in Python.
The course provides an introduction to key concepts, architectures, and algorithms for Deep Learning and its applications. It covers the following topics:
Part One: Multilayer Peceptron and Backpropagation
From a Single Layer Perceptron to Deep Learning: a historical perspective
Algorithms for training MLPs: Stochastic Gradient Descent and its variants; Backpropagation
Alternative activation and loss functions; Initialization, Regularization, Dropout, Batch Normalization
Introduction to GPU-computing, Keras, TensorFlow; Hyperparameter Tuning
Part Two: Deep Learning for computer vision and language processing
Convolutional Networks: key architectures and applications; Transfer Learning
Recurrent Networks: from Backpropagation Through Time to Attention Mechanism and Transformers
Part Three: Generative Networks
Generative Adversarial Networks (GAN's)
During the course several state-of-the-art applications of Deep Learning to image recognition, computer vision, language modeling, game playing programs, etc., will be discussed. The course consists of weekly lectures, three programming assignments (in Python, TensorFlow) and the final written exam.
The objectives of this course are:
to provide a general introduction to the field of deep neural networks and their applications
to develop practical skills for designing and training neural networks for tasks like: image classification, forecasting, language processing, game playing
to master a popular system for developing deep networks: TensorFlow
The most recent timetable can be found at the Computer Science (MSc) student website.
You will find the timetables for all courses and degree programmes of Leiden University in the tool MyTimetable (login). Any teaching activities that you have sucessfully registered for in MyStudyMap will automatically be displayed in MyTimeTable. Any timetables that you add manually, will be saved and automatically displayed the next time you sign in.
MyTimetable allows you to integrate your timetable with your calendar apps such as Outlook, Google Calendar, Apple Calendar and other calendar apps on your smartphone. Any timetable changes will be automatically synced with your calendar. If you wish, you can also receive an email notification of the change. You can turn notifications on in ‘Settings’ (after login).
For more information, watch the video or go the the 'help-page' in MyTimetable. Please note: Joint Degree students Leiden/Delft have to merge their two different timetables into one. This video explains how to do this.
Mode of instruction
Practical Assignments (by teams of 2 or 3 students)
Total hours of study 6 EC course: 168 hrs.
The final grade will be the weighted average of grades for:
programming assignments (3*20%=60%)
To pass the course, grades for both components should be at least 5.5.
The teacher will inform the students how the inspection of and follow-up discussion of the exams will take place.
- Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (Third Edition; check on https://www.oreilly.com/library/view/hands-on-machine-learning/9781098125967/)
From the academic year 2023-2024 on every student has to register for courses with the new enrollment tool MyStudyMap. There are two registration periods per year: registration for the fall semester opens in July and registration for the spring semester opens in December. Please see this page for more information.
Please note that it is compulsory to both preregister and confirm your participation for every exam and retake. Not being registered for a course means that you are not allowed to participate in the final exam of the course. Confirming your exam participation is possible until ten days before the exam.
Extensive FAQ's on MyStudymap can be found here.