Back to all courses

Deep learning with Python

Individual course

Max amount of FITech students: 1000

This course is intended as a follow-up for CS-EJ3211 Machine Learning with Python.

This course is an introduction to deep learning.

Course contents

This is an introductory course where you will learn how to train high-dimensional non-linear models, represented by deep artificial neural networks (ANN), using few lines of Python code.

Deep learning is an umbrella term for methods using deep nets, i.e., ANNs that consist of several consecutive layers of artificial neurons. The course gives you a brief overview of gradient descent which is the most widely used algorithm for tuning the weights of deep nets. You will learn some powerful tricks that allow tuning billions of ANN weights using only hundreds of training examples. Some of the most successful deep learning methods are enabled by few clever regularisation techniques, such as data augmentation and transfer learning, to avoid overfitting.

Learning outcomes

After successfully completing the course, the student

  • understands how ANNs can be used for learning and evaluating high-dimensional non-linear models.
  • understands the basic principle of gradient descent.
  • is able to build and train ANNs using the Python package Keras.
  • is able to diagnose the learning process by comparing training with validation loss.
  • is able to use data augmentation to synthetically enlarge the training set.
  • is able to implement transfer learning by fine-tuning a pre-trained deep net.

Course material

Background reading:

1. F. Chollet, 2017. “Deep Learning with Python.” New York, NY: Manning
2. A. Géron, 2019. “Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow : Concepts, Tools, and Techniques to Build Intelligent Systems.“ Second edition, O’Reilly Media.

Books 2-4 can be accessed via Aalto University library service.

Completion methods

The grading is based on Python coding assignments.

More information in the Aalto University study guide.

You can get a digital badge after completing this course.

syväoppiminen keinotekoiset neuroverkot neuroverkko gradientti

Responsible teachers

Aalto University
Alex Jung, Assistant professor
Aalto University
Shamsiiat Abdurakhmanova

Further information about the studies

Aalto University
Tiina Porthén

Contact person for applications

FITech Network University
Fanny Qvickström, Student services specialist
Application period has ended
Application period has ended
AI and machine learning,
Course code:
Study credits:
0 €
Course level:
Teaching period:
Application start date:
Application deadline:
Application period has ended
Host university:
Aalto University
Who can apply:
Adult learner,
Degree student
Teaching method:
Teaching language:
General prerequisites:
High-school math (functions, derivatives, vectors). Basic Python programming (variables, functions, loops). The course Machine Learning with Python.
Interested in this course? Subscribe and get updates about the course directly to your email. You can cancel subscription any time you want.

This course is included in the following theme