Deze opleiding
| locatieAmsterdam |
| diplomacertificate |
| typesummer course, 3 EC |
| taalvolledig English |
| opleidingsduur5 dagen voltijd |
The lectures will introduce to students the fundamental building blocks of deep learning methods and the weaknesses and strengths of the different architectures. Students will learn how to tailor a model for a particular application. During tutorials students practice the theory using exercises and have the opportunity to ask for additional explanation for those parts of the material perceived as more difficult. Computer lab sessions aim at making the material come alive and train students in how the methods learnt in class can actually be applied to data. The lab sessions are meant to work on the assignments, such that the students automatically keep up with the material.
Waarom aan de Erasmus University Rotterdam?
Deep Learning components (gradient descent models, loss functions, avoiding over-fitting, introducing asymmetry)
Feed forward neural networks
Convolutional neural networks
Embeddings (pre-trained embeddings, examples of pre-trained models, e.g., Word2Vec)
Generative Adversarial Network (GAN)
Advanced architectures (Densely connected networks, Adaptive structural learning)
Onderwijs
| taal van onderwijs | 100% en |
|---|---|
| avondonderwijs | n.v.t. |
| afstandsonderwijs | n.v.t. |
onderwijsvormen
computer exercise, lecture, tutorialinstruction modes
The lectures will introduce to students the fundamental building blocks of deep learning methods and the weaknesses and strengths of the different architectures. Students will learn how to tailor a model for a particular application. During tutorials students practice the theory using exercises and have the opportunity to ask for additional explanation for those parts of the material perceived as more difficult. Computer lab sessions aim at making the material come alive and train students in hoobjectives
The lectures will introduce to students the fundamental building blocks of deep learning methods and the weaknesses and strengths of the different architectures. Students will learn how to tailor a model for a particular application.study load
Participants who joined at least 80% of all sessions and hand in the assignment will receive a certificate of participation stating that the summer school is equivalent to a work load of 3 ECTS. Note that it is the student's own responsibility to get these credits registered at their university.Toelating en kosten
toelatingseisen
vooropleiding
The summer course welcomes Master's and PhD students, alumni, professionals in economics and related fields, who are interested in deep learning. The level is introductory, targeted at participants who would like to familiarize themselves with the topic, and acquire a good basis from which to approach deep learning potential applications.
basiskennis
Students are expected to have a solid background in calculus, linear algebra, and classical statistics. Familiarity with open source languages such as R or Python is a must.
inbegrepen in collegegeld
| certificate |
| registration fee |
| lunch |
| study material |
Na de studie
