TensorFlow (8 op)
Toteutuksen tunnus: TT00EO90-3003
Toteutuksen perustiedot
- Ajoitus
- 01.01.2022 - 31.12.2022
- Toteutus on päättynyt.
- Opintopistemäärä
- 8 op
- Virtuaaliosuus
- 8 op
- Toteutustapa
- Etäopetus
- Toimipiste
- Karaportti 2
- Opetuskielet
- englanti
- Paikat
- 0 - 5000
- Koulutus
- Tieto- ja viestintätekniikan tutkinto-ohjelma
- Opettajat
- Virve Prami
- Opintojakso
- TT00EO90
Aika ja paikka
Course is online in TechClass environment and it can be done in own pace.
Oppimateriaalit
Online.
Harjoittelu- ja työelämäyhteistyö
N/A
Tenttien ajankohdat ja uusintamahdollisuudet
Online.
Kansainvälisyys
N/A
Toteutuksen valinnaiset suoritustavat
N/A
Arviointimenetelmät ja arvioinnin perusteet
Exercises 30%
Quizzes 20%
Project 40%
Essay 10%
Opiskelijan ajankäyttö ja kuormitus
Lectures = 80h
Assignments = 50h
Self-study = 80h
Quiz = 10h
Project = 40h
Essay = 10h
Total = 270 hours
Sisällön jaksotus
Up to Student her-/himself.
Opetusmenetelmät
This course is 100% virtual thanks to the comprehensive interactive material and content prepared for this course.
Course includes:
- Tutorial Videos
- Exercises
- Quiz
- Project
- Self-study
Arviointiasteikko
Hyväksytty/Hylätty
Arviointikriteerit, tyydyttävä (1)
- The student is familiar with TensorFlow’s features for machine/deep learning applications.
- The student knows about the first and the second generations of TensorFlow.
- The student knows how to set up and get started with TensorFlow in the Google Colab environment.
- The student is familiar with the basic syntax of Python and knows how to write simple scripts.
- The student is familiar with the general framework of Keras.
- The student is familiar with machine learning models and their basic concepts.
Arviointikriteerit, hyvä (3)
- The student knows how to train simple machine learning models, evaluate them, and make predictions based on them in TensorFlow.
- The student knows how to implement simple neural networks in TensorFlow.
- The student knows the concept of tensors and how they are different from variables.
- The student is familiar with the intuition behind callbacks.
- The student is familiar with the general framework of convolutional neural networks (CNN).
- The student is familiar with different layers of CNN.
- The student is familiar with the concepts of overfitting and regularization.
- The student knows how to implement CNN in TensorFlow for computer vision tasks.
Arviointikriteerit, kiitettävä (5)
- The student knows how to analyze the performance of CNN after training.
- The student is familiar with L1 and L2 regularizations and can employ them to avoid overfitting.
- The student understands the concept of early stopping to avoid overfitting.
- The student understands dropout and batch normalization techniques to avoid overfitting.
- The student knows how to transfer learning models work.
- The student is familiar with TensorFlow Hub.