TensorFlow (8 op)

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.