deep-learning-seminaari
Contents
These are the lecture notes in Finnish for the TUT PLA-79100 course. In the course we went through the book Deep Learning (2016) by Goodfellow et al. and these are the presentable notes created. Initially they were ment for personal use only, so any enhancement proposals are welcome through pull requests.
Note: The notes are viewable only in a desktop browser due to Github’s Jupyter Notebook rendering settings.
Updated Note: The pages are now rendered from ipynb files and should be viewable with mobile devices as well.
Why in Finnish and not in English?
The simple answer: To help native Finnish speakers grasp the contents better. There’s a plethora of books and online courses already about the subjects. What is missing however is an all-around repository of machine learning talk in Finnish. And its a shame, as native Finns regularly resort to speaking fluent Finglish when discussing the topics of machine learning. This is why.
Lecture Notes
- 01-04 - From Linear Algebra to Numerical Computation
- 05 - Machine Learning Basics
- 06 - Deep Feedforward Networks
- 07 - Regularization for Deep Learning
- 08 - Optimization for Training Deep Models
- 09 - Convolutional Networks
- 10 - Sequence Modeling with Recurrent and Recursive Nets
- 11 - Practical Methodology
- 12 - Applications
- 13 - Linear Factor Models
- 14 - Autoencoders
- 15 - Representation Learning
- 16 - Structured Probabilistic Models for Deep Learning
- 17 - Monte Carlo Methods
- 18 - Confronting the Partition Function
- 19 - Approximate Inference
- 20 - Deep Generative Models
To use and edit these files a Python installation with Jupyter package is required, while the contents are viewable within this Github repo. The notebooks for each chapter can be found under the folder notebooks.
All diagrams have been made with draw.io.