Deep-Learning-Lecture-2020-2021-First-Term
Deep-Learning-Lecture-2020-2021-First-Term copied to clipboard
Skylab Deep Learning Lecture Notes | 2020-2021 First Term.
Introduction To Deep Learning Lecture
Repository of 2020-2021 first term Introduction to Deep Learning lecture. Contains lecture materials, notebook, datasets etc.
Intructors:
- Şafak Bilici
- Oğuzhan Ercan
Lecture Videos
Lectures are uploaded to this youtube playlist.
-
Videos: Turkish
-
All Materials: English
Notebook Links
Github does not render LaTeX well. So clone the repo and use notebooks in localhost, or use following links. (Even though following links, wait 10 or 15 seconds to LaTeX rendering).
Syllabus
Materials
Selected Materials To Study Before The Lecture
- Linear Algebra Review
- Probability Review
- Introductory Statistics
- The Matrix Calculus You Need For Deep Learning (Necessary),
- Probability Cheatsheet (Neccesary)
- Deep Learning Pt. 1
- Deep Learning Pt. 2
- Deep Learning Pt. 3
- An Intuitive Explanation of Convolutional Neural Networks
Good Materials To Learn Machine Learning/Deep Learning
- Introduction to Statistical Learning With Applications In R (Textbook)
- Deep Learning by Goodfellow & Bengio & Courville (Textbook)
- Ilker Bilbir's Lectures On Statistical Learning (Turkish) (Video Series)
- Andrew Ng's Lectures On Machine Learning (Video Series)
- Geoffrey Hinton's Lectures On Deep Learning (Video Series)
- Deniz Yuret's Lectures On Deep Learning (Video Series)
- MIT's Lectures On Deep Learning (Video Series)
- Intel's AI Course
- CS231n: Convolutional Neural Networks for Visual Recognition (Blog Post)
Materails For PyTorch
Can I Use This Lecture Notes For Other Purposes?
Everyone is permitted to copy and distribute verbatim or modified copies of this lecture notes.
References
Paper/textbook references are cited in references.bib file. Blog posts and others cited in references.txt file. Please warn, edit me if I missed any reference.