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

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).

• 01-Machine-Learning

• 02-Models-Performance

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)
• 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)