Introduction to Deep Learning
Code: AI41F
Type
Fundamental
For Grades
9-12
Target Competitions
This course aims to inspire in machine learning, provide project ideas, prepare students for the STEM project competitions both in theory and in practice. The level of the projects in the course is prestigious national / international competitions such as:
Description
This course introduces neural networks and modern ML workflows. Students learn:
- neural network building blocks and training intuition
- optimization and training stability
- deep learning patterns used in real AI systems
- how architecture choices affect performance
Who should take this course?
This course requires approval after registration. The minimum requirements for this course is as follows:
- AI: Introduction to Machine Learning
- Math: Algebra II
Competition background is not required but is a plus: USACO Gold, AIME or above
Content
The course is composed of 16 lessons:- Lesson 1&2: Neural Networks 101
- Lesson 3&4: Feed Forward Neural Networks
- Lesson 5&6: Training and Backpropagation
- Lesson 7&8: Keras and Real-World Models
- Lesson 9&10: Model Evaluation
- Lesson 11&12: Intro to CNNs
- Lesson 13&14: NLP and Word Embeddings
- Lesson 15&16: Final Project
