AI For Youth: Grade 12

About Course

This course is the second part of the introduction to deep learning. Students study the architectures that make depth worthwhile, including convolution, sequences, transfer learning, attention and language models, and generative models, and finish with deploying these systems responsibly. Features: self-paced, about 1 hour per lesson, 8 lessons, 8 quizzes, 1 project, prerequisite: Grade 11.

Course Content

Unit 1: Foundations of Deep Learning
The machinery of depth: what it buys you over a shallow network, what convolution assumes about your data, how a deep stack is actually trained, and how these ideas extend to sequences. Lessons 1 through 4.

Lesson 1: Depth and Representation Learning
Quiz 1: Depth and Representation Learning
Lesson 2: Convolutional Networks
Quiz 2: Convolutional Networks
Lesson 3: Generalization and Regularization
Quiz 3: Generalization and Regularization
Lesson 4: Sequences and Recurrent Networks
Quiz 4: Sequences and Recurrent Networks

Unit 2: Modern Architectures
<p>Pretrained models and what they enable: transfer learning as the escape hatch from data hunger, attention and language models, generative systems, and the responsibilities that come with deploying any of it. Lessons 5 through 8.</p>

Lesson 5: Transfer Learning
Quiz 5: Transfer Learning
Lesson 6: Attention and Transformers
Quiz 6: Attention and Transformers
Lesson 7: Generative Models
Quiz 7: Generative Models
Lesson 8: Capstone — Deployment and Consequences
Quiz 8: Deployment and Consequences
Capstone Audit: One Deployed System

About Course

Course Content

Unit 1: Foundations of Deep Learning The machinery of depth: what it buys you over a shallow network, what convolution assumes about your data, how a deep stack is actually trained, and how these ideas extend to sequences. Lessons 1 through 4.

Lesson 1: Depth and Representation Learning

Quiz 1: Depth and Representation Learning

Lesson 2: Convolutional Networks

Quiz 2: Convolutional Networks

Lesson 3: Generalization and Regularization

Quiz 3: Generalization and Regularization

Lesson 4: Sequences and Recurrent Networks

Quiz 4: Sequences and Recurrent Networks

Unit 2: Modern Architectures <p>Pretrained models and what they enable: transfer learning as the escape hatch from data hunger, attention and language models, generative systems, and the responsibilities that come with deploying any of it. Lessons 5 through 8.</p>

Lesson 5: Transfer Learning

Quiz 5: Transfer Learning

Lesson 6: Attention and Transformers

Quiz 6: Attention and Transformers

Lesson 7: Generative Models

Quiz 7: Generative Models

Lesson 8: Capstone — Deployment and Consequences

Quiz 8: Deployment and Consequences

Capstone Audit: One Deployed System

Welcome to Sheaves AI

Unit 1: Foundations of Deep Learning
The machinery of depth: what it buys you over a shallow network, what convolution assumes about your data, how a deep stack is actually trained, and how these ideas extend to sequences. Lessons 1 through 4.

Unit 2: Modern Architectures
<p>Pretrained models and what they enable: transfer learning as the escape hatch from data hunger, attention and language models, generative systems, and the responsibilities that come with deploying any of it. Lessons 5 through 8.</p>