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Computer Science • Series

Intro to AI: Hands-On Exploration with Neural Networks, GANs, CNNs, and More

Next session on Nov 24, 2024

Aashrita K

Series Details

Sessions

Public Discussion

Series Details

About

This course is beginner friendly

Week 1: Intro to Neural Networks
Week 2: Training Neural Networks
Week 3: Intro to GANs
Week 4: Creating with GANs
Week 5: Intro to CNNs
Week 6: CNN Applications
Week 7: Intro to RNNs & Transformers
Week 8: Applications of RNNs & Transformers
Week 9: Final Project

✋ ATTENDANCE POLICY

You will be withdrawn from the series if you have more than 2 unexcused absences in a row. Please message the tutor in advance for any absences!

Dates

November 10 - December 22

Learners

2 / 50

Total Sessions

7

About the Tutor

High Schooler in California

View Aashrita K's Profile

Upcoming Sessions

5
24
Nov

Session 3

Artificial Intelligence

Week 3: Intro to GANs
Learn GAN basics (generators and discriminators)
Activity: Experiment with a pre-built GAN model.

1
Dec

Session 4

Artificial Intelligence

Week 4: Creating with GANs
Explore GAN applications and ethics
Activity: Generate custom images; discuss ethical considerations.

8
Dec

Session 5

Artificial Intelligence

Week 5: Intro to CNNs
Understand CNN layers and feature extraction
Activity: Build a simple CNN for image recognition.
15
Dec

Session 6

Artificial Intelligence

Week 6: CNN Applications
Explore CNN applications in real-world tasks
Activity: Classify images; discuss use cases.
22
Dec

Session 7

Artificial Intelligence

Week 8: Applications of RNNs & Transformers
Apply to language tasks (translation, sentiment analysis)
Activity: Experiment with a pre-trained Transformer model.

Past Sessions

2
10
Nov

Session 1

Artificial Intelligence

Week 1: Intro to Neural Networks
Overview of basic concepts (perceptrons, layers)
Activity: Build a simple neural network.
17
Nov

Session 2

Artificial Intelligence

Week 2: Training Neural Networks
Focus on training basics (backpropagation, gradient descent)
Activity: Adjust model parameters to observe learning.

Public Discussion

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