Class Projects
Students will build, test, and publish their own game in KTBlocks
CODING PLATFORM
The KTCoder all-in-one coding platform supports our interactive online classes, our specialized curriculum, and our students’ passion for learning.
STUDENT HELP HOURS
Help hours are led by our highly qualified teaching assistants. It is an easy and free way to get immediate feedback on your code.
PROGRESS REPORTS
KTBYTE will e-mail parents with behavior and grade progess reports.
COMPLETION CERTIFICATES
Students can request a certificate of completion once they finish each course.
Class Description:
Learn the most modern techniques for supervised learning, used in common applications such as facial recognition, speech recognition, and self driving cars. This course will also provide students with a linux server with GPU acceleration to run their algorithms. Topics include regression, test classification, convolutional image recognition, and more.
Features:
- This course teaches students how to use linux tools and the CUDA + GPU accelerated python research environment, using Tensorflow-GPU and Keras
- Course material draws from recent academic research published in the last 2-5 years, including artificial neural networks, image classification models, vectorizated models for language, as well as demo projects on image and text generation (GAN and RNN).
When students move from [AI 1] to [AI 2], their focus shifts to getting the best possible model accuracies on real world data. Half the homework involves using pre-formatted data sets, while the second half involves students finding their own data sets. Students taking [AI 2] apply neural networks to text, images, and other data. Because this is a research-focused course, much of [AI 2] involves the practice of parsing data and preparing it for use on research servers. This includes the use of linux command line tools, as well gaining a familiarity with different formats for data such as comma separated files, JSON, and all sorts of image file types. Although the models are written in python, the class overall is language agnostic, as students learn to use different tools to most effectively deal with different data types. Indeed, students are expected to become proficient in the entire research lifecycle, from coming up to a hypothesis to explaining their model results.
Research Projects from KTBYTE students
Prerequisites
Completion of [CORE 5b] or AP CS, or permission of instructor. Requires Algebra II math experience.
[AI 1] highly recommended but not required.
Class Description:
Learn the most modern techniques for supervised learning, used in common applications such as facial recognition, speech recognition, and self driving cars. This course will also provide students with a linux server with GPU acceleration to run their algorithms. Topics include regression, test classification, convolutional image recognition, and more.
Features:
- This course teaches students how to use linux tools and the CUDA + GPU accelerated python research environment, using Tensorflow-GPU and Keras
- Course material draws from recent academic research published in the last 2-5 years, including artificial neural networks, image classification models, vectorizated models for language, as well as demo projects on image and text generation (GAN and RNN).
When students move from [AI 1] to [AI 2], their focus shifts to getting the best possible model accuracies on real world data. Half the homework involves using pre-formatted data sets, while the second half involves students finding their own data sets. Students taking [AI 2] apply neural networks to text, images, and other data. Because this is a research-focused course, much of [AI 2] involves the practice of parsing data and preparing it for use on research servers. This includes the use of linux command line tools, as well gaining a familiarity with different formats for data such as comma separated files, JSON, and all sorts of image file types. Although the models are written in python, the class overall is language agnostic, as students learn to use different tools to most effectively deal with different data types. Indeed, students are expected to become proficient in the entire research lifecycle, from coming up to a hypothesis to explaining their model results.
Prerequisites:
Completion of [CORE 5b] or AP CS, or permission of instructor. Requires Algebra II math experience.
[AI 1] highly recommended but not required.
Related Classes
Syllabus:
Introduction to Neural Networks
In this class we'll learn about the Perceptron as the building block for neural networks and deep learning
Introduction to TensorFlow
In this class we'll start exploring how to use the TensorFlow library.
More TensorFlow, Intro to Keras
In this class we'll keep working with TensorFlow and start learning to use Keras.
Working with Images
In this lesson we'll start learning how to process images with our ML architectures, including running a model for image ID or generation.
Convolutional Neural Networks (CNN)
Today we'll start exploring a new type of neural network and learn about regularizations
Transfer Learning
Transfer learning allows us to copy effective parts of existing models. Today we will also introduce the midterm project.
Midterm Project: Image Classification
Project Day
Finish Midterm Project, presentations
Students will present their work from the midterm project. Class discussion on topics to cover in Unit 2 in order to meet student goals.
Recurrent Neural Networks (RNNs) - Part 1
RNNs can be used to make predictions about time series data, like words in a sentence. This lesson focuses on the basics of building these models and how to avoid common issues.
Recurrent Neural Networks (RNNs) - Part 2
In this lesson we'll wrap up our discussion of RNNs and gated layers (GRU and LSTM). Then we'll cover a longer demo of text generation with RNNs.
Image Generation
In this lesson we'll delve into the theory of how deep learning can be used to generate images, and cover the basics of models like GANs and stable diffusion.
Research Project Brainstorming
In today's class we'll review topics covered in the course. Students will be provided with time to start brainstorming a research question for their final project.
Research Projects
Continue working on research projects.
Research Project Presentations
Come to class prepared to present your research project.
Syllabus
Introduction to Neural Networks
In this class we'll learn about the Perceptron as the building block for neural networks and deep learning
Introduction to TensorFlow
In this class we'll start exploring how to use the TensorFlow library.
More TensorFlow, Intro to Keras
In this class we'll keep working with TensorFlow and start learning to use Keras.
Working with Images
In this lesson we'll start learning how to process images with our ML architectures, including running a model for image ID or generation.
Convolutional Neural Networks (CNN)
Today we'll start exploring a new type of neural network and learn about regularizations
Transfer Learning
Transfer learning allows us to copy effective parts of existing models. Today we will also introduce the midterm project.
Midterm Project: Image Classification
Project Day
Finish Midterm Project, presentations
Students will present their work from the midterm project. Class discussion on topics to cover in Unit 2 in order to meet student goals.
Recurrent Neural Networks (RNNs) - Part 1
RNNs can be used to make predictions about time series data, like words in a sentence. This lesson focuses on the basics of building these models and how to avoid common issues.
Recurrent Neural Networks (RNNs) - Part 2
In this lesson we'll wrap up our discussion of RNNs and gated layers (GRU and LSTM). Then we'll cover a longer demo of text generation with RNNs.
Image Generation
In this lesson we'll delve into the theory of how deep learning can be used to generate images, and cover the basics of models like GANs and stable diffusion.
Research Project Brainstorming
In today's class we'll review topics covered in the course. Students will be provided with time to start brainstorming a research question for their final project.
Research Projects
Continue working on research projects.
Research Project Presentations
Come to class prepared to present your research project.