CS84 - Deep Learning
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.
- This course gives students access to professional research grade hardware, including compute servers such as a 16 core server with 128GB RAM, terabytes of fast storage, and research grade graphics processors such as the Titan X Pascal GPU
- This course also teaches students how to use linux tools and the CUDA + GPU accelerated python research environment, using Tensorflow-GPU 1.4, Keras 2.1.4 - tools compiled with lubcudnn 6 and 7
- Course material draws from recent academic research published in the last 2-5 years, including deep networks, single shot detection, convolutional or vectorizated models for language, as well as (time permitting) demo projects featuring AlphaZero Go and GAN inspired sequence to sequence learning.
This course no longer uses Theano, and students will model primarily with the Keras deep learning library backed by Tensorflow. Research from KTBYTE students and alumni
Completion of [CORE 5b] or AP CS, or permission of instructor. Also requires Algebra II math experience. [AI 1] highly recommended but not required.
These are examples of projects that students create as they grow their skills in [AI 2]
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 allows us to copy effective parts of existing models. Today we will also introduce the midterm project.
Midterm Project: Image Classification
Start Unit 2: More Image Processing
Exact topics TBD depending on student goals
We're planning our next semester. Please check back in May for Summer classes.
In the meantime, click here to submit your time request.
Spring Semester: Once Per Week
** Instructors currently scheduled are not guaranteed and could change at KTBYTE's discretion
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