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CS84: Deep Learning

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Prerequisites: Completion of CS01b or AP CS, or permission of instructor. Also requires Algebra II math experience. CS82 highly recommended but not required.

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 test classification, convolutional image recognition, q-learning, and more.

This course uses the Keras deep learning library.

Course Content

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  1. (Skipped this semester) Review of Python, Numpy, and Jupyter Notebook
  2. Using Linux
    • Recommended Reading from Cornell: lec02 lec03
    • Linux Command Line Tutorial, WinSCP, XMing
    • (Last semester) Homework: Write a program to conditionally average a column in a CSV in at least 5 different languages: Java, Python, Bash, R, and awk
    • (This semester) Homework: 2017-02-7 Read historical machine learning papers, pick favorites
  3. Using Keras and python
  4. Using Keras for Classification Part 2
  5. Convolutional Networks, MNIST
    • Homework Pt 1 (Start in class): Kaggle Contest
    • Homework Pt 2 Shape Count
    • (Alternative homework) Shape Points. Continue the in-class project "Shape Points". Modify the shapePoints project to draw two circles and several squares. Output an image representing the distance from the closest circle for each pixel (in manhattan distance, not euclidian). Build a neural network to predict the manhattan distance. Write a processing program that loads the test data and predictions data, and draws 4 pictures side by side. Picture 1 should be the input. Picture 2 is the correct output. Picture 3 is the predicted output. Picture 4 is the difference between pictures 2 and 3.
  6. Review Week
  7. Captcha Breaking (chars74k), Image Pre-Processing
  8. Word Vectorization and gensim, Text Classification, GloVe
  9. Text Parsing, IMDB Sentiment Analysis
  10. Paper and Project Review
  11. Cats versus Dogs
  12. Cats versus Dogs pt 2
  13. The Titantic Data Set
  14. Q Learning Part 1
  15. Q Learning Part 2 - Games
  16. Paper and Project Discussion (Favorite Topics)
  17. Paper and Project Discussion (Favorite Datasets)
  18. Independent Project: Data Munging
  19. Independent Project: Data Munging
Format: Web-conference

We Offer this Course in the below Durations

Meeting Location

Online Live Webconference

We will send you the webconference link once enrolled.








Tue Sept 05 - Tue Jan 23

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  • 7:25-8:25pm ET
  • Includes weekly homework & review content
  • Includes VM

$1260/session
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