/stat479-deep-learning-ss19

Course material for STAT 479: Deep Learning (SS 2019) at University Wisconsin-Madison

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STAT479: Deep Learning (Spring 2019)

Instructor: Sebastian Raschka

Lecture material for the STAT 479 Deep Learning course at University Wisconsin-Madison. For details, please see the course website at http://pages.stat.wisc.edu/~sraschka/teaching/stat479-ss2019/

Course Calendar

Please see http://pages.stat.wisc.edu/~sraschka/teaching/stat479-ss2019/#calendar.

Topic Outline

  • History of neural networks and what makes deep learning different from “classic machine learning”
  • Introduction to the concept of neural networks by connecting it to familiar concepts such as logistic regression and multinomial logistic regression (which can be seen as special cases: single-layer neural nets)
  • Modeling and deriving non-convex loss function through computation graphs
  • Introduction to automatic differentiation and PyTorch for efficient data manipulation using GPUs
  • Convolutional neural networks for image analysis
  • 1D convolutions for sequence analysis
  • Sequence analysis with recurrent neural networks
  • Generative models to sample from input distributions
    • Autoencoders
    • Variational autoencoders
    • Generative Adversarial Networks

Material



Project Presentation Awards

Without exception, we had amazing project presentations this semester. Nonetheles, we have some winners the top 5 project presentations for each of the 3 categories, as determined by voting among the ~65 students:

Best Oral Presentation:

  1. Saisharan Chimbiki, Grant Dakovich, Nick Vander Heyden (Creating Tweets inspired by Deepak Chopra), average score: 8.417

  2. Josh Duchniak, Drew Huang, Jordan Vonderwell (Predicting Blog Authors’ Age and Gender), average score: 7.663

  3. Sam Berglin, Jiahui Jiang, Zheming Lian (CNNs for 3D Image Classification), average score: 7.595

  4. Christina Gregis, Wengie Wang, Yezhou Li (Music Genre Classification Based on Lyrics), average score: 7.588

  5. Ping Yu, Ke Chen, Runfeng Yong (NLP on Amazon Fine Food Reviews) average score: 7.525

Most Creative Project:

  1. Saisharan Chimbiki, Grant Dakovich, Nick Vander Heyden (Creating Tweets inspired by Deepak Chopra), average score: 8.313

  2. Yien Xu, Boyang Wei, Jiongyi Cao (Judging a Book by its Cover: A Modern Approach), average score: 7.952

  3. Xueqian Zhang, Yuhan Meng, Yuchen Zeng (Handwritten Math Symbol Recognization), average score: 7.919

  4. Jinhyung Ahn, Jiawen Chen, Lu Li (Diagnosing Plant Diseases from Images for Improving Agricultural Food Production), average score: 7.917

  5. Poet Larsen, Reng Chiz Der, Noah Haselow (Convolutional Neural Networks for Audio Recognition), average score: 7.854

Best Visualizations:

  1. Ping Yu, Ke Chen, Runfeng Yong (NLP on Amazon Fine Food Reviews), average score: 8.189

  2. Xueqian Zhang, Yuhan Meng, Yuchen Zeng (Handwritten Math Symbol Recognization), average score: 8.153

  3. Saisharan Chimbiki, Grant Dakovich, Nick Vander Heyden (Creating Tweets inspired by Deepak Chopra), average score: 7.677

  4. Poet Larsen, Reng Chiz Der, Noah Haselow (Convolutional Neural Networks for Audio Recognition), average score: 7.656

  5. Yien Xu, Boyang Wei, Jiongyi Cao (Judging a Book by its Cover: A Modern Approach), average score: 7.490