rasbt/stat479deeplearningss19
Course material for STAT 479: Deep Learning (SS 2019) at University WisconsinMadison
repo name  rasbt/stat479deeplearningss19 
repo link  https://github.com/rasbt/stat479deeplearningss19 
homepage  http://pages.stat.wisc.edu/~sraschka/teaching/stat479ss2019/ 
language  Jupyter Notebook 
size (curr.)  168326 kB 
stars (curr.)  349 
created  20190119 
license  
STAT479: Deep Learning (Spring 2019)
Instructor: Sebastian Raschka
Lecture material for the STAT 479 Deep Learning course at University WisconsinMadison. For details, please see the course website at http://pages.stat.wisc.edu/~sraschka/teaching/stat479ss2019/
Course Calendar
Please see http://pages.stat.wisc.edu/~sraschka/teaching/stat479ss2019/#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: singlelayer neural nets)
 Modeling and deriving nonconvex 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

L01: What are Machine Learning and Deep Learning? An Overview. [Slides]

L02: A Brief Summary of the History of Neural Networks and Deep Learning. [Slides]

L04: Linear Algebra for Deep Learning. [Slides]

L06: Automatic Differentiation with PyTorch. [Slides] [Code]

L07: Cloud Computing. [Slides]

L08: Logistic Regression and Multiclass Classification [Slides] [Code]

L11: Normalization and Weight Initialization [Slides]

L12: Learning Rates and Optimization Algorithms [Slides]

L13: Introduction to Convolutional Neural Networks [Slides (part 1)] [Slides (part 2)] [Slides (part 3)]

L14: Introduction to Recurrent Neural Networks [Slides (part 1) Slides (part 2)] [Code]

L16: Variational Autoencoders (skipped due to timing constraints)

A summary/gallery of some of the awesome student projects students in this class worked on.
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:

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

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

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

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

Ping Yu, Ke Chen, Runfeng Yong (NLP on Amazon Fine Food Reviews) average score: 7.525
Most Creative Project:

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

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

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

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

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

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

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

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

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

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