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STAT 157, Spring 19
Table Of Contents
Syllabus
Assignments
Projects
1. Ensuring Quality Conversations in Online Forums
2. Image attribute classification using disentangled embeddings on multimodal data
3. Deep Learning with NLP (Tacotron)
4. Image captioning
5. Explainable Electrocardiogram Classifications using Neural Networks
7. Deep fitting room
8. Bot controlled accounts
9. Predicting Next Day Stock Returns After Earnings Reports Using Deep Learning in Sentiment Analysis
10. Predict the presence of oil palm plantation in satellite imagery
11. Audio Recovery
12. Visual Speech recognition
14. Examination of Associative Vocabulary in Internet Language through Word Embedding
15. What Are You Thinking: Image Generation By Deconvolution Network
16. Predicting Length of Stay at Hospitals
17. Handwriting to Latex
18. ACID: Analyzing Census and Imaging Data
19. Parameterizable Single GAN Multi-Style
20. Measuring Amazon Product Reviews Helpfulness
Units
Introduction to Deep Learning
Introduction to Deep Learning
Installation
Linear Algebra
Using Jupyter Notebook
Using AWS to Run Code
Probability and Statistics
Probability and Statistics
Naive Bayes
Gradients, Chain Rule, Automatic Differentiation
Autograd
Linear Regression, Basic Optimization
Likelihood, Loss Functions, Logisitic Regression, Information Theory
Softmax Regression
Fashion MNIST
Softmax Regression from scratch
Softmax Regression - concise version
Multilayer Perceptron
Multilayer Perceptron
Multilayer Perceptron Implementation
Multilayer Perceptron in Gluon
Model Selection, Weight Decay, Dropout
Model Selection
Weight Decay
Dropout
Numerical Stability, Hardware
Numerical Stability and Initialization
Predicting House Prices on Kaggle
GPU Purchase Guide
Machine Learning Problems and Statistical Environment
Statistical Environment
Layers, Parameters, GPUs
Blocks and Layers
Parameter Management
Deferred Initialization
Custom Layers
File I/O
GPUs
Convolutional Networks
Convolutional Neural Networks
Convolutions
Padding and Strides
Channels
Pooling
Basic Convolutional Networks
LeNet
AlexNet
VGG
NiN
Residual Networks and Advanced Architectures
Network with Parallel Concatenations
Residual Networks
Densely Connected Networks
Computation Performance, Multi-GPU and Multi-Machine Training
Hybridization
Asynchrony
Automatic Parallelization
Multiple GPUs
Multiple GPUs in Gluon
Image Augmentation, Fine Turning, Neural Style
Image Augmentation
Fine Tuning
Neural Style
Object Detection
Object Detection and Bounding Boxes
Anchor Boxes
Multiscale Object Detection
Object Detection Data Set (Pikachu)
Single Shot Multibox Detection (SSD)
Region-based CNNs (R-CNNs)
Sequence Models and Language
Sequence Models
Language Models
Recurrent Neural Networks
Text Preprocessing
Recurrent Neural Networks
Building an RNN
Building an RNN in Gluon
Backpropagation through Time
Advanced Sequence Models
Deep Recurrent Neural Networks
Bidirectional RNNs
Beam Search
Word2vec, FastText, GloVe, Sentiment Analysis
Encoder-Decoder, Seq2seq, Machine Translation
Attention, Transformer, BERT
Convex Optimization, Convergence Rate
Momentum, AdaGrad, RMSProp, Adam
FAQ
STAT 157, Spring 19
Table Of Contents
Syllabus
Assignments
Projects
1. Ensuring Quality Conversations in Online Forums
2. Image attribute classification using disentangled embeddings on multimodal data
3. Deep Learning with NLP (Tacotron)
4. Image captioning
5. Explainable Electrocardiogram Classifications using Neural Networks
7. Deep fitting room
8. Bot controlled accounts
9. Predicting Next Day Stock Returns After Earnings Reports Using Deep Learning in Sentiment Analysis
10. Predict the presence of oil palm plantation in satellite imagery
11. Audio Recovery
12. Visual Speech recognition
14. Examination of Associative Vocabulary in Internet Language through Word Embedding
15. What Are You Thinking: Image Generation By Deconvolution Network
16. Predicting Length of Stay at Hospitals
17. Handwriting to Latex
18. ACID: Analyzing Census and Imaging Data
19. Parameterizable Single GAN Multi-Style
20. Measuring Amazon Product Reviews Helpfulness
Units
Introduction to Deep Learning
Introduction to Deep Learning
Installation
Linear Algebra
Using Jupyter Notebook
Using AWS to Run Code
Probability and Statistics
Probability and Statistics
Naive Bayes
Gradients, Chain Rule, Automatic Differentiation
Autograd
Linear Regression, Basic Optimization
Likelihood, Loss Functions, Logisitic Regression, Information Theory
Softmax Regression
Fashion MNIST
Softmax Regression from scratch
Softmax Regression - concise version
Multilayer Perceptron
Multilayer Perceptron
Multilayer Perceptron Implementation
Multilayer Perceptron in Gluon
Model Selection, Weight Decay, Dropout
Model Selection
Weight Decay
Dropout
Numerical Stability, Hardware
Numerical Stability and Initialization
Predicting House Prices on Kaggle
GPU Purchase Guide
Machine Learning Problems and Statistical Environment
Statistical Environment
Layers, Parameters, GPUs
Blocks and Layers
Parameter Management
Deferred Initialization
Custom Layers
File I/O
GPUs
Convolutional Networks
Convolutional Neural Networks
Convolutions
Padding and Strides
Channels
Pooling
Basic Convolutional Networks
LeNet
AlexNet
VGG
NiN
Residual Networks and Advanced Architectures
Network with Parallel Concatenations
Residual Networks
Densely Connected Networks
Computation Performance, Multi-GPU and Multi-Machine Training
Hybridization
Asynchrony
Automatic Parallelization
Multiple GPUs
Multiple GPUs in Gluon
Image Augmentation, Fine Turning, Neural Style
Image Augmentation
Fine Tuning
Neural Style
Object Detection
Object Detection and Bounding Boxes
Anchor Boxes
Multiscale Object Detection
Object Detection Data Set (Pikachu)
Single Shot Multibox Detection (SSD)
Region-based CNNs (R-CNNs)
Sequence Models and Language
Sequence Models
Language Models
Recurrent Neural Networks
Text Preprocessing
Recurrent Neural Networks
Building an RNN
Building an RNN in Gluon
Backpropagation through Time
Advanced Sequence Models
Deep Recurrent Neural Networks
Bidirectional RNNs
Beam Search
Word2vec, FastText, GloVe, Sentiment Analysis
Encoder-Decoder, Seq2seq, Machine Translation
Attention, Transformer, BERT
Convex Optimization, Convergence Rate
Momentum, AdaGrad, RMSProp, Adam
FAQ
7. Deep fitting room
¶
Team: Xinchen Xu, Jiazhong Mei, Liyang Zhao, Ziyang Zhou, Ziqi Pei
Midterm Presentation
¶
Slides
Table Of Contents
7. Deep fitting room
Midterm Presentation
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