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STAT 157, Spring 19
Table Of Contents
Syllabus
Calendar
Assignments
Project
FAQ
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
Sampling
Gradients, Chain Rule, Automatic Differentiation
Autograd
Linear Regression, Basic Optimization
Linear Regression
Linear Regression Implementation from Scratch
Concise Implementation of Linear Regression
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
Capacity Control and Overfitting
Model Selection
Weight Decay
Dropout
Dropout and Initialization
Dropout
Initialization
Computational Graphs
Machine Learning Problems and Statistical Environment
Statistical Environment
Blocks and Layers
Blocks and Layers
Parameters
Deferred Initialization
GPUs
Convolutional Networks
Convolutional Neural Networks
Convolutions
Padding and Strides
Channels
Pooling
Basic Convolutional Networks
LeNet
AlexNet
Network Structures
Network in Network
Network with Parallel Concatenations
Residual Networks and Advanced Architectures
Residual Networks
Densely Connected Networks
Making a Computer Vision Model work
Image Augmentation
Fine Tuning
100 Dogs
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
Sequence Models with Memory and Gating
Truncated Backpropagation
Gated Recurrent Unit
Long Short Term Memory
Advanced Sequence Models
Deep Recurrent Neural Networks
Bidirectional RNNs
Beam Search
Optimization Basics
Introduction to Optimization
Stochastic Gradient Descent for Deep Learning
Stochastic Gradient Descent
Batching
Momentum
Advanced Optimization Algorithms
Adagrad
Adam
Parallel Processing
Asynchrony
Automatic Parallelization
Multiple GPUs
Multiple GPUs in Gluon
Peak Performance on ImageNet
Gluon CV Toolkit
STAT 157, Spring 19
Table Of Contents
Syllabus
Calendar
Assignments
Project
FAQ
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
Sampling
Gradients, Chain Rule, Automatic Differentiation
Autograd
Linear Regression, Basic Optimization
Linear Regression
Linear Regression Implementation from Scratch
Concise Implementation of Linear Regression
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
Capacity Control and Overfitting
Model Selection
Weight Decay
Dropout
Dropout and Initialization
Dropout
Initialization
Computational Graphs
Machine Learning Problems and Statistical Environment
Statistical Environment
Blocks and Layers
Blocks and Layers
Parameters
Deferred Initialization
GPUs
Convolutional Networks
Convolutional Neural Networks
Convolutions
Padding and Strides
Channels
Pooling
Basic Convolutional Networks
LeNet
AlexNet
Network Structures
Network in Network
Network with Parallel Concatenations
Residual Networks and Advanced Architectures
Residual Networks
Densely Connected Networks
Making a Computer Vision Model work
Image Augmentation
Fine Tuning
100 Dogs
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
Sequence Models with Memory and Gating
Truncated Backpropagation
Gated Recurrent Unit
Long Short Term Memory
Advanced Sequence Models
Deep Recurrent Neural Networks
Bidirectional RNNs
Beam Search
Optimization Basics
Introduction to Optimization
Stochastic Gradient Descent for Deep Learning
Stochastic Gradient Descent
Batching
Momentum
Advanced Optimization Algorithms
Adagrad
Adam
Parallel Processing
Asynchrony
Automatic Parallelization
Multiple GPUs
Multiple GPUs in Gluon
Peak Performance on ImageNet
Gluon CV Toolkit
Sequence Models and Language
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Slides
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Content
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Sequence Models
Language Models
Recurrent Neural Networks
Text Preprocessing
Videos
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Table Of Contents
Sequence Models and Language
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Content
Videos
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Making a Computer Vision Model work
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Recurrent Neural Networks