Deep Learning and Scientific Computing with R torch Book Club
Welcome
Book club meetings
Pace
1
Overview (Getting familiar with torch)
Setup
SLIDE 1
Meeting Videos
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2
On torch, and how to get it
SLIDE 1
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3
Tensors
What are tensors?
3.1
Creating tensors
3.1.1
Tensors from values
3.1.2
Tensors from specifications
3.1.3
Tensors from datasets
3.2
Operations on Tensors
3.2.1
Summary operations
3.3
Accessing Parts of a Tensor (indexing and slicing)
3.3.1
Beyond R
3.4
Reshaping Tensors
3.5
Broadcasting
Meeting Videos
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4
Autograd
SLIDE 1
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5
Function minimization with autograd
An Optimization Classic
Minimization from Scratch
Meeting Videos
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6
A neural network from scratch
SLIDE 1
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7
Modules
7.1
Built-in modules
7.2
Linear Layer:
nn_linear()
7.3
Sequential Models:
nn_squential()
7.4
General Models:
nn_module()
7.5
Meeting Videos
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8
Optimizers
SLIDE 1
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9
Loss functions
SLIDE 1
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10
Function minimization with L-BFGS
SLIDE 1
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11
Modularizing the neural network
SLIDE 1
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12
Overview (Deep learning with torch)
SLIDE 1
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13
Loading data
SLIDE 1
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14
Training with luz
SLIDE 1
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15
A first go at image classification
SLIDE 1
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16
Making models generalize
SLIDE 1
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17
Speeding up training
SLIDE 1
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18
Image classification, take two: Improving performance
SLIDE 1
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19
Image segmentation
SLIDE 1
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20
Tabular data
SLIDE 1
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21
Time series
SLIDE 1
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22
Audio classification
SLIDE 1
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23
Overview (Other things to do with torch)
SLIDE 1
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24
Matrix computations: Least-squares problems
SLIDE 1
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25
Matrix computations: Convolution
SLIDE 1
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26
Exploring the Discrete Fourier Transform (DFT)
SLIDE 1
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27
The Fast Fourier Transform (FFT)
SLIDE 1
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28
Wavelets
SLIDE 1
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Deep Learning and Scientific Computing with R torch Book Club
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