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
  - Cohort 1
2 On torch, and how to get it
- SLIDE 1
- Meeting Videos
  - Cohort 1
3 Tensors
4 Autograd
- SLIDE 1
- Meeting Videos
  - Cohort 1
5 Function minimization with autograd
6 A neural network from scratch
- SLIDE 1
- Meeting Videos
  - Cohort 1
7 Modules
8 Optimizers
- SLIDE 1
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  - Cohort 1
9 Loss functions
- SLIDE 1
- Meeting Videos
  - Cohort 1
10 Function minimization with L-BFGS
- SLIDE 1
- Meeting Videos
  - Cohort 1
11 Modularizing the neural network
- SLIDE 1
- Meeting Videos
  - Cohort 1
12 Overview (Deep learning with torch)
- SLIDE 1
- Meeting Videos
  - Cohort 1
13 Loading data
- SLIDE 1
- Meeting Videos
  - Cohort 1
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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  - Cohort 1
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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  - Cohort 1
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Deep Learning and Scientific Computing with R torch Book Club

Chapter 18 Image classification, take two: Improving performance

Learning objectives:

THESE ARE NICE TO HAVE BUT NOT ABSOLUTELY NECESSARY