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
- Meeting Videos
  - 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
- Meeting Videos
  - Cohort 1
15 A first go at image classification
- SLIDE 1
- Meeting Videos
  - Cohort 1
16 Making models generalize
- SLIDE 1
- Meeting Videos
  - Cohort 1
17 Speeding up training
- SLIDE 1
- Meeting Videos
  - Cohort 1
18 Image classification, take two: Improving performance
- SLIDE 1
- Meeting Videos
  - Cohort 1
19 Image segmentation
- SLIDE 1
- Meeting Videos
  - Cohort 1
20 Tabular data
- SLIDE 1
- Meeting Videos
  - Cohort 1
21 Time series
- SLIDE 1
- Meeting Videos
  - Cohort 1
22 Audio classification
- SLIDE 1
- Meeting Videos
  - Cohort 1
23 Overview (Other things to do with torch)
- SLIDE 1
- Meeting Videos
  - Cohort 1
24 Matrix computations: Least-squares problems
- SLIDE 1
- Meeting Videos
  - Cohort 1
25 Matrix computations: Convolution
- SLIDE 1
- Meeting Videos
  - Cohort 1
26 Exploring the Discrete Fourier Transform (DFT)
- SLIDE 1
- Meeting Videos
  - Cohort 1
27 The Fast Fourier Transform (FFT)
- SLIDE 1
- Meeting Videos
  - Cohort 1
28 Wavelets
- SLIDE 1
- Meeting Videos
  - Cohort 1
Published with bookdown

Deep Learning and Scientific Computing with R torch Book Club

Chapter 27 The Fast Fourier Transform (FFT)

Learning objectives:

THESE ARE NICE TO HAVE BUT NOT ABSOLUTELY NECESSARY