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week 40

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Morten Hjorth-Jensen
2021-10-04 20:57:06 +02:00
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doc/src/week40/week40.do.txt
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TITLE: Week 40: From Stochastic Gradient Descent to Neural networks
AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of Physics, University of Oslo & Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of Physics, University of Oslo, Norway & Department of Physics and Astronomy and Facility for Rare Ion Beams, Michigan State University, USA
DATE: today
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===== Plan for week 40 =====
* Thursday: Stochastic Gradient descent with examples and automatic differentiation and begin Neural Networks. "Video of Lecture":"https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOctober1.mp4?vrtx=view-as-webpage"
* Friday: Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model. "Video of Lecture":"https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOctober2.mp4?vrtx=view-as-webpage"
* Thursday: Stochastic Gradient descent with examples and automatic differentiation and begin Neural Networks.
* Friday: Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model.
Reading suggestions for both days: "Aurelien Geron's chapter 10":"https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf" and Hastie et al chapter 11.
For Stochastic Gradient Descent, we recommend chapter 4 of Geron's text.
For Stochastic Gradient Descent, we recommend chapter 4 of Geron's text.
For neural networks we recommend Goodfellow et al chapters 6 and 7 and Bishop 5.1-5.4
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===== Overview video for week 40 =====
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Here it is convenient to use stochastic gradient descent (see the examples below) with mini-batches with an outer loop that steps through multiple epochs of training.