diff --git a/doc/pub/BM/html/BM-bs.html b/doc/pub/BM/html/BM-bs.html index a4996e476..13c1c0081 100644 --- a/doc/pub/BM/html/BM-bs.html +++ b/doc/pub/BM/html/BM-bs.html @@ -40,8 +40,8 @@ Automatically generated HTML file from DocOnce source
  • Unsupervised learning, ovrarching aims
    • -
  • Types of Machine Learning
    • +
  • Types of Machine Learning, a repetition
    • +
  • Why Boltzmann machines?
  • Boltzmann Machines
  • Some similarities and differences from DNNs
  • The structure of the RBM network
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    Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University and Department of Physics, University of Oslo, Norway

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    Nov 19, 2018

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    Nov 20, 2018


    @@ -181,20 +181,7 @@ MathJax.Hub.Config({ -

    Unsupervised learning, ovrarching aims

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    -

    - -

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    - - -

    - - -

    Types of Machine Learning

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    Types of Machine Learning, a repetition

    @@ -216,11 +203,27 @@ Some of the most common tasks are:
  • Classification: Outputs are divided into two or more classes. The goal is to produce a model that assigns inputs into one of these classes. An example is to identify digits based on pictures of hand-written ones. Classification is typically supervised learning.
  • Regression: Finding a functional relationship between an input data set and a reference data set. The goal is to construct a function that maps input data to continuous output values.
  • Clustering: Data are divided into groups with certain common traits, without knowing the different groups beforehand. It is thus a form of unsupervised learning.
    • +
  • Other unsupervised learning algortihms, here Boltzmann machines
    • +

    + + +

    Why Boltzmann machines?

    + +

    +What is known as restricted Boltzmann Machines (RMB) have received a lot of attention lately. +One of the major reasons is that they can be stacked layer-wise to build deep neural networks that capture complicated statistics. + +

    +The original RBMs had just one visible layer and a hidden layer, but recently so-called Gaussian-binary RBMs have gained quite some popularity in imaging since they are capable of modeling continuous data that are common to natural images. + +

    +Furthermore, they have been used to solve complicated quantum mechanical many-particle problems or classical statistical physics problems like the Ising and Potts classes of models. +

    diff --git a/doc/pub/BM/html/BM-reveal.html b/doc/pub/BM/html/BM-reveal.html index 66b338087..032ca9983 100644 --- a/doc/pub/BM/html/BM-reveal.html +++ b/doc/pub/BM/html/BM-reveal.html @@ -147,7 +147,7 @@ MathJax.Hub.Config({

    Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University and Department of Physics, University of Oslo, Norway

     
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    Nov 19, 2018

    +

    Nov 20, 2018


    @@ -158,16 +158,7 @@ MathJax.Hub.Config({

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    Unsupervised learning, ovrarching aims

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    - -

    -

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    - - -
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    Types of Machine Learning

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    Types of Machine Learning, a repetition

    @@ -192,11 +183,28 @@ Some of the most common tasks are:

  • Regression: Finding a functional relationship between an input data set and a reference data set. The goal is to construct a function that maps input data to continuous output values.

  • Clustering: Data are divided into groups with certain common traits, without knowing the different groups beforehand. It is thus a form of unsupervised learning.
    • + +

  • Other unsupervised learning algortihms, here Boltzmann machines
    • +
    +

    Why Boltzmann machines?

    + +

    +What is known as restricted Boltzmann Machines (RMB) have received a lot of attention lately. +One of the major reasons is that they can be stacked layer-wise to build deep neural networks that capture complicated statistics. + +

    +The original RBMs had just one visible layer and a hidden layer, but recently so-called Gaussian-binary RBMs have gained quite some popularity in imaging since they are capable of modeling continuous data that are common to natural images. + +

    +Furthermore, they have been used to solve complicated quantum mechanical many-particle problems or classical statistical physics problems like the Ising and Potts classes of models. +

    + +

    Boltzmann Machines

    diff --git a/doc/pub/BM/html/BM-solarized.html b/doc/pub/BM/html/BM-solarized.html index f2642008e..23fe5f547 100644 --- a/doc/pub/BM/html/BM-solarized.html +++ b/doc/pub/BM/html/BM-solarized.html @@ -60,8 +60,8 @@ div { text-align: justify; text-justify: inter-word; } +

    Nov 20, 2018












    -

    Unsupervised learning, ovrarching aims

    -
    - -

    - - -

    - - -

    -









    - -

    Types of Machine Learning

    +

    Types of Machine Learning, a repetition

    @@ -177,10 +165,26 @@ Some of the most common tasks are:
  • Classification: Outputs are divided into two or more classes. The goal is to produce a model that assigns inputs into one of these classes. An example is to identify digits based on pictures of hand-written ones. Classification is typically supervised learning.
  • Regression: Finding a functional relationship between an input data set and a reference data set. The goal is to construct a function that maps input data to continuous output values.
  • Clustering: Data are divided into groups with certain common traits, without knowing the different groups beforehand. It is thus a form of unsupervised learning.
    • +
  • Other unsupervised learning algortihms, here Boltzmann machines
    • +

    +









    + +

    Why Boltzmann machines?

    + +

    +What is known as restricted Boltzmann Machines (RMB) have received a lot of attention lately. +One of the major reasons is that they can be stacked layer-wise to build deep neural networks that capture complicated statistics. + +

    +The original RBMs had just one visible layer and a hidden layer, but recently so-called Gaussian-binary RBMs have gained quite some popularity in imaging since they are capable of modeling continuous data that are common to natural images. + +

    +Furthermore, they have been used to solve complicated quantum mechanical many-particle problems or classical statistical physics problems like the Ising and Potts classes of models. +











    diff --git a/doc/pub/BM/html/BM.html b/doc/pub/BM/html/BM.html index 5524078ad..4f2c99ef3 100644 --- a/doc/pub/BM/html/BM.html +++ b/doc/pub/BM/html/BM.html @@ -65,8 +65,8 @@ div { text-align: justify; text-justify: inter-word; } +

    Nov 20, 2018












    -

    Unsupervised learning, ovrarching aims

    -
    - -

    - - -

    - - -

    -









    - -

    Types of Machine Learning

    +

    Types of Machine Learning, a repetition

    @@ -182,10 +170,26 @@ Some of the most common tasks are:
  • Classification: Outputs are divided into two or more classes. The goal is to produce a model that assigns inputs into one of these classes. An example is to identify digits based on pictures of hand-written ones. Classification is typically supervised learning.
  • Regression: Finding a functional relationship between an input data set and a reference data set. The goal is to construct a function that maps input data to continuous output values.
  • Clustering: Data are divided into groups with certain common traits, without knowing the different groups beforehand. It is thus a form of unsupervised learning.
    • +
  • Other unsupervised learning algortihms, here Boltzmann machines
    • +

    +









    + +

    Why Boltzmann machines?

    + +

    +What is known as restricted Boltzmann Machines (RMB) have received a lot of attention lately. +One of the major reasons is that they can be stacked layer-wise to build deep neural networks that capture complicated statistics. + +

    +The original RBMs had just one visible layer and a hidden layer, but recently so-called Gaussian-binary RBMs have gained quite some popularity in imaging since they are capable of modeling continuous data that are common to natural images. + +

    +Furthermore, they have been used to solve complicated quantum mechanical many-particle problems or classical statistical physics problems like the Ising and Potts classes of models. +











    diff --git a/doc/pub/BM/ipynb/BM.ipynb b/doc/pub/BM/ipynb/BM.ipynb index 965152b34..ae1152929 100644 --- a/doc/pub/BM/ipynb/BM.ipynb +++ b/doc/pub/BM/ipynb/BM.ipynb @@ -10,22 +10,15 @@ " \n", "**Morten Hjorth-Jensen**, Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University and Department of Physics, University of Oslo, Norway\n", "\n", - "Date: **Nov 19, 2018**\n", + "Date: **Nov 20, 2018**\n", "\n", "Copyright 1999-2018, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n", "\n", " \n", "\n", "\n", - "## Unsupervised learning, ovrarching aims\n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "## Types of Machine Learning\n", + "## Types of Machine Learning, a repetition\n", "\n", "The approaches to machine learning are many, but are often split into two main categories. \n", "In *supervised learning* we know the answer to a problem,\n", @@ -44,10 +37,21 @@ "\n", " * Clustering: Data are divided into groups with certain common traits, without knowing the different groups beforehand. It is thus a form of unsupervised learning.\n", "\n", + " * Other unsupervised learning algortihms, here Boltzmann machines\n", "\n", "\n", "\n", "\n", + "## Why Boltzmann machines?\n", + "\n", + "What is known as restricted Boltzmann Machines (RMB) have received a lot of attention lately. \n", + "One of the major reasons is that they can be stacked layer-wise to build deep neural networks that capture complicated statistics.\n", + "\n", + "The original RBMs had just one visible layer and a hidden layer, but recently so-called Gaussian-binary RBMs have gained quite some popularity in imaging since they are capable of modeling continuous data that are common to natural images. \n", + "\n", + "Furthermore, they have been used to solve complicated quantum mechanical many-particle problems or classical statistical physics problems like the Ising and Potts classes of models. \n", + "\n", + "\n", "\n", "\n", "## Boltzmann Machines\n", diff --git a/doc/pub/BM/ipynb/ipynb-BM-src.tar.gz b/doc/pub/BM/ipynb/ipynb-BM-src.tar.gz index b13aeacc2..0c5b14146 100644 Binary files a/doc/pub/BM/ipynb/ipynb-BM-src.tar.gz and b/doc/pub/BM/ipynb/ipynb-BM-src.tar.gz differ diff --git a/doc/pub/BM/pdf/BM-beamer-handouts2x3.pdf b/doc/pub/BM/pdf/BM-beamer-handouts2x3.pdf index 65d46ef1b..b14446a0f 100644 Binary files a/doc/pub/BM/pdf/BM-beamer-handouts2x3.pdf and b/doc/pub/BM/pdf/BM-beamer-handouts2x3.pdf differ diff --git a/doc/pub/BM/pdf/BM-beamer.pdf b/doc/pub/BM/pdf/BM-beamer.pdf index b353b4734..b9f2d3c41 100644 Binary files a/doc/pub/BM/pdf/BM-beamer.pdf and b/doc/pub/BM/pdf/BM-beamer.pdf differ diff --git a/doc/pub/BM/pdf/BM-minted.pdf b/doc/pub/BM/pdf/BM-minted.pdf index 329f88ad3..626afe46d 100644 Binary files a/doc/pub/BM/pdf/BM-minted.pdf and b/doc/pub/BM/pdf/BM-minted.pdf differ diff --git a/doc/src/BoltzmannMachines/BM.do.txt b/doc/src/BoltzmannMachines/BM.do.txt index 439ad1704..a3b7cc9d2 100644 --- a/doc/src/BoltzmannMachines/BM.do.txt +++ b/doc/src/BoltzmannMachines/BM.do.txt @@ -3,17 +3,9 @@ AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of DATE: today -!split -===== Unsupervised learning, ovrarching aims ===== -!bblock - - -!eblock - - !split -===== Types of Machine Learning ===== +===== Types of Machine Learning, a repetition ===== !bblock The approaches to machine learning are many, but are often split into two main categories. @@ -32,10 +24,22 @@ Some of the most common tasks are: * Regression: Finding a functional relationship between an input data set and a reference data set. The goal is to construct a function that maps input data to continuous output values. * Clustering: Data are divided into groups with certain common traits, without knowing the different groups beforehand. It is thus a form of unsupervised learning. + + * Other unsupervised learning algortihms, here Boltzmann machines !eblock +!split +===== Why Boltzmann machines? ===== +What is known as restricted Boltzmann Machines (RMB) have received a lot of attention lately. +One of the major reasons is that they can be stacked layer-wise to build deep neural networks that capture complicated statistics. + +The original RBMs had just one visible layer and a hidden layer, but recently so-called Gaussian-binary RBMs have gained quite some popularity in imaging since they are capable of modeling continuous data that are common to natural images. + +Furthermore, they have been used to solve complicated quantum mechanical many-particle problems or classical statistical physics problems like the Ising and Potts classes of models. + + !split