update jupyter-book
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Morten Hjorth-Jensen
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<h1 class="site-logo" id="site-title">Applied Data Analysis and Machine Learning</h1>
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Applied Data Analysis and Machine Learning
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</a>
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</ul>
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<p aria-level="2" class="caption" role="heading">
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About the course
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Teaching schedule with links to material
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<li class="toctree-l1">
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<a class="reference internal" href="teachers.html">
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Teachers and Grading
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Textbooks
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</ul>
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<p aria-level="2" class="caption" role="heading">
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<span class="caption-text">
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Review of Statistics with Resampling Techniques and Linear Algebra
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</span>
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</p>
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<ul class="nav bd-sidenav">
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<li class="toctree-l1">
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<a class="reference internal" href="statistics.html">
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1. Elements of Probability Theory and Statistical Data Analysis
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="linalg.html">
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2. Linear Algebra, Handling of Arrays and more Python Features
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</a>
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</li>
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</ul>
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<p aria-level="2" class="caption" role="heading">
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<span class="caption-text">
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From Regression to Support Vector Machines
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</span>
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</p>
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<ul class="nav bd-sidenav">
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<li class="toctree-l1">
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<a class="reference internal" href="chapter1.html">
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3. Linear Regression
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="chapter2.html">
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4. Ridge and Lasso Regression
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="chapter3.html">
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5. Resampling Methods
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="chapter4.html">
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6. Logistic Regression
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="chapteroptimization.html">
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7. Optimization, the central part of any Machine Learning algortithm
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="chapter5.html">
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8. Support Vector Machines, overarching aims
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</a>
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</li>
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</ul>
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<p aria-level="2" class="caption" role="heading">
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<span class="caption-text">
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Decision Trees, Ensemble Methods and Boosting
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</span>
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</p>
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<ul class="nav bd-sidenav">
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<li class="toctree-l1">
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<a class="reference internal" href="chapter6.html">
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9. Decision trees, overarching aims
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="chapter7.html">
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10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
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</a>
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</li>
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</ul>
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<p aria-level="2" class="caption" role="heading">
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<span class="caption-text">
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Dimensionality Reduction
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</span>
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</p>
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<ul class="nav bd-sidenav">
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<li class="toctree-l1">
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<a class="reference internal" href="chapter8.html">
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11. Basic ideas of the Principal Component Analysis (PCA)
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="clustering.html">
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12. Clustering and Unsupervised Learning
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</a>
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</li>
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</ul>
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<p aria-level="2" class="caption" role="heading">
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<span class="caption-text">
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Deep Learning Methods
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</span>
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</p>
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<ul class="nav bd-sidenav">
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<li class="toctree-l1">
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<a class="reference internal" href="chapter9.html">
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13. Neural networks
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="chapter10.html">
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14. Building a Feed Forward Neural Network
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="chapter11.html">
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15. Solving Differential Equations with Deep Learning
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="chapter12.html">
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16. Convolutional Neural Networks
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="chapter13.html">
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17. Recurrent neural networks: Overarching view
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</a>
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</li>
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</ul>
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<p aria-level="2" class="caption" role="heading">
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<span class="caption-text">
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Weekly material, notes and exercises
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</span>
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</p>
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<ul class="nav bd-sidenav">
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<li class="toctree-l1">
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<a class="reference internal" href="exercisesweek34.html">
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Exercises week 34
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="week34.html">
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Week 34: Introduction to the course, Logistics and Practicalities
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</a>
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<li class="toctree-l1">
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<a class="reference internal" href="exercisesweek35.html">
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Exercises week 35
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="week35.html">
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Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="exercisesweek36.html">
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Exercises week 36
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="week36.html">
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Week 36: Linear Regression and Statistical interpretations
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="exercisesweek37.html">
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Exercises week 37
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="week37.html">
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Week 37: Statistical interpretations and Resampling Methods
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="exercisesweek38.html">
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Exercises week 38
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="week38.html">
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Week 38: Logistic Regression and Optimization
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</a>
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<li class="toctree-l1">
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<a class="reference internal" href="exercisesweek39.html">
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Exercises week 39
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="week39.html">
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Week 39: Optimization and Gradient Methods
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="week40.html">
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Week 40: Gradient descent methods (continued) and start Neural networks
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="exercisesweek41.html">
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Exercises week 41
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</a>
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</li>
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Week 41 Neural networks and constructing a neural network code
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Exercises week 42
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Week 42 Constructing a Neural Network code with examples
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Exercises Week 42: Logistic Regression and Optimization, reminders from week 38 and week 40
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Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
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Exercises week 43
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Week 45, Convolutional Neural Networks (CCNs) and Recurrent Neural Networks (RNNs)
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Applied Data Analysis and Machine Learning
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<li class="toctree-l1"><a class="reference internal" href="chapter1.html">3. Linear Regression</a></li>
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<li class="toctree-l1"><a class="reference internal" href="chapter7.html">10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods</a></li>
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<li class="toctree-l1"><a class="reference internal" href="chapter11.html">15. Solving Differential Equations with Deep Learning</a></li>
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<li class="toctree-l1"><a class="reference internal" href="exercisesweek34.html">Exercises week 34</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week34.html">Week 34: Introduction to the course, Logistics and Practicalities</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week38.html">Week 38: Logistic Regression and Optimization</a></li>
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<li class="toctree-l1"><a class="reference internal" href="exercisesweek39.html">Exercises week 39</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week39.html">Week 39: Optimization and Gradient Methods</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week40.html">Week 40: Gradient descent methods (continued) and start Neural networks</a></li>
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<li class="toctree-l1"><a class="reference internal" href="exercisesweek41.html">Exercises week 41</a></li>
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||||
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||||
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||||
<li class="toctree-l1"><a class="reference internal" href="week41.html">Week 41 Neural networks and constructing a neural network code</a></li>
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<li class="toctree-l1"><a class="reference internal" href="exercisesweek42.html">Exercises week 42</a></li>
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||||
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||||
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||||
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||||
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||||
<li class="toctree-l1"><a class="reference internal" href="week42.html">Week 42 Constructing a Neural Network code with examples</a></li>
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||||
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||||
<li class="toctree-l1"><a class="reference internal" href="week43.html">Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations</a></li>
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||||
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||||
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||||
<li class="toctree-l1"><a class="reference internal" href="week44.html">Week 44, Convolutional Neural Networks (CNN)</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week45.html">Week 45, Convolutional Neural Networks (CCNs) and Recurrent Neural Networks (RNNs)</a></li>
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<li class="toctree-l1"><a class="reference internal" href="week46.html">Week 46: Decision Trees, Ensemble methods and Random Forests</a></li>
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</main>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Scripts loaded after <body> so the DOM is not blocked -->
|
||||
<script defer src="_static/scripts/bootstrap.js?digest=26a4bc78f4c0ddb94549"></script>
|
||||
<script defer src="_static/scripts/pydata-sphinx-theme.js?digest=26a4bc78f4c0ddb94549"></script>
|
||||
|
||||
<footer class="bd-footer">
|
||||
</footer>
|
||||
</body>
|
||||
</html>
|
||||
Reference in New Issue
Block a user