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<!-- <p id="title">Machine Learning Refined</p> -->
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<p class="sub-title"> The set of posts, cut into short series, use careful writing and interactive coding widgets
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<label for="chapter02"><span>CHAPTER 2. Zero order optimization methods </span></label>
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<p><strong>2.0. </strong> Motivation for mathematical optimization<a target="_blank" class="sublink-active" href="blog_posts/2_Zero_order_methods/2_0_Motivation.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/Mathematical_Optimization_presentations/Part_1_motivation_random_search.slides.html#/"> slides</a></p>
<p><strong>2.1. </strong> The zero order condition for optimality <a target="_blank" class="sublink-active" href="blog_posts/2_Zero_order_methods/2_1_Zero.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>2.2. </strong> Global optimization methods <a target="_blank" class="sublink-active" href="blog_posts/2_Zero_order_methods/2_2_Global.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>2.3. </strong> Local optimization methods <a target="_blank" class="sublink-active" href="blog_posts/2_Zero_order_methods/2_3_Local.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>2.4. </strong> Random search<a target="_blank" class="sublink-active" href="blog_posts/2_Zero_order_methods/2_4_Random.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>2.5. </strong> Coordinate search and descent<a target="_blank" class="sublink-active" href="blog_posts/2_Zero_order_methods/2_5_Coordinate.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
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<label for="chapter03"><span>CHAPTER 3. First order optimization methods</span></label>
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<p><strong>3.0. </strong> Introduction <a target="_blank" class="sublink-active" href="blog_posts/3_First_order_methods/3_0_Introduction.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>3.1. </strong> The first order optimality condition <a target="_blank" class="sublink-active" href="blog_posts/3_First_order_methods/3_1_First.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>3.2. </strong> The geometric anatomy of lines and hyperplanes <a target="_blank" class="sublink-active" href="blog_posts/3_First_order_methods/3_2_Hyperplane.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/6_First_order_methods/6_2_Hyperplane_anatomy.slides.html#/"> slides</a></p>
<p><strong>3.3. </strong> The geometric anatomy of first order Taylor series approximations <a target="_blank" class="sublink-active" href="blog_posts/3_First_order_methods/3_3_Tangent.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/6_First_order_methods/6_3_tangent_plane_anatomy.slides.html#/"> slides</a></p>
<p><strong>3.4. </strong> Automatic differentiation <a target="_blank" class="sublink-active" href="blog_posts/3_First_order_methods/3_4_Automatic.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/6_First_order_methods/6_4_Gradient_descent.slides.html#/"> slides</a></p>
<p><strong>3.5. </strong> Gradient descent <a target="_blank" class="sublink-active" href="blog_posts/3_First_order_methods/3_5_Descent.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>3.6. </strong> Two problems with the gradient descent direction <a target="_blank" class="sublink-active" href="blog_posts/3_First_order_methods/3_6_Problems.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>3.7. </strong> Momentum methods <a target="_blank" class="sublink-active" href="blog_posts/3_First_order_methods/3_7_Momentum.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>3.8. </strong> Normalized gradient descent <a target="_blank" class="sublink-active" href="blog_posts/3_First_order_methods/3_8_Normalized.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>3.9. </strong> Advanced gradient methods <a target="_blank" class="sublink-active" href="blog_posts/3_First_order_methods/3_9_Advanced.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>3.10. </strong> Mini-batch methods <a target="_blank" class="sublink-active" href="blog_posts/3_First_order_methods/3_10_Minibatch.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>3.11. </strong> Conservative steplength rules <a target="_blank" class="sublink-active" href="blog_posts/3_First_order_methods/3_11_Conservative.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
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<label for="chapter04"><span>CHAPTER 4. Second order optimization methods</span></label>
<div class="six_col">
<div class="description">
<p><strong>4.1. </strong> Quadratic functions <a target="_blank" class="sublink-inactive" href="blog_posts/4_Second_order_methods/4_1_Quadratic.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>4.2. </strong> Curvature and the second order optimality condition <a target="_blank" class="sublink-active" href="blog_posts/4_Second_order_methods/4_2_Second.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>4.3. </strong> Newton's method <a target="_blank" class="sublink-active" href="blog_posts/4_Second_order_methods/4_3_Newtons.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>4.4. </strong> Two problems with Newton's method <a target="_blank" class="sublink-active" href="blog_posts/4_Second_order_methods/4_4_Problems.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>4.5. </strong> Newton's method, regularization, and non-convex functions <a target="_blank" class="sublink-active" href="blog_posts/4_Second_order_methods/4_5_Nonconvex.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>4.6. </strong> Quasi-Newton methods <a target="_blank" class="sublink-active" href="blog_posts/4_Second_order_methods/4_6_Quasi.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
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<div class="chapters-container">
<input type="checkbox" name="chapters" id="chapter05" checked/>
<label for="chapter05"><span>CHAPTER 5. Linear regression </span></label>
<div class="five_col">
<div class="description">
<p><strong>5.1. </strong> Least squares linear regression <a target="_blank" class="sublink-active" href="blog_posts/5_Linear_regression/5_1_Least.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>5.2. </strong> Least absolute deviations linear regression <a target="_blank" class="sublink-active" href="blog_posts/5_Linear_regression/5_2_Absolute.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>5.3. </strong> Regression metrics and predictions <a target="_blank" class="sublink-active" href="blog_posts/5_Linear_regression/5_3_Metrics.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>5.4. </strong> Weighted regression <a target="_blank" class="sublink-active" href="blog_posts/5_Linear_regression/5_4_Weighted.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>5.5. </strong> Multi-output regression <a target="_blank" class="sublink-active" href="blog_posts/5_Linear_regression/5_5_Multi.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
</div>
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<input type="checkbox" name="chapters" id="chapter06" checked/>
<label for="chapter06"><span>CHAPTER 6. Linear two-class classification </span></label>
<div class="eight_col">
<div class="description">
<p><strong>6.1. </strong> Logistic regression and the Cross Entropy cost <a target="_blank" class="sublink-active" href="blog_posts/6_Linear_twoclass_classification/6_1_Cross_entropy.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/courses/deep_learning/Lecture_3_logistic_regression.slides.html#/"> slides</a></p>
<p><strong>6.2. </strong> Logistic regression and the Softmax cost <a target="_blank" class="sublink-active" href="blog_posts/6_Linear_twoclass_classification/6_2_Softmax.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/courses/deep_learning/Lecture_3_logistic_regression.slides.html#/"> slides</a></p>
<p><strong>6.3. </strong> The Perceptron <a target="_blank" class="sublink-active" href="blog_posts/6_Linear_twoclass_classification/6_3_Perceptron.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/courses/deep_learning/Lecture_3_logistic_regression.slides.html#/"> slides</a></p>
<p><strong>6.4. </strong> Support Vector Machines <a target="_blank" class="sublink-active" href="blog_posts/6_Linear_twoclass_classification/6_4_SVMs.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/courses/deep_learning/Lecture_3_logistic_regression.slides.html#/"> slides</a></p>
<p><strong>6.5. </strong> Classification with Categorical Labels <a target="_blank" class="sublink-active" href="blog_posts/6_Linear_twoclass_classification/6_5_Categorical.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/courses/deep_learning/Lecture_3_logistic_regression.slides.html#/"> slides</a></p>
<p><strong>6.6. </strong> Comparing classification schemes <a target="_blank" class="sublink-active" href="blog_posts/6_Linear_twoclass_classification/6_6_Comparison.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/courses/deep_learning/Lecture_3_logistic_regression.slides.html#/"> slides</a></p>
<p><strong>6.7. </strong> Two-class Classification Metrics <a target="_blank" class="sublink-active" href="blog_posts/6_Linear_twoclass_classification/6_7_Metrics.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/courses/deep_learning/Lecture_3_logistic_regression.slides.html#/"> slides</a></p>
<p><strong>6.8. </strong> Weighted two-class classification <a target="_blank" class="sublink-active" href="blog_posts/6_Linear_twoclass_classification/6_8_Weighted.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/courses/deep_learning/Lecture_3_logistic_regression.slides.html#/"> slides</a></p>
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<div class="chapters-container">
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<label for="chapter07"><span>CHAPTER 7. Linear multi-class classification </span></label>
<div class="five_col">
<div class="description">
<p><strong>7.1. </strong> One-versus-All classification <a target="_blank" class="sublink-active" href="blog_posts/7_Linear_multiclass_classification/7_1_OvA.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/courses/deep_learning/Lecture_5_one_versus_all.slides.html#/"> slides</a></p>
<p><strong>7.2. </strong> The Multi-class Perceptron <a target="_blank" class="sublink-active" href="blog_posts/7_Linear_multiclass_classification/7_2_Perceptron.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/courses/deep_learning/Lecture_5_one_versus_all.slides.html#/"> slides</a></p>
<p><strong>7.3. </strong> Comparing Multi-class Schemes <a target="_blank" class="sublink-active" href="blog_posts/7_Linear_multiclass_classification/7_3_Comparison.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/courses/deep_learning/Lecture_5_one_versus_all.slides.html#/"> slides</a></p>
<p><strong>7.4. </strong> Multi-class Classification with Categorical Labels <a target="_blank" class="sublink-active" href="blog_posts/7_Linear_multiclass_classification/7_4_Categorical.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/courses/deep_learning/Lecture_5_one_versus_all.slides.html#/"> slides</a></p>
<p><strong>7.5. </strong> Multi-class Classification Metrics <a target="_blank" class="sublink-active" href="blog_posts/7_Linear_multiclass_classification/7_5_Metrics.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/courses/deep_learning/Lecture_5_one_versus_all.slides.html#/"> slides</a></p>
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<div class="chapters-container">
<input type="checkbox" name="chapters" id="chapter08" checked/>
<label for="chapter08"><span>CHAPTER 8. Linear unsupervised learning </span></label>
<div class="seven_col">
<div class="description">
<p><strong>8.1. </strong> Fixed spanning sets, orthonormality, and projections <a target="_blank" class="sublink-active" href="blog_posts/8_Linear_unsupervised_learning/8_1_Spanning.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>8.2. </strong> Principal Component Analysis and the Autoencoder <a target="_blank" class="sublink-active" href="blog_posts/8_Linear_unsupervised_learning/8_2_PCA.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>8.3. </strong> The Linear Autoencoder <a target="_blank" class="sublink-active" href="blog_posts/8_Linear_unsupervised_learning/8_3_Autoencoder.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>8.4. </strong> The Classic PCA Solution <a target="_blank" class="sublink-active" href="blog_posts/8_Linear_unsupervised_learning/8_4_Classic.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>8.5. </strong> Recommender Systems <a target="_blank" class="sublink-active" href="blog_posts/8_Linear_unsupervised_learning/8_5_Recommender.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>8.6. </strong> K-means clustering <a target="_blank" class="sublink-active" href="blog_posts/8_Linear_unsupervised_learning/8_6_Kmeans.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>8.7. </strong> General matrix factorization techniques <a target="_blank" class="sublink-active" href="blog_posts/8_Linear_unsupervised_learning/8_7_Factorization.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
</div>
</div>
</div>
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<input type="checkbox" name="chapters" id="chapter09" checked/>
<label for="chapter09"><span>CHAPTER 9. Principles of Feature Engineering and Selection </span></label>
<div class="six_col">
<div class="description">
<p><strong>9.1. </strong> Histogram Features <a target="_blank" class="sublink-active" href="blog_posts/9_Feature_engineer_select/9_1_Histogram.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/courses/deep_learning/Lecture_5_one_versus_all.slides.html#/"> slides</a></p>
<p><strong>9.2. </strong> Feature Scaling via Standard Normalization <a target="_blank" class="sublink-active" href="blog_posts/9_Feature_engineer_select/9_2_Scaling.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/courses/deep_learning/Lecture_5_one_versus_all.slides.html#/"> slides</a></p>
<p><strong>9.3. </strong> Imputing missing values <a target="_blank" class="sublink-active" href="blog_posts/9_Feature_engineer_select/9_3_Cleaning.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/courses/deep_learning/Lecture_5_one_versus_all.slides.html#/"> slides</a></p>
<p><strong>9.4. </strong> Feature Scaling via PCA Sphereing <a target="_blank" class="sublink-active" href="blog_posts/9_Feature_engineer_select/9_4_PCA_sphereing.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/courses/deep_learning/Lecture_5_one_versus_all.slides.html#/"> slides</a></p>
<p><strong>9.5. </strong> Feature Selection via Boosting <a target="_blank" class="sublink-active" href="blog_posts/9_Feature_engineer_select/9_5_Boosting.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/courses/deep_learning/Lecture_5_one_versus_all.slides.html#/"> slides</a></p>
<p><strong>9.6. </strong> Feature Selection via Regularization <a target="_blank" class="sublink-active" href="blog_posts/9_Feature_engineer_select/9_6_Regularization.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/courses/deep_learning/Lecture_5_one_versus_all.slides.html#/"> slides</a></p>
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<label for="chapter10"><span>CHAPTER 10. Introduction to Nonlinear Learning</span></label>
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<p><strong>10.1. </strong> Nonlinear Regression <a target="_blank" class="sublink-active" href="blog_posts/10_Nonlinear_intro/10_1_Regression.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/12_Nonlinear_intro/12_1_Introduction_nonlinear_regression_SLIDES.slides.html#/"> slides</a></p>
<p><strong>10.2. </strong> Nonlinear Multi-output Regression <a target="_blank" class="sublink-active" href="blog_posts/10_Nonlinear_intro/10_2_MultReg.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/12_Nonlinear_intro/12_1_Introduction_nonlinear_regression_SLIDES.slides.html#/"> slides</a></p>
<p><strong>10.3. </strong> Nonlinear Two-class Classification <a target="_blank" class="sublink-active" href="blog_posts/10_Nonlinear_intro/10_3_Twoclass.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/12_Nonlinear_intro/12_1_Introduction_nonlinear_regression_SLIDES.slides.html#/"> slides</a></p>
<p><strong>10.4. </strong> Nonlinear Multiclass-class Classification <a target="_blank" class="sublink-active" href="blog_posts/10_Nonlinear_intro/10_4_Multiclass.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/12_Nonlinear_intro/12_1_Introduction_nonlinear_regression_SLIDES.slides.html#/"> slides</a></p>
<p><strong>10.5. </strong> Nonlinear Unsupervised Learning <a target="_blank" class="sublink-active" href="blog_posts/10_Nonlinear_intro/10_5_Unsupervised.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/12_Nonlinear_intro/12_1_Introduction_nonlinear_regression_SLIDES.slides.html#/"> slides</a></p>
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<label for="chapter11"><span>CHAPTER 11. Principles of Feature Learning </span></label>
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<p><strong>11.1. </strong> Universal Approximators <a target="_blank" class="sublink-active" href="blog_posts/11_Feature_learning/11_1_Universal.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/12_Nonlinear_intro/12_1_Introduction_nonlinear_regression_SLIDES.slides.html#/"> slides</a></p>
<p><strong>11.2. </strong> The bias-variance Tradeoff and Cross-Validation <a target="_blank" class="sublink-active" href="blog_posts/11_Feature_learning/11_2_BiasVariance.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/12_Nonlinear_intro/12_1_Introduction_nonlinear_regression_SLIDES.slides.html#/"> slides</a></p>
<p><strong>11.3. </strong> Boosting <a target="_blank" class="sublink-active" href="blog_posts/11_Feature_learning/11_3_Boosting.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/12_Nonlinear_intro/12_1_Introduction_nonlinear_regression_SLIDES.slides.html#/"> slides</a></p>
<p><strong>11.4. </strong> Regularization <a target="_blank" class="sublink-active" href="blog_posts/11_Feature_learning/11_4_Regularization.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/12_Nonlinear_intro/12_1_Introduction_nonlinear_regression_SLIDES.slides.html#/"> slides</a></p>
<p><strong>11.5. </strong> Ensembling <a target="_blank" class="sublink-active" href="blog_posts/11_Feature_learning/11_5_Ensembling.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/12_Nonlinear_intro/12_1_Introduction_nonlinear_regression_SLIDES.slides.html#/"> slides</a></p>
<p><strong>11.6. </strong> K-Folds Cross-Validation <a target="_blank" class="sublink-active" href="blog_posts/11_Feature_learning/11_6_Kfolds.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/12_Nonlinear_intro/12_1_Introduction_nonlinear_regression_SLIDES.slides.html#/"> slides</a></p>
<p><strong>11.7. </strong> Testing <a target="_blank" class="sublink-active" href="blog_posts/11_Feature_learning/11_7_Testing.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/12_Nonlinear_intro/12_1_Introduction_nonlinear_regression_SLIDES.slides.html#/"> slides</a></p>
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<label for="chapter12"><span>CHAPTER 12. Kernels </span></label>
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<p><strong>12.1. </strong> The Variety of Kernel-based Learners <a target="_blank" class="sublink-inactive" href="blog_posts/11_Feature_learning/11_1_Universal_approximation.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/12_Nonlinear_intro/12_1_Introduction_nonlinear_regression_SLIDES.slides.html#/"> slides</a></p>
<p><strong>12.2. </strong> The Kernel Trick <a target="_blank" class="sublink-inactive" href="blog_posts/11_Feature_learning/11_1_Universal_approximation.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/12_Nonlinear_intro/12_1_Introduction_nonlinear_regression_SLIDES.slides.html#/"> slides</a></p>
<p><strong>12.3. </strong> Kernels as Similarity Measures <a target="_blank" class="sublink-inactive" href="blog_posts/11_Feature_learning/11_1_Universal_approximation.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/12_Nonlinear_intro/12_1_Introduction_nonlinear_regression_SLIDES.slides.html#/"> slides</a></p>
<p><strong>12.4. </strong> Scaling Kernels <a target="_blank" class="sublink-inactive" href="blog_posts/11_Feature_learning/11_1_Universal_approximation.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/12_Nonlinear_intro/12_1_Introduction_nonlinear_regression_SLIDES.slides.html#/"> slides</a></p>
<p><strong>12.5. </strong> Cross-validation with Kernels <a target="_blank" class="sublink-inactive" href="blog_posts/11_Feature_learning/11_1_Universal_approximation.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/12_Nonlinear_intro/12_1_Introduction_nonlinear_regression_SLIDES.slides.html#/"> slides</a></p>
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<label for="chapter13"><span>CHAPTER 13. Multi-layer perceptrons (MLPs) </span></label>
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<p><strong>13.1. </strong> Multi-layer perceptrons <a target="_blank" class="sublink-active" href="blog_posts/13_Multilayer_perceptrons/13_1_Multi_layer_perceptrons.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/13_Multilayer_perceptrons/13_1_Multi_layer_perceptrons_SLIDES.slides.html#/"> slides</a></p>
<p><strong>13.2. </strong> Optimization <a target="_blank" class="sublink-active" href="blog_posts/13_Multilayer_perceptrons/13_2_Optimization.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/13_Multilayer_perceptrons/13_1_Multi_layer_perceptrons_SLIDES.slides.html#/"> slides</a></p>
<p><strong>13.3. </strong> The Backpropogation Algorithm <a target="_blank" class="sublink-inactive" href="blog_posts/13_Multilayer_perceptrons/13_3_Backprop.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/13_Multilayer_perceptrons/13_1_Multi_layer_perceptrons_SLIDES.slides.html#/"> slides</a></p>
<p><strong>13.4. </strong> Activation functions <a target="_blank" class="sublink-inactive" href="blog_posts/13_Multilayer_perceptrons/13_4_Activation.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>13.5. </strong> Batch normalization <a target="_blank" class="sublink-active" href="blog_posts/13_Multilayer_perceptrons/13_5_Batch_normalization.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>13.6. </strong> Early Stopping <a target="_blank" class="sublink-active" href="blog_posts/13_Multilayer_perceptrons/13_6_early_stopping.html"> text</a> <a target="_blank" class="sublink-active" href="presentations/13_Multilayer_perceptrons/13_4_Momentum_trick_SLIDES.slides.html#/"> slides</a></p>
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<label for="chapter14"><span>CHAPTER 14. Trees </span></label>
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<p><strong>14.1. </strong> The Variety of Tree-based Learners <a target="_blank" class="sublink-inactive" href="blog_posts/13_Multilayer_perceptrons/13_1_Multi_layer_perceptrons.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/13_Multilayer_perceptrons/13_1_Multi_layer_perceptrons_SLIDES.slides.html#/"> slides</a></p>
<p><strong>14.2. </strong> Generating Trees <a target="_blank" class="sublink-inactive" href="blog_posts/13_Multilayer_perceptrons/13_2_Optimization.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/13_Multilayer_perceptrons/13_1_Multi_layer_perceptrons_SLIDES.slides.html#/"> slides</a></p>
<p><strong>14.3. </strong> Interpreting Tree-based Models <a target="_blank" class="sublink-inactive" href="blog_posts/13_Multilayer_perceptrons/13_3_Backprop.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/13_Multilayer_perceptrons/13_1_Multi_layer_perceptrons_SLIDES.slides.html#/"> slides</a></p>
<p><strong>14.4. </strong> Boosting and Early-Stopping <a target="_blank" class="sublink-inactive" href="blog_posts/13_Multilayer_perceptrons/13_4_Activation.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>14.5. </strong> Ensembling and Random Forests <a target="_blank" class="sublink-inactive" href="blog_posts/13_Multilayer_perceptrons/13_4_Activation.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
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<label for="chapter15"><span>CHAPTER 15. Calculus and Automatic Differentiation </span></label>
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<p><strong>15.1. </strong> The Derivative and Gradient <a target="_blank" class="sublink-inactive" href="blog_posts/13_Multilayer_perceptrons/13_1_Multi_layer_perceptrons.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/13_Multilayer_perceptrons/13_1_Multi_layer_perceptrons_SLIDES.slides.html#/"> slides</a></p>
<p><strong>15.2. </strong> Derivatives of Elementary Functions <a target="_blank" class="sublink-inactive" href="blog_posts/13_Multilayer_perceptrons/13_2_Optimization.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/13_Multilayer_perceptrons/13_1_Multi_layer_perceptrons_SLIDES.slides.html#/"> slides</a></p>
<p><strong>15.3. </strong> Computation Graphs <a target="_blank" class="sublink-inactive" href="blog_posts/13_Multilayer_perceptrons/13_3_Backprop.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/13_Multilayer_perceptrons/13_1_Multi_layer_perceptrons_SLIDES.slides.html#/"> slides</a></p>
<p><strong>15.4. </strong> The Forward Mode of Automatic Differentiation <a target="_blank" class="sublink-inactive" href="blog_posts/13_Multilayer_perceptrons/13_4_Activation.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>15.5. </strong> The Reverse Mode of Automatic Differentiation <a target="_blank" class="sublink-inactive" href="blog_posts/13_Multilayer_perceptrons/13_4_Activation.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>15.6. </strong> Higher Order Derivatives <a target="_blank" class="sublink-inactive" href="blog_posts/13_Multilayer_perceptrons/13_4_Activation.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>15.7. </strong> Taylor Series <a target="_blank" class="sublink-inactive" href="blog_posts/13_Multilayer_perceptrons/13_4_Activation.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
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<label for="chapter16"><span>CHAPTER 16. Elements of Linear Algebra and Statistics </span></label>
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<p><strong>16.1. </strong> Vectors and Vector Arithmetic <a target="_blank" class="sublink-active" href="blog_posts/16_Computational_linear_algebra/16_1_Vectors.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/13_Multilayer_perceptrons/13_1_Multi_layer_perceptrons_SLIDES.slides.html#/"> slides</a></p>
<p><strong>16.2. </strong> Matrices and Matrix Arithmetic <a target="_blank" class="sublink-active" href="blog_posts/16_Computational_linear_algebra/16_2_Matrices.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/13_Multilayer_perceptrons/13_1_Multi_layer_perceptrons_SLIDES.slides.html#/"> slides</a></p>
<p><strong>16.3. </strong> Eigenvectors and Eigenvalues <a target="_blank" class="sublink-active" href="blog_posts/16_Computational_linear_algebra/16_3_Eigen.html"> text</a> <a target="_blank" class="sublink-inactive" href="presentations/13_Multilayer_perceptrons/13_1_Multi_layer_perceptrons_SLIDES.slides.html#/"> slides</a></p>
<p><strong>16.4. </strong> Vector Norms <a target="_blank" class="sublink-active" href="blog_posts/16_Computational_linear_algebra/16_4_Norms.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
<p><strong>16.5. </strong> Sample Statistics <a target="_blank" class="sublink-inactive" href="blog_posts/16_Computational_linear_algebra/13_4_Activation.html"> text</a> <a target="_blank" class="sublink-inactive" href=""> slides</a></p>
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