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ویرایش:
نویسندگان: Peng Liu
سری:
ناشر: Manning Publications
سال نشر: 2023
تعداد صفحات: 288
زبان: English
فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود)
حجم فایل: 18 Mb
در صورت تبدیل فایل کتاب Regularization in Deep Learning (MEAP v6) به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب منظم سازی در یادگیری عمیق (MEAP v6) نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
Regularization in Deep Learning MEAP V06 Copyright Welcome letter Brief contents Chapter 1: Introducing Regularization 1.1 Why do we need regularization? 1.2 Curse of dimensionality 1.3 Understanding underfitting and overfitting 1.4 Understanding bias-variance trade-off 1.5 More on the model training path 1.6 Understanding the model training process 1.7 The many faces of regularization 1.8 Summary Chapter 2: Generalization: A Classical View 2.1 The data 2.1.1 Sampling from the underlying data distribution 2.1.2 The train-test split 2.2 The model 2.2.1 The prediction function 2.2.2 The bias trick 2.2.3 Implementing the prediction function 2.3 The cost function 2.3.1 Expressing the cost function with linear algebra 2.4 The optimization algorithm 2.4.1 The multiple minima 2.4.2 The closed-form solution of linear regression 2.4.3 The gradient descent algorithm 2.4.4 Different types of gradient descent 2.4.5 The stochastic gradient descent algorithm 2.4.6 The impact of the learning rate 2.5 Improving the predictive performance 2.5.1 Augmented representation via feature engineering 2.5.2 Quadratic basis function 2.6 Empirical risk minimization 2.6.1 More on the model 2.6.2 Bias and variance decomposition 2.6.3 Understanding bias and variance using bootstrap 2.6.4 Reduced generalization with high model complexity 2.7 Summary Chapter 3: Generalization: A Modern View 3.1 A modern view on generalization 3.1.1 Beyond perfect interpolation 3.1.2 Behind the “double descent” phenomenon 3.1.3 Extending the “double descent” phenomenon 3.2 Double Descent in Polynomial Regression 3.2.1 Smoothing spline 3.2.2 Rewriting the smoothing spline cost function 3.2.3 Deriving the closed-form solution 3.2.4 Implementing the smoothing spline model 3.2.5 Sample non-monotonicity 3.3 Summary Chapter 4: Fundamentals of Training Deep Neural Networks 4.1 Multilayer perceptron 4.1.1 A two-layer neural network 4.1.2 Shallow versus deep neural network 4.2 Automatic differentiation 4.2.1 Gradient-based optimization 4.2.2 The chain rule with partial derivatives 4.2.3 Different modes of multiplication 4.3 Training a simple CNN using MNIST 4.3.1 Download and loading MNIST 4.3.2 Defining the prediction function 4.3.3 Define the cost function 4.3.4 Define the optimization procedure 4.3.5 Update the weights via iterative training 4.4 More on generalization 4.4.1 Multiple global minima 4.4.2 Best versus worst global minimum 4.5 Summary Chapter 5: Regularization via Data 5.1 Data-based methods 5.1.1 Data augmentation 5.1.2 Label smoothing 5.2 Training deep neural networks using data augmentation 5.2.1 Training without data augmentation LeNet 5.2.2 Training with data augmentation 5.3 The deep bootstrap framework 5.3.1 Insufficiency of classical generalization framework 5.3.2 Online optimization 5.3.3 Connecting online optimization with offline generalization 5.3.4 Constructing the ideal world with CIFAR-5m 5.3.5 Model training in the ideal world 5.3.6 Model testing 5.3.7 Bootstrap error between real world and ideal world 5.3.8 Implicit bias in convolutional neural networks 5.4 Summary Chapter 6: Regularization via Model 6.1 Inductive bias in convolutional neural networks 6.1.1 Revisiting the fully-connected network 6.1.2 Translational invariance in convolutional neural networks 6.1.3 Understanding the convolution operator 6.1.4 Weight sharing in the convolution operation 6.2 Regularizing deep neural networks via dropout 6.2.1 Introducing dropout 6.2.2 Inducing a sparse representation 6.2.3 Dropout in action 6.2.4 Applying dropout in CNN 6.3 Implicit regularization in multi-task learning 6.3.1 Two MTL approaches in deep neural networks 6.3.2 Modifying the loss function to achieve soft parameter sharing 6.3.3 MTL in action 6.4 Summary Chapter 7: Regularization via Objective Function 7.1 Introducing the regularization term 7.1.1 The unregularized linear regression 7.1.2 Norm-based penalty 7.2 L2 regularization in ridge regression 7.2.1 Using the analytic solution 7.2.2 Using gradient descent algorithm 7.2.3 Handling the bias term 7.2.4 L2 regularization in action 7.3 Sparse estimation via LASSO 7.3.1 Geometric interpretation of ridge regression 7.3.2 Introducing the L0 norm 7.3.3 Introducing the L1 norm 7.3.4 Understanding LASSO 7.3.5 The soft-thresholding rule 7.3.6 LASSO in action 7.4 Summary Chapter 8: Regularization via Optimization 8.1 Stochastic optimization 8.1.1 Empirical risk minimization via gradient descent 8.1.2 Convergence of SGD 8.1.3 Implicit regularization of SGD 8.1.4 Analyzing the mean iterate 8.1.5 SGD variants: better or worse? 8.2 More on SGD convergence 8.2.1 SGD in univariate linear regression 8.2.2 SGD’s convergence in expectation 8.2.3 SGD: past, present, and future 8.3 Summary 08.pdf Chapter 8: Regularization via Optimization 8.1 Stochastic optimization 8.1.1 Empirical risk minimization via gradient descent 8.1.2 Convergence of SGD 8.1.3 Implicit regularization of SGD 8.1.4 Analyzing the mean iterate 8.1.5 SGD variants: better or worse? 8.2 More on SGD convergence 8.2.1 SGD in univariate linear regression 8.2.2 SGD’s convergence in expectation 8.2.3 SGD: past, present, and future 8.3 Summary