Exploring the Use of Experimental Design Techniques for Hyperparameter Optimization in Convolutional Neural Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Exploring the Use of Experimental Design Techniques for Hyperparameter Optimization in Convolutional Neural Networks write by Ashley Chiu. This book was released on 2021. Exploring the Use of Experimental Design Techniques for Hyperparameter Optimization in Convolutional Neural Networks available in PDF, EPUB and Kindle. Deep learning techniques have become commonplace tools for complex prediction, classification, and recognition tasks. Yet, the performance of such learning techniques is highly influenced by user-set hyperparameters. As a result, efficient hyperparameter tuning and optimization is an increasingly important area of study. Traditional model-free tuning methods are often computationally inefficient and may miss optimal settings, while model-based approaches rely on parametric models and cannot easily be parallelized. In this thesis, we propose the use of experimental design techniques, a Design of Experiments (DOE) Approach, to more efficiently and intuitively optimize hyperparameters. We use fractional factorial designs, nearly orthogonal arrays, sliced Latin hypercube designs, and composite variations of these three small-run designs to identify relationships between continuous, discrete and categorical hyperparameters and test accuracy in convolutional neural networks using beta regression. We find that our proposed methodology successfully identifies optimal hyperparameter settings for convolutional neural networks trained on the MNIST dataset.
Spatially Explicit Hyperparameter Optimization for Neural Networks
Spatially Explicit Hyperparameter Optimization for Neural Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Spatially Explicit Hyperparameter Optimization for Neural Networks write by Minrui Zheng. This book was released on 2021-10-18. Spatially Explicit Hyperparameter Optimization for Neural Networks available in PDF, EPUB and Kindle. Neural networks as the commonly used machine learning algorithms, such as artificial neural networks (ANNs) and convolutional neural networks (CNNs), have been extensively used in the GIScience domain to explore the nonlinear and complex geographic phenomena. However, there are a few studies that investigate the parameter settings of neural networks in GIScience. Moreover, the model performance of neural networks often depends on the parameter setting for a given dataset. Meanwhile, adjusting the parameter configuration of neural networks will increase the overall running time. Therefore, an automated approach is necessary for addressing these limitations in current studies. This book proposes an automated spatially explicit hyperparameter optimization approach to identify optimal or near-optimal parameter settings for neural networks in the GIScience field. Also, the approach improves the computing performance at both model and computing levels. This book is written for researchers of the GIScience field as well as social science subjects.
Automated Machine Learning
Automated Machine Learning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Automated Machine Learning write by Frank Hutter. This book was released on 2019-05-17. Automated Machine Learning available in PDF, EPUB and Kindle. This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.
Automated Deep Learning Using Neural Network Intelligence
Automated Deep Learning Using Neural Network Intelligence - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Automated Deep Learning Using Neural Network Intelligence write by Ivan Gridin. This book was released on 2022-06-21. Automated Deep Learning Using Neural Network Intelligence available in PDF, EPUB and Kindle. Optimize, develop, and design PyTorch and TensorFlow models for a specific problem using the Microsoft Neural Network Intelligence (NNI) toolkit. This book includes practical examples illustrating automated deep learning approaches and provides techniques to facilitate your deep learning model development. The first chapters of this book cover the basics of NNI toolkit usage and methods for solving hyper-parameter optimization tasks. You will understand the black-box function maximization problem using NNI, and know how to prepare a TensorFlow or PyTorch model for hyper-parameter tuning, launch an experiment, and interpret the results. The book dives into optimization tuners and the search algorithms they are based on: Evolution search, Annealing search, and the Bayesian Optimization approach. The Neural Architecture Search is covered and you will learn how to develop deep learning models from scratch. Multi-trial and one-shot searching approaches of automatic neural network design are presented. The book teaches you how to construct a search space and launch an architecture search using the latest state-of-the-art exploration strategies: Efficient Neural Architecture Search (ENAS) and Differential Architectural Search (DARTS). You will learn how to automate the construction of a neural network architecture for a particular problem and dataset. The book focuses on model compression and feature engineering methods that are essential in automated deep learning. It also includes performance techniques that allow the creation of large-scale distributive training platforms using NNI. After reading this book, you will know how to use the full toolkit of automated deep learning methods. The techniques and practical examples presented in this book will allow you to bring your neural network routines to a higher level. What You Will Learn Know the basic concepts of optimization tuners, search space, and trials Apply different hyper-parameter optimization algorithms to develop effective neural networks Construct new deep learning models from scratch Execute the automated Neural Architecture Search to create state-of-the-art deep learning models Compress the model to eliminate unnecessary deep learning layers Who This Book Is For Intermediate to advanced data scientists and machine learning engineers involved in deep learning and practical neural network development
Convergent and Efficient Methods to Optimize Deep Learning
Convergent and Efficient Methods to Optimize Deep Learning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Convergent and Efficient Methods to Optimize Deep Learning write by Mehdi Mashayekhi. This book was released on 2022. Convergent and Efficient Methods to Optimize Deep Learning available in PDF, EPUB and Kindle. Deep Learning Neural Networks (DLNNs) are flexible modeling methods, capable of generating prediction of both continuous and discrete outputs. These methods continue to make large contributions to people’s lives. Machine Learning (ML) algorithms are efficient in handling everyday problems, especially big-data ones. DLNNs have a variety of applications, such as recovering disrupted audio files, self-driving cars, YouTube tumblers, and the list goes on. Nonetheless, the performance of DLNNs and ML algorithms, in general, depends upon a collection of choices made by their users. These decisions can be described using factors called “hyperparameters” or “generalized hyperparameters” and further categorized into three groups. We say “generalized” because some of the groups might not conventionally be optimized over. One group defines the structure of a DLNN, for instance, the number of layers, activation functions, and the layer type. The second group relates to the parameters governing the optimization algorithms to derive the weights which minimize the loss function. Some might argue that optimizing over these hyperparameters endangers convergence on training sets for the weight optimization. Yet, here we consider these hyperparameters to be fully adjustable because we argue that fostering test set (unseen data) prediction accuracy is more important than the surrogate goal of achieving convergence on training sets. The third group of hyperparameters relates to controlling data preparation including feature generation and the sampling of training sets. The problem of optimally designing these generalized hyperparameter settings has received relatively little attention. In addition, DLNNs have large numbers of hyperparameters due to their structure. Here, we focus on optimization examples involving eight generalized hyperparameters. The large number of options makes the associate decision problem for DLNN design difficult. The common approaches for this problem include so-called grid searches which are “full factorials” in the experimental design literature and so-called “Tree Parzan Estimator (TPE)” which is often one choice from a family of black box optimization methods. In general, the problem of how to even formulate the generalized hyperparameter problem that includes a desire to predict well on validation and test sets has been minimally studied. Here, we propose two formulations relating to fostering the most accurate empirical models possible. To effectively solve these formulations and foster accurate deep learning models, we explore two types of approaches. First, for problems with big data where testing is computationally expensive, single shot Design of Experiment (DOE) approaches are explored systematically to support the generation of alternatives to grid search, which is equivalent to full factorial experimentation. A so-called “meta-experiment” is designed to study how choices about random sampling, the effects of different types of data, and different validation strategies impact the performance of DLNNs on twelve standard datasets from the literature. Findings from this study include the promising merits of resolution V fractional factorials and the general benefits of combining sampling and k-fold validation. Both random effects and fixed effects models for the meta-experiment are considered to explore the generality and practical implications of the findings. Second, in problems for which large numbers of trained DLNNs are possible (e.g., over 30), hyperparameter optimization is explored formally as a simulation-optimization problem. Because simulation optimization can be viewed as sequential design and analysis of experiments, this effort represents an extension of the study of “single shot” DOE. Then, an extension of a well-known algorithm is proposed called Randomized Balanced Explorative and Exploitative Search with Choice (R-BEESE-C) sets. The intent is to capitalize on the fact that there are patterns in the solutions of past problems that give human intuition the ability to pick relevant “choice” sets for prioritization. Then, R-BEESE-C is compared to three other competitors including the popular TPE methods from Hyperopt on 12 test classification problems with three replicates, and the results are discussed. On seven of the test problems, R-BEESE-C derives the highest average accuracy compared to all alternatives. Also, we prove that, with sufficiently high numbers of evaluations, R-BEESE-C converges almost surely to the global optimal generalized hyperparameter solution for all datasets with a few qualifications. Because of our computational results and the convergence, we believe that our recommended approaches including resolution V fractional factorials and R-BEESE-C offer the most relevant approaches for deep learning hyperparameter optimization presently available. Finally, an application of DLNNs in social media is explored. R-BEESE-C is tested for optimizing hyperparameters of the Deep Learning (DL) in Bidirectional Encoder Representations from Transformers (BERT) to detect fake news regarding COVID19. The study is based on 4,505 news articles in addition to posts in social media platforms, such as Facebook. The results illustrate how the proposed optimal strategies from the meta-experiment (k-fold validation) and the hyperparameter optimization method (R-BEESE-C) can foster improved classification accuracy and automatic protections from misinformation.
