Automated Machine Learning PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Automated Machine Learning PDF full book. Access full book title Automated Machine Learning by Frank Hutter. Download full books in PDF and EPUB format.
Author: Frank Hutter
Publisher: Springer
ISBN: 3030053180
Category : Computers
Languages : en
Pages : 223
Get Book Here
Book Description
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.
Author: Frank Hutter
Publisher: Springer
ISBN: 3030053180
Category : Computers
Languages : en
Pages : 223
Get Book Here
Book Description
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.
Author: Yanan Sun
Publisher: Springer Nature
ISBN: 3031168682
Category : Technology & Engineering
Languages : en
Pages : 335
Get Book Here
Book Description
This book systematically narrates the fundamentals, methods, and recent advances of evolutionary deep neural architecture search chapter by chapter. This will provide the target readers with sufficient details learning from scratch. In particular, the method parts are devoted to the architecture search of unsupervised and supervised deep neural networks. The people, who would like to use deep neural networks but have no/limited expertise in manually designing the optimal deep architectures, will be the main audience. This may include the researchers who focus on developing novel evolutionary deep architecture search methods for general tasks, the students who would like to study the knowledge related to evolutionary deep neural architecture search and perform related research in the future, and the practitioners from the fields of computer vision, natural language processing, and others where the deep neural networks have been successfully and largely used in their respective fields.
Author: Jason Zhi Liang
Publisher:
ISBN:
Category :
Languages : en
Pages : 356
Get Book Here
Book Description
Deep neural networks (DNNs) have produced state-of-the-art results in many benchmarks and problem domains. However, the success of DNNs depends on the proper configuration of its architecture and hyperparameters. DNNs are often not used to their full potential because it is difficult to determine what architectures and hyperparameters should be used. While several approaches have been proposed, computational complexity of searching large design spaces makes them impractical for large modern DNNs. This dissertation introduces an efficient evolutionary algorithm (EA) for simultaneous optimization of DNN architecture and hyperparameters. It builds upon extensive past research of evolutionary optimization of neural network structure. Various improvements to the core algorithm are introduced, including: (1) discovering DNN architectures of arbitrary complexity; (1) generating modular, repetitive modules commonly seen in state-of-the-art DNNs; (3) extending to the multitask learning and multiobjective optimization domains; (4) maximizing performance and reducing wasted computation through asynchronous evaluations. Experimental results in image classification, image captioning, and multialphabet character recognition show that the approach is able to evolve networks that are competitive with or even exceed hand-designed networks. Thus, the method enables an automated and streamlined process to optimize DNN architectures for a given problem and can be widely applied to solve harder tasks
Author: Hitoshi Iba
Publisher: Springer Nature
ISBN: 9811536856
Category : Computers
Languages : en
Pages : 437
Get Book Here
Book Description
This book delivers the state of the art in deep learning (DL) methods hybridized with evolutionary computation (EC). Over the last decade, DL has dramatically reformed many domains: computer vision, speech recognition, healthcare, and automatic game playing, to mention only a few. All DL models, using different architectures and algorithms, utilize multiple processing layers for extracting a hierarchy of abstractions of data. Their remarkable successes notwithstanding, these powerful models are facing many challenges, and this book presents the collaborative efforts by researchers in EC to solve some of the problems in DL. EC comprises optimization techniques that are useful when problems are complex or poorly understood, or insufficient information about the problem domain is available. This family of algorithms has proven effective in solving problems with challenging characteristics such as non-convexity, non-linearity, noise, and irregularity, which dampen the performance of most classic optimization schemes. Furthermore, EC has been extensively and successfully applied in artificial neural network (ANN) research —from parameter estimation to structure optimization. Consequently, EC researchers are enthusiastic about applying their arsenal for the design and optimization of deep neural networks (DNN). This book brings together the recent progress in DL research where the focus is particularly on three sub-domains that integrate EC with DL: (1) EC for hyper-parameter optimization in DNN; (2) EC for DNN architecture design; and (3) Deep neuroevolution. The book also presents interesting applications of DL with EC in real-world problems, e.g., malware classification and object detection. Additionally, it covers recent applications of EC in DL, e.g. generative adversarial networks (GAN) training and adversarial attacks. The book aims to prompt and facilitate the research in DL with EC both in theory and in practice.
Author: Maria Gabriela Baldeón Calisto
Publisher:
ISBN:
Category : Diagnostic imaging
Languages : en
Pages : 120
Get Book Here
Book Description
Deep neural networks have become very successful at solving many complex tasks such as image classification, image segmentation, and speech recognition. These models are composed of multiple layers that have the capacity to learn increasingly higher-level features, without prior handcrafted specifications. However, the success of a deep neural network relies on finding the proper configuration for the task in hand. Given the vast number of hyperparameters and the massive search space, manually designing or fine-tuning deep learning architectures requires extensive knowledge, time, and computational resources. There is a growing interest in developing methods that automatically design a neural network ́s architecture, known as neural architecture search (NAS). NAS is usually modeled as a single-objective optimization problem where the aim is to find an architecture that maximizes the prediction ́s accuracy. However, most deep learning applications require accurate as well as efficient architectures to reduce memory consumption and enable their use in computationally-limited environments. This has led to the need to model NAS as a multiple objective problem that optimizes both the predictive performance and efficiency of the network. Furthermore, most NAS framework have focused on either optimizing the micro-structure (structure of the basic cell), or macro-structure (optimal number of cells and their connection) of the architecture. Consequently, manual engineering is required to find the topology of the non-optimized structure. Although NAS has demonstrated great potential in automatically designing an architecture, it remains a computationally expensive and time-consuming process because it requires training and evaluating many potential configurations. Recent work has focused on improving the search time of NAS algorithms, but most techniques have been developed and applied only for single-objective optimization problems. Given that optimizing multiple objectives has a higher complexity and requires more iterations to approximate the Pareto Front, it is critical to investigate algorithms that decrease the search time of multiobjective NAS. One critical application of deep learning is medical image segmentation. Segmentation of medical images provides valuable information for various critical tasks such as analyzing anatomical structures, monitoring disease progression, and predicting patient outcomes. Nonetheless, achieving accurate segmentation is challenging due to the inherent variability in appearance, shape, and location of the region of interest (ROI) between patients and the differences in imagining equipment and acquisition protocols. Therefore, neural networks are usually tailored to a specific application, anatomical region, and image modality. Moreover, medical image data is often volumetric requiring expensive 3D operations that result in large and complex architectures. Hence, training and deploying them requires considerable storage and memory bandwidth that makes them less suitable for clinical applications. To overcome these challenges, the main goal of this research is to automatically design accurate and efficient deep neural networks using multiobjective optimization algorithms for medical image segmentation. The proposed research consists of three major objectives: (1) to design a deep neural network that uses a multiobjective evolutionary based algorithm to automatically adapt to different medical image datasets while minimizing the model’s size; (2) to design a self-adaptive 2D-3D Fully Convolutional network (FCN) ensemble that incorporates volumetric information and optimizes both the performance and the size of the architecture; and (3) to design an efficient multiobjective neural architecture search framework that decreases the search time while simultaneously optimizing the micro- and macro-structure of the neural architecture. For the first objective, a multiobjective adaptive convolutional neural network named AdaResU-Net is presented for 2D medical image segmentation. The proposed AdaResU-Net is comprised of a fixed architecture and a learning framework that adjusts the hyperparameters to a particular training dataset using a multiobjective evolutionary based algorithm (MEA algorithm). The MEA algorithm evolves the AdaResU-Net network to optimize both the segmentation accuracy and model size. In the second objective, a self-adaptive ensemble of 2D-3D FCN named AdaEn-Net is proposed for 3D medical image segmentation. The AdaEn-Net is comprised of a 2D FCN that extracts intra-slice and long-range 2D context, and a 3D FCN architecture that exploits inter-slice and volumetric information. The 2D and 3D FCN architectures are automatically fitted for a specific medical image segmentation task by simultaneously optimizing the expected segmentation error and size of the network using the MEA algorithm. Finally, for the third objective, an efficient multiobjective neural architecture search framework named EMONAS is presented for 3D medical image segmentation. EMONAS has two main components, a novel search space that includes the hyperparameters that define the micro- and macro-structure of the architecture, and a Surrogate-assisted multiobjective evolutionary based algorithm (SaMEA algorithm) that efficiently searches for the best hyperparameter values using a Random Forest surrogate and guiding selection probabilities. The broader impact of the proposed research is as follows: (1) automating the design of deep neural networks’ architecture and hyperparameters to improve the performance and efficiency of the models; and (2) increase the accessibility of deep learning to a broader range of organizations and people by reducing the need of expert knowledge and GPU time when automatically designing deep neural networks. In the medical area, the proposed models aim to improve the automatic extraction of data from medical images to potentially enhance diagnosis, treatment planning and survival prediction of various diseases such as cardiac disease and prostate cancer. Although the proposed techniques are applied to medical image segmentation tasks, they can also be implemented in other applications where accurate and resource-efficient deep neural networks are needed such as autonomous navigation, augmented reality and internet-of-things.
Author: Michael Lanham
Publisher: Simon and Schuster
ISBN: 1617299529
Category : Computers
Languages : en
Pages : 358
Get Book Here
Book Description
Discover one-of-a-kind AI strategies never before seen outside of academic papers! Learn how the principles of evolutionary computation overcome deep learning’s common pitfalls and deliver adaptable model upgrades without constant manual adjustment. Evolutionary Deep Learning is a guide to improving your deep learning models with AutoML enhancements based on the principles of biological evolution. This exciting new approach utilizes lesser- known AI approaches to boost performance without hours of data annotation or model hyperparameter tuning. Google Colab notebooks make it easy to experiment and play around with each exciting example. By the time you’ve finished reading Evolutionary Deep Learning, you’ll be ready to build deep learning models as self-sufficient systems you can efficiently adapt to changing requirements. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
Author: Yaochu Jin
Publisher: Springer Nature
ISBN: 3030746402
Category : Computers
Languages : en
Pages : 393
Get Book Here
Book Description
Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques. New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available. This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms. Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included.
Author: S. RAJASEKARAN
Publisher: PHI Learning Pvt. Ltd.
ISBN: 812035334X
Category : Computers
Languages : en
Pages : 574
Get Book Here
Book Description
The second edition of this book provides a comprehensive introduction to a consortium of technologies underlying soft computing, an evolving branch of computational intelligence, which in recent years, has turned synonymous to it. The constituent technologies discussed comprise neural network (NN), fuzzy system (FS), evolutionary algorithm (EA), and a number of hybrid systems, which include classes such as neuro-fuzzy, evolutionary-fuzzy, and neuro-evolutionary systems. The hybridization of the technologies is demonstrated on architectures such as fuzzy backpropagation network (NN-FS hybrid), genetic algorithm-based backpropagation network (NN-EA hybrid), simplified fuzzy ARTMAP (NN-FS hybrid), fuzzy associative memory (NN-FS hybrid), fuzzy logic controlled genetic algorithm (EA-FS hybrid) and evolutionary extreme learning machine (NN-EA hybrid) Every architecture has been discussed in detail through illustrative examples and applications. The algorithms have been presented in pseudo-code with a step-by-step illustration of the same in problems. The applications, demonstrative of the potential of the architectures, have been chosen from diverse disciplines of science and engineering. This book, with a wealth of information that is clearly presented and illustrated by many examples and applications, is designed for use as a text for the courses in soft computing at both the senior undergraduate and first-year postgraduate levels of computer science and engineering. It should also be of interest to researchers and technologists desirous of applying soft computing technologies to their respective fields of work.
Author: Haiqin Yang
Publisher: Springer Nature
ISBN: 3030638367
Category : Computers
Languages : en
Pages : 660
Get Book Here
Book Description
The three-volume set of LNCS 12532, 12533, and 12534 constitutes the proceedings of the 27th International Conference on Neural Information Processing, ICONIP 2020, held in Bangkok, Thailand, in November 2020. Due to COVID-19 pandemic the conference was held virtually. The 187 full papers presented were carefully reviewed and selected from 618 submissions. The papers address the emerging topics of theoretical research, empirical studies, and applications of neural information processing techniques across different domains. The third volume, LNCS 12534, is organized in topical sections on biomedical information; neural data analysis; neural network models; recommender systems; time series analysis.
Author: Kohei Arai
Publisher: Springer Nature
ISBN: 3030801195
Category : Technology & Engineering
Languages : en
Pages : 1184
Get Book Here
Book Description
This book is a comprehensive collection of chapters focusing on the core areas of computing and their further applications in the real world. Each chapter is a paper presented at the Computing Conference 2021 held on 15-16 July 2021. Computing 2021 attracted a total of 638 submissions which underwent a double-blind peer review process. Of those 638 submissions, 235 submissions have been selected to be included in this book. The goal of this conference is to give a platform to researchers with fundamental contributions and to be a premier venue for academic and industry practitioners to share new ideas and development experiences. We hope that readers find this volume interesting and valuable as it provides the state-of-the-art intelligent methods and techniques for solving real-world problems. We also expect that the conference and its publications is a trigger for further related research and technology improvements in this important subject.
