Deep Active Learning

Deep Active Learning PDF
Author: Kayo Matsushita
Publisher: Springer
ISBN: 9811056609
Size: 32.91 MB
Format: PDF, ePub, Mobi
Category : Education
Languages : en
Pages : 226
View: 6555

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Book Description: This is the first book to connect the concepts of active learning and deep learning, and to delineate theory and practice through collaboration between scholars in higher education from three countries (Japan, the United States, and Sweden) as well as different subject areas (education, psychology, learning science, teacher training, dentistry, and business).It is only since the beginning of the twenty-first century that active learning has become key to the shift from teaching to learning in Japanese higher education. However, “active learning” in Japan, as in many other countries, is just an umbrella term for teaching methods that promote students’ active participation, such as group work, discussions, presentations, and so on.What is needed for students is not just active learning but deep active learning. Deep learning focuses on content and quality of learning whereas active learning, especially in Japan, focuses on methods of learning. Deep active learning is placed at the intersection of active learning and deep learning, referring to learning that engages students with the world as an object of learning while interacting with others, and helps the students connect what they are learning with their previous knowledge and experiences as well as their future lives.What curricula, pedagogies, assessments and learning environments facilitate such deep active learning? This book attempts to respond to that question by linking theory with practice.

Evaluation Von Active Learning Zum Trainieren Eines Deep Learning Objektdetektors

Evaluation von Active Learning zum Trainieren eines Deep Learning Objektdetektors PDF
Author: Patrick Schönberger
Publisher:
ISBN:
Size: 69.74 MB
Format: PDF, ePub
Category :
Languages : de
Pages :
View: 943

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Book Description:

Deep Learning In Healthcare

Deep Learning in Healthcare PDF
Author: Yen-Wei Chen
Publisher: Springer Nature
ISBN: 3030326063
Size: 79.79 MB
Format: PDF, Kindle
Category : Technology & Engineering
Languages : en
Pages : 218
View: 4634

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Book Description: This book provides a comprehensive overview of deep learning (DL) in medical and healthcare applications, including the fundamentals and current advances in medical image analysis, state-of-the-art DL methods for medical image analysis and real-world, deep learning-based clinical computer-aided diagnosis systems. Deep learning (DL) is one of the key techniques of artificial intelligence (AI) and today plays an important role in numerous academic and industrial areas. DL involves using a neural network with many layers (deep structure) between input and output, and its main advantage of is that it can automatically learn data-driven, highly representative and hierarchical features and perform feature extraction and classification on one network. DL can be used to model or simulate an intelligent system or process using annotated training data. Recently, DL has become widely used in medical applications, such as anatomic modelling, tumour detection, disease classification, computer-aided diagnosis and surgical planning. This book is intended for computer science and engineering students and researchers, medical professionals and anyone interested using DL techniques.

Deep Learning Applications

Deep Learning Applications PDF
Author: M. Arif Wani
Publisher: Springer Nature
ISBN: 9811518165
Size: 73.36 MB
Format: PDF, ePub
Category : Technology & Engineering
Languages : en
Pages : 178
View: 3319

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Book Description: This book presents a compilation of selected papers from the 17th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2018), focusing on use of deep learning technology in application like game playing, medical applications, video analytics, regression/classification, object detection/recognition and robotic control in industrial environments. It highlights novel ways of using deep neural networks to solve real-world problems, and also offers insights into deep learning architectures and algorithms, making it an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.

Medical Image Computing And Computer Assisted Intervention Miccai 2020

Medical Image Computing and Computer Assisted Intervention     MICCAI 2020 PDF
Author: Anne L. Martel
Publisher: Springer Nature
ISBN: 3030597105
Size: 69.18 MB
Format: PDF, ePub, Mobi
Category :
Languages : en
Pages :
View: 6051

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Book Description:

Artificial Neural Networks And Machine Learning Icann 2018

Artificial Neural Networks and Machine Learning     ICANN 2018 PDF
Author: Věra Kůrková
Publisher: Springer
ISBN: 3030014215
Size: 80.64 MB
Format: PDF, Docs
Category : Computers
Languages : en
Pages : 632
View: 6401

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Book Description: This three-volume set LNCS 11139-11141 constitutes the refereed proceedings of the 27th International Conference on Artificial Neural Networks, ICANN 2018, held in Rhodes, Greece, in October 2018. The 139 full and 28 short papers as well as 41 full poster papers and 41 short poster papers presented in these volumes was carefully reviewed and selected from total of 360 submissions. They are related to the following thematic topics: AI and Bioinformatics, Bayesian and Echo State Networks, Brain Inspired Computing, Chaotic Complex Models, Clustering, Mining, Exploratory Analysis, Coding Architectures, Complex Firing Patterns, Convolutional Neural Networks, Deep Learning (DL), DL in Real Time Systems, DL and Big Data Analytics, DL and Big Data, DL and Forensics, DL and Cybersecurity, DL and Social Networks, Evolving Systems – Optimization, Extreme Learning Machines, From Neurons to Neuromorphism, From Sensation to Perception, From Single Neurons to Networks, Fuzzy Modeling, Hierarchical ANN, Inference and Recognition, Information and Optimization, Interacting with The Brain, Machine Learning (ML), ML for Bio Medical systems, ML and Video-Image Processing, ML and Forensics, ML and Cybersecurity, ML and Social Media, ML in Engineering, Movement and Motion Detection, Multilayer Perceptrons and Kernel Networks, Natural Language, Object and Face Recognition, Recurrent Neural Networks and Reservoir Computing, Reinforcement Learning, Reservoir Computing, Self-Organizing Maps, Spiking Dynamics/Spiking ANN, Support Vector Machines, Swarm Intelligence and Decision-Making, Text Mining, Theoretical Neural Computation, Time Series and Forecasting, Training and Learning.

Secure And Privacy Aware Machine Learning

Secure and Privacy Aware Machine Learning PDF
Author: Xuhui Chen
Publisher:
ISBN:
Size: 13.51 MB
Format: PDF, ePub
Category : Cloud computing
Languages : en
Pages : 112
View: 4654

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Book Description: With the onset of the big data era, designing efficient and secure machine learning frameworks to analyze large-scale data is in dire need. This dissertation considers two machine learning paradigms, the centralized learning scenario, where we study the secure outsourcing problem in cloud computing, and the distributed learning scenario, where we explore blockchain techniques to remove the untrusted central server to solve the security problems. In the centralized machine learning paradigm, inference using deep neural networks (DNNs) may be outsourced to the cloud due to its high computational cost, which, however, raises security concerns. Particularly, the data involved in DNNs can be highly sensitive, such as in medical, financial, commercial applications, and hence should be kept private. Besides, DNN models owned by research institutions or commercial companies are their valuable intellectual properties and can contain proprietary information, which should be protected as well. Moreover, an untrusted cloud service provider may return inaccurate and even erroneous computing results. To address above issues, we propose a secure outsourcing framework for deep neural network inference called SecureNets, which can preserve both a user's data privacy and his/her neural network model privacy, and also verify the computation results returned by the cloud. Specifically, we employ a secure matrix transformation scheme in SecureNets to avoid privacy leakage of the data and the model. Meanwhile, we propose a verification method that can efficiently verify the correctness of cloud computing results. Our simulation results on four- and five-layer deep neural networks demonstrate that SecureNets can reduce the processing runtime by up to 64%. Compared with CryptoNets, one of the previous schemes, SecureNets can increase the throughput by 104.45% while reducing the data transmission size by 69.78% per instance. We further improve the privacy level in SecureNets and implement it in a practical scenario. The Internet of Things (IoT) emerge as a ubiquitous information collection and processing paradigm that can potentially exploit the collected massive data for various applications like smart health, smart transportation, cyber-physical systems, by taking advantage of machine learning technologies. However, these data are usually unlabeled, while the labeling process is usually both time and effort consuming. Active learning is one approach to reduce the data labeling cost by only sending the most informative samples to experts for labeling. In this process, two most computation-intensive operations, i.e., sample selection and learning model training, hinder the use of active learning on resource-limited IoT devices. To address this issue, we develop a secure outsourcing framework for deep active learning (SEDAL) by considering a general active learning framework with a deep neural network (DNN) learning model. The improved SecureNets is adopted in the model inferences in sample selection and DNN learning phases. Compared with traditional homomorphic encryption based secure outsourcing schemes, our scheme reduces the computational complexity at the user from O(n^3) to O(n^2). To evaluate the performance of the proposed system, we implement it on an android phone and Amazon AWS cloud for an arrhythmia diagnosis application. Experiment results show that the proposed scheme can obtain a well-trained classifier using fewer queried samples, and the computation time and communication overhead are acceptable and practical. Besides the centralized learning paradigms, in practice, data can also be generated by multiple parties and stored in a geographically distributed manner, which spurs the study of distributed machine learning. Traditional master-worker type of distributed machine learning algorithms assumes a trusted central server and focuses on the privacy issue in linear learning models, while privacy in nonlinear learning models and security issues are not well studied. To address these issues, in this work, we explore the blockchain technique to propose a decentralized privacy-preserving and secure machine learning system, called LearningChain, by considering a general (linear or nonlinear) learning model and without a trusted central server. Specifically, we design a decentralized Stochastic Gradient Descent (SGD) algorithm to learn a general predictive model over the blockchain. In decentralized SGD, we develop differential privacy based schemes to protect each party's data privacy, and propose an l-nearest aggregation algorithm to protect the system from potential Byzantine attacks. We also conduct theoretical analysis on the privacy and security of the proposed LearningChain. Finally, we implement LearningChain and demonstrate its efficiency and effectiveness through extensive experiments.

200 Active Learning Strategies And Projects For Engaging Students Multiple Intelligences

200  Active Learning Strategies and Projects for Engaging Students   Multiple Intelligences PDF
Author: James Bellanca
Publisher: Corwin Press
ISBN: 1412968844
Size: 70.99 MB
Format: PDF, Docs
Category : Education
Languages : en
Pages : 346
View: 7096

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Book Description: Organized by intelligence area, this resource provides more than 200 new and enhanced strategies to help teachers increase students' motivation and transform them into active learners.

Active Learning Online

Active Learning Online PDF
Author: Stephen Kosslyn
Publisher:
ISBN: 9781735810706
Size: 66.19 MB
Format: PDF
Category :
Languages : en
Pages : 120
View: 1068

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Book Description: Inspired by the recent proliferation of online courses necessitated by the COVID 19 pandemic, researcher and educational innovator Stephen M. Kosslyn offers instructors and course designers (as well as school administrations and teacher-education students) a treasure trove of active learning principles and activities for implementation in online, hybrid and in-person courses. Whether your course is synchronous (e.g., live with Zoom) or asynchronous (e.g., using video content on Canvas), this book will inject active learning into existing courses or into courses designed from scratch. In both cases, active learning will make the courses not only more interesting but also more effective; student engagement will increase, learning outcomes will be reached, and general teaching and learning experiences will be enriched.