Автор: Carneiro Название: Deep Learning and Data Labeling for Medical Applications ISBN: 3319469754 ISBN-13(EAN): 9783319469751 Издательство: Springer Рейтинг: Цена: 6988.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the refereed proceedings of two workshops held at the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016, and the Second International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016. The 28 revised regular papers presented in this book were carefully reviewed and selected from a total of 52 submissions. The 7 papers selected for LABELS deal with topics from the following fields: crowd-sourcing methods; active learning; transfer learning; semi-supervised learning; and modeling of label uncertainty.
The 21 papers selected for DLMIA span a wide range of topics such as image description; medical imaging-based diagnosis; medical signal-based diagnosis; medical image reconstruction and model selection using deep learning techniques; meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures; and applications based on deep learning techniques.
Автор: Li Deng; Yang Liu Название: Deep Learning in Natural Language Processing ISBN: 9811052085 ISBN-13(EAN): 9789811052088 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A glossary of technical terms and commonly used acronyms in the intersection of deep learning and NLP is also provided. The book appeals to advanced undergraduate and graduate students, post-doctoral researchers, lecturers and industrial researchers, as well as anyone interested in deep learning and natural language processing.
This book constitutes the refereed joint proceedings of the Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017, and the 6th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Quebec City, QC, Canada, in September 2017.
The 38 full papers presented at DLMIA 2017 and the 5 full papers presented at ML-CDS 2017 were carefully reviewed and selected. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.
Автор: Taweh Beysolow II Название: Introduction to Deep Learning Using R ISBN: 1484227336 ISBN-13(EAN): 9781484227336 Издательство: Springer Рейтинг: Цена: 5309.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Understand deep learning, the nuances of its different models, and where these models can be applied.
The abundance of data and demand for superior products/services have driven the development of advanced computer science techniques, among them image and speech recognition. Introduction to Deep Learning Using R provides a theoretical and practical understanding of the models that perform these tasks by building upon the fundamentals of data science through machine learning and deep learning. This step-by-step guide will help you understand the disciplines so that you can apply the methodology in a variety of contexts. All examples are taught in the R statistical language, allowing students and professionals to implement these techniques using open source tools.What You Will Learn:
Understand the intuition and mathematics that power deep learning models
Utilize various algorithms using the R programming language and its packages
Use best practices for experimental design and variable selection
Practice the methodology to approach and effectively solve problems as a data scientist
Evaluate the effectiveness of algorithmic solutions and enhance their predictive power
Who this book is for: Students, researchers, and data scientists who are familiar with programming using R. This book also is also of use for those who wish to learn how to appropriately deploy these algorithms in applications where they would be most useful.
Описание: This book proposes complex hierarchical deep architectures (HDA) for predicting bankruptcy, a topical issue for business and corporate institutions that in the past has been tackled using statistical, market-based and machine-intelligence prediction models.
Автор: Nikhil Ketkar Название: Deep Learning with Python ISBN: 1484227654 ISBN-13(EAN): 9781484227657 Издательство: Springer Рейтинг: Цена: 6288.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Chapter 1: An intuitive look at the fundamentals of deep learning based on practical applicationsChapter 2: A survey of the current state-of-the-art implementations of libraries, tools and packages for deep learning and the case for the Python ecosystemChapter 3: A detailed look at Keras [1], which is a high level framework for deep learning suitable for beginners to understand and experiment with deep learningChapter 4: A detailed look at Theano [2], which is a low level framework for implementing architectures and algorithms in deep learning from scratchChapter 5: A detailed look at Caffe [3], which is highly optimized framework for implementing some of the most popular deep learning architectures (mainly computer vision)Chapter 6: A brief introduction to GPUs and why they are a game changer for Deep LearningChapter 7: A brief introduction to Automatic DifferentiationChapter 8: A brief introduction to Backpropagation and Stochastic Gradient DescentChapter 9: A survey of Deep Learning ArchitecturesChapter 10: Advice on running large scale experiments in deep learning and taking models to productionChapter 11: Introduction to TensorflowChapter 12: Introduction to PyTorchChapter 13: Regularization TechniquesChapter 14: Training Deep Leaning Models
Автор: Jean-Pierre Briot; Ga?tan Hadjeres; Fran?ois Pache Название: Deep Learning Techniques for Music Generation ISBN: 3319701622 ISBN-13(EAN): 9783319701622 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is a survey and analysis of how deep learning can be used to generate musical content. The authors offer a comprehensive presentation of the foundations of deep learning techniques for music generation. They also develop a conceptual framework used to classify and analyze various types of architecture, encoding models, generation strategies, and ways to control the generation. The five dimensions of this framework are: objective (the kind of musical content to be generated, e.g., melody, accompaniment); representation (the musical elements to be considered and how to encode them, e.g., chord, silence, piano roll, one-hot encoding); architecture (the structure organizing neurons, their connexions, and the flow of their activations, e.g., feedforward, recurrent, variational autoencoder); challenge (the desired properties and issues, e.g., variability, incrementality, adaptability); and strategy (the way to model and control the process of generation, e.g., single-step feedforward, iterative feedforward, decoder feedforward, sampling). To illustrate the possible design decisions and to allow comparison and correlation analysis they analyze and classify more than 40 systems, and they discuss important open challenges such as interactivity, originality, and structure. The authors have extensive knowledge and experience in all related research, technical, performance, and business aspects. The book is suitable for students, practitioners, and researchers in the artificial intelligence, machine learning, and music creation domains. The reader does not require any prior knowledge about artificial neural networks, deep learning, or computer music. The text is fully supported with a comprehensive table of acronyms, bibliography, glossary, and index, and supplementary material is available from the authors' website.
Автор: Foy Brian D., Phoenix Tom, Schwartz Randal L. Название: Learning Perl: Making Easy Things Easy and Hard Things Possible ISBN: 1491954329 ISBN-13(EAN): 9781491954324 Издательство: Wiley Рейтинг: Цена: 5067.00 р. Наличие на складе: Поставка под заказ.
Описание: Popularly known as "the Llama," Learning Perl is the book most programmers rely on to get started with this versatile language. The bestselling Perl tutorial since it was first published in 1993, this sixth edition includes recent changes to the language up to Perl 5.24.
Автор: Giovanni Pezzulo; Martin V. Butz; Olivier Sigaud; Название: Anticipatory Behavior in Adaptive Learning Systems ISBN: 3642025641 ISBN-13(EAN): 9783642025648 Издательство: Springer Рейтинг: Цена: 14365.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the post-workshop proceedings of the 4th International Workshop on Anticipatory Behavior in Adaptive Learning Systems, ABiALS 2008, held in Munich, in June 2008. The introductory chapter provides an overview of the contributions.
Автор: Wray Buntine; Marko Grobelnik; Dunja Mladenic; Joh Название: Machine Learning and Knowledge Discovery in Databases ISBN: 3642041795 ISBN-13(EAN): 9783642041792 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2009, held in Bled, Slovenia, in September 2009.
Автор: Sio-Iong Ao; Burghard B. Rieger; Mahyar Amouzegar Название: Machine Learning and Systems Engineering ISBN: 9400733747 ISBN-13(EAN): 9789400733749 Издательство: Springer Рейтинг: Цена: 28732.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A large international conference on Advances in Machine Learning and Systems Engineering was held in UC Berkeley, California, USA, October 20-22, 2009, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2009).
