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Analog IC Placement Generation via Neural Networks from Unlabeled Data
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ISBN: 9783030500610 Year: 2020 Publisher: Cham Springer International Publishing :Imprint: Springer

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Oracle Digital Assistant
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ISBN: 9781484254226 Year: 2020 Publisher: Berkeley, CA Apress :Imprint: Apress

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Unity game development cookbook : essential for every game
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ISBN: 9781491999158 Year: 2020 Publisher: Sebastopol : O'Reilly,

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Find out how to use the Unity Game Engine to its fullest for both 3D and 2D game development-from the basics to the hottest new tricks in virtual reality. With this unique cookbook, you'll get started in two ways: First, you'll learn about the Unity game engine by following very brief exercises that teach specific features of the software Second, this tutorial-oriented guide provides a collection of snippets that solve common gameplay problems, like determining if a player has completed a lap in a race Using our cookbook format, we pinpoint the problem, set out the solution, and discuss how to solve your problem in the best and most straightforward way possible so you can move onto the next step in the project. Unity Game Development Cookbook is ideal for beginning to intermediate Unity developers. Beginners will get a broad immersion into the Unity development environment, while intermediate developers will learn how to apply the foundational Unity skills they have to solve real game development problems.


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Analog IC Placement Generation via Neural Networks from Unlabeled Data
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ISBN: 9783030500610 Year: 2020 Publisher: Cham Springer International Publishing

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In this book, innovative research using artificial neural networks (ANNs) is conducted to automate the placement task in analog integrated circuit layout design, by creating a generalized model that can generate valid layouts at push-button speed. Further, it exploits ANNs’ generalization and push-button speed prediction (once fully trained) capabilities, and details the optimal description of the input/output data relation. The description developed here is chiefly reflected in two of the system’s characteristics: the shape of the input data and the minimized loss function. In order to address the latter, abstract and segmented descriptions of both the input data and the objective behavior are developed, which allow the model to identify, in newer scenarios, sub-blocks which can be found in the input data. This approach yields device-level descriptions of the input topology that, for each device, focus on describing its relation to every other device in the topology. By means of these descriptions, an unfamiliar overall topology can be broken down into devices that are subject to the same constraints as a device in one of the training topologies. In the experimental results chapter, the trained ANNs are used to produce a variety of valid placement solutions even beyond the scope of the training/validation sets, demonstrating the model’s effectiveness in terms of identifying common components between newer topologies and reutilizing the acquired knowledge. Lastly, the methodology used can readily adapt to the given problem’s context (high label production cost), resulting in an efficient, inexpensive and fast model. .


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Oracle Digital Assistant : A Guide to Enterprise-Grade Chatbots
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ISBN: 9781484254226 Year: 2020 Publisher: Berkeley, CA Apress

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Use this book as an on-ramp to the development of enterprise-grade chatbots and digital assistants. It is based on real-life experience and explains everything you need to know to start building your own digital assistant (DA) using Oracle technologies. You will become familiar with the concepts involved in DA development from both a user experience and technical point of view. You will learn to create DAs using Oracle technologies, including the Oracle Digital Assistant Cloud. In the first part of the book, you will learn the basic principles of the digital assistant (AKA Chatbot) technology. You will be guided through the steps involved in designing a DA, including how to make sure that the user receives a satisfying experience when using the assistant. In the second part of the book, you will learn how to implement the digital assistant that was designed in the first part. You will begin with a basic implementation, later enhancing that implementation with agents, multi-language support, and webviews. The final part of the book adds a deep dive into custom component development with an example around sentiment analysis and speech. You will: Design digital assistants to meet business requirements Implement a great user experience for your digital assistants Translate digital assistant design into real implementations Build multi-language support into your assistants Enhance digital assistants with webviews Create custom components to extend feature sets.


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Deep Neural Evolution
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ISBN: 9789811536854 Year: 2020 Publisher: Singapore Springer Singapore :Imprint: Springer

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Deep Neural Evolution : Deep Learning with Evolutionary Computation
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ISBN: 9789811536854 Year: 2020 Publisher: Singapore Springer Singapore, Imprint: Springer

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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.


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Chinese Computational Linguistics
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ISBN: 9783030630317 Year: 2020 Publisher: Cham Springer International Publishing :Imprint: Springer

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Chinese Computational Linguistics : 19th China National Conference, CCL 2020, Hainan, China, October 30 – November 1, 2020, Proceedings
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ISBN: 9783030630317 Year: 2020 Publisher: Cham Springer International Publishing

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This book constitutes the proceedings of the 19th China National Conference on Computational Linguistics, CCL 2020, held in Hainan, China, in October/November 2020. The 32 full and 2 short papers presented in this volume were carefully reviewed and selected from 99 submissions. They were organized in topical sections named: fundamental theory and methods of computational linguistics; information retrieval, dialogue and question answering; text generation and summarization; knowledge graph and information extraction; machine translation and multilingual information processing; minority language information processing; language resource and evaluation; social computing and sentiment analysis; and NLP applications.


Digital
Computational Linguistics : 16th International Conference of the Pacific Association for Computational Linguistics, PACLING 2019, Hanoi, Vietnam, October 11–13, 2019, Revised Selected Papers
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ISBN: 9789811561689 Year: 2020 Publisher: Singapore Springer Singapore, Imprint: Springer

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This book constitutes the refereed proceedings of the 16th International Conference of the Pacific Association for Computational Linguistics, PACLING 2019, held in Hanoi, Vietnam, in October 2019. The 28 full papers and 14 short papers presented were carefully reviewed and selected from 70 submissions. The papers are organized in topical sections on text summarization; relation and word embedding; machine translation; text classification; web analyzing; question and answering, dialog analyzing; speech and emotion analyzing; parsing and segmentation; information extraction; and grammar error and plagiarism detection.

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