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Author: Matt R. Cole Publisher: BPB Publications ISBN: 9388511018 Category : Computers Languages : en Pages : 392
Book Description
Get hands on with Kelp.Net , MicrosoftÕs latest Deep Learning framework Description Deep Learning with Kelp.Net is the ultimate reference for C# .Net developers who are passionate about deep learning. Readers will learn all the skills necessary to develop powerful, scalable and flexible deep learning models from a fluid and easy to use API. Upon completing the book the reader will have all the tools necessary to add powerful deep learning capabilities to their new or existing applications. Key Features Deep Learning Basics The ultimate Kelp.Net reference guide Develop state of the art deep learning applications C# Deep Learning code Develop advanced deep learning models with minimal code Develop your own advanced Deep Learning models Loading and Saving Deep Learning Models Comprehensive Kelp.Net reference Sample Deep Learning Models and Tests OpenCL Reference Easily add deep learning to your applications Many sample models and tests Intuitive and user friendly What you will learn In-depth knowledge of Kelp.Net How to develop Deep Learning models C# Deep Learning programming Open-Computing Language (OpenCL) Loading and saving Deep Learning models How to develop and use activation functions How to test Deep Learning models Who This Book is For This book targets C# .Net developers who are passionate about deep learning yet want to do so from an easy and intuitive API. Table of Contents Introduction ML/DL Terms and Concepts Deep Instrumentation Kelp.Net Reference Loading and Saving Models Model Testing and Training Sample Deep Learning Tests Creating Your Own Deep Learning Tests Appendix A: Evaluation Metrics Appendix B: OpenCL
Author: Matt R. Cole Publisher: BPB Publications ISBN: 9388511018 Category : Computers Languages : en Pages : 392
Book Description
Get hands on with Kelp.Net , MicrosoftÕs latest Deep Learning framework Description Deep Learning with Kelp.Net is the ultimate reference for C# .Net developers who are passionate about deep learning. Readers will learn all the skills necessary to develop powerful, scalable and flexible deep learning models from a fluid and easy to use API. Upon completing the book the reader will have all the tools necessary to add powerful deep learning capabilities to their new or existing applications. Key Features Deep Learning Basics The ultimate Kelp.Net reference guide Develop state of the art deep learning applications C# Deep Learning code Develop advanced deep learning models with minimal code Develop your own advanced Deep Learning models Loading and Saving Deep Learning Models Comprehensive Kelp.Net reference Sample Deep Learning Models and Tests OpenCL Reference Easily add deep learning to your applications Many sample models and tests Intuitive and user friendly What you will learn In-depth knowledge of Kelp.Net How to develop Deep Learning models C# Deep Learning programming Open-Computing Language (OpenCL) Loading and saving Deep Learning models How to develop and use activation functions How to test Deep Learning models Who This Book is For This book targets C# .Net developers who are passionate about deep learning yet want to do so from an easy and intuitive API. Table of Contents Introduction ML/DL Terms and Concepts Deep Instrumentation Kelp.Net Reference Loading and Saving Models Model Testing and Training Sample Deep Learning Tests Creating Your Own Deep Learning Tests Appendix A: Evaluation Metrics Appendix B: OpenCL
Author: Cole Matt R. Publisher: BPB Publications ISBN: 9389423740 Category : Computers Languages : en Pages : 388
Book Description
Get hands on with Kelp.Net, Microsoft's latest Deep Learning frameworkKey features Deep Learning Basics The ultimate Kelp.Net reference guide Develop state of the art deep learning applications C# deep learning code Develop advanced deep learning models with minimal code Develop your own advanced deep learning models Loading and Saving Deep Learning Models Comprehensive Kelp.Net reference Sample Deep Learning Models and Tests penCL Reference Easily add deep learning to your applications Many sample models and tests Intuitive and user friendly Description Deep Learning with Kelp.Net is the ultimate reference for C# .Net developers who are passionate about deep learning. Readers will learn all the skills necessary to develop powerful, scalable and flexible deep learning models from a fluid and easy to use API. Upon completing the book the reader will have all the tools necessary to add powerful deep learning capabilities to their new or existing applications.What will you learn In-depth knowledge of Kelp.Net How to develop deep learning models C# deep learning programming Open-Computing Language (OpenCL) Loading and saving deep learning models How to develop and use activation functions How to test deep learning modelsWho this book is for This book targets C# .Net developers who are passionate about deep learning yet want to do so from an easy and intuitive API.Table of contents1. Introduction2. ML/DL Terms and Concepts3. Deep Instrumentation4. Kelp.Net Reference5. Loading and Saving Models6. Model Testing and Training7. Sample Deep Learning Tests8. Creating Your Own Deep Learning Tests9. Appendix A: Evaluation Metrics10. Appendix B: OpenCL About the authorMatt R. Cole is a seasoned developer and published author with over 30 years' experience in Microsoft Windows, C, C++, C# and .Net. Matt is the owner of Evolved AI Solutions, a premier provider of advanced Machine Learning/Bio-AI technologies. Matt developed the first enterprise grade MicroService framework written completely in C# and .Net, which is used in production by a major hedge fund in NYC. Matt also developed the first Bio Artificial Intelligence framework which completely integrates mirror and canonical neurons. He continues to push the limits of Machine Learning, Biological Artificial Intelligence, Deep Learning and MicroServices. In his spare time Matt loves to continue his education and contribute to open source efforts such as Kelp.Net. His Website: www.evolvedaisolutions.comHis LinkedIn Profile: https://www.linkedin.com/in/evolvedai/His Blog: https://evolvedaisolutions.com/blog.html
Author: Scott O'Dell Publisher: Houghton Mifflin Harcourt ISBN: 0395069629 Category : Juvenile Fiction Languages : en Pages : 195
Book Description
Far off the coast of California looms a harsh rock known as the island of San Nicholas. Dolphins flash in the blue waters around it, sea otter play in the vast kep beds, and sea elephants loll on the stony beaches. Here, in the early 1800s, according to history, an Indian girl spent eighteen years alone, and this beautifully written novel is her story. It is a romantic adventure filled with drama and heartache, for not only was mere subsistence on so desolate a spot a near miracle, but Karana had to contend with the ferocious pack of wild dogs that had killed her younger brother, constantly guard against the Aleutian sea otter hunters, and maintain a precarious food supply. More than this, it is an adventure of the spirit that will haunt the reader long after the book has been put down. Karana's quiet courage, her Indian self-reliance and acceptance of fate, transform what to many would have been a devastating ordeal into an uplifting experience. From loneliness and terror come strength and serenity in this Newbery Medal-winning classic.
Author: Karthikeyan NG Publisher: Packt Publishing Ltd ISBN: 1789347041 Category : Computers Languages : en Pages : 303
Book Description
Learn to build end-to-end AI apps from scratch for Android and iOS using TensorFlow Lite, CoreML, and PyTorch Key FeaturesBuild practical, real-world AI projects on Android and iOSImplement tasks such as recognizing handwritten digits, sentiment analysis, and moreExplore the core functions of machine learning, deep learning, and mobile visionBook Description We’re witnessing a revolution in Artificial Intelligence, thanks to breakthroughs in deep learning. Mobile Artificial Intelligence Projects empowers you to take part in this revolution by applying Artificial Intelligence (AI) techniques to design applications for natural language processing (NLP), robotics, and computer vision. This book teaches you to harness the power of AI in mobile applications along with learning the core functions of NLP, neural networks, deep learning, and mobile vision. It features a range of projects, covering tasks such as real-estate price prediction, recognizing hand-written digits, predicting car damage, and sentiment analysis. You will learn to utilize NLP and machine learning algorithms to make applications more predictive, proactive, and capable of making autonomous decisions with less human input. In the concluding chapters, you will work with popular libraries, such as TensorFlow Lite, CoreML, and PyTorch across Android and iOS platforms. By the end of this book, you will have developed exciting and more intuitive mobile applications that deliver a customized and more personalized experience to users. What you will learnExplore the concepts and fundamentals of AI, deep learning, and neural networksImplement use cases for machine vision and natural language processingBuild an ML model to predict car damage using TensorFlowDeploy TensorFlow on mobile to convert speech to textImplement GAN to recognize hand-written digitsDevelop end-to-end mobile applications that use AI principlesWork with popular libraries, such as TensorFlow Lite, CoreML, and PyTorchWho this book is for Mobile Artificial Intelligence Projects is for machine learning professionals, deep learning engineers, AI engineers, and software engineers who want to integrate AI technology into mobile-based platforms and applications. Sound knowledge of machine learning and experience with any programming language is all you need to get started with this book.
Author: Matt R. Cole Publisher: Packt Publishing ISBN: 9781788994941 Category : Computers Languages : en Pages : 274
Book Description
Explore supervised and unsupervised learning techniques and add smart features to your applications Key Features Leverage machine learning techniques to build real-world applications Use the Accord.NET machine learning framework for reinforcement learning Implement machine learning techniques using Accord, nuML, and Encog Book Description The necessity for machine learning is everywhere, and most production enterprise applications are written in C# using tools such as Visual Studio, SQL Server, and Microsoft Azur2e. Hands-On Machine Learning with C# uniquely blends together an understanding of various machine learning concepts, techniques of machine learning, and various available machine learning tools through which users can add intelligent features.These tools include image and motion detection, Bayes intuition, and deep learning, to C# .NET applications. Using this book, you will learn to implement supervised and unsupervised learning algorithms and will be better equipped to create excellent predictive models. In addition, you will learn both supervised and unsupervised forms of regression, mainly logistic and linear regression, in depth. Next, you will use the nuML machine learning framework to learn how to create a simple decision tree. In the concluding chapters, you will use the Accord.Net machine learning framework to learn sequence recognition of handwritten numbers using dynamic time warping. We will also cover advanced concepts such as artificial neural networks, autoencoders, and reinforcement learning. By the end of this book, you will have developed a machine learning mindset and will be able to leverage C# tools, techniques, and packages to build smart, predictive, and real-world business applications. What you will learn Learn to parameterize a probabilistic problem Use Naive Bayes to visually plot and analyze data Plot a text-based representation of a decision tree using nuML Use the Accord.NET machine learning framework for associative rule-based learning Develop machine learning algorithms utilizing fuzzy logic Explore support vector machines for image recognition Understand dynamic time warping for sequence recognition Who this book is for Hands-On Machine Learning with C#is forC# .NETdevelopers who work on a range of platforms from .NET and Windows to mobile devices. Basic knowledge of statistics is required.
Author: Radek Silhavy Publisher: Springer Nature ISBN: 3030519651 Category : Technology & Engineering Languages : en Pages : 621
Book Description
This book gathers the refereed proceedings of the Intelligent Algorithms in Software Engineering Section of the 9th Computer Science On-line Conference 2020 (CSOC 2020), held on-line in April 2020. Software engineering research and its applications to intelligent algorithms have now assumed an essential role in computer science research. In this book, modern research methods, together with applications of machine and statistical learning in software engineering research, are presented.
Author: Harold Mooney Publisher: Univ of California Press ISBN: 0520278801 Category : Nature Languages : en Pages : 1008
Book Description
This long-anticipated reference and sourcebook for CaliforniaÕs remarkable ecological abundance provides an integrated assessment of each major ecosystem typeÑits distribution, structure, function, and management. A comprehensive synthesis of our knowledge about this biologically diverse state, Ecosystems of California covers the state from oceans to mountaintops using multiple lenses: past and present, flora and fauna, aquatic and terrestrial, natural and managed. Each chapter evaluates natural processes for a specific ecosystem, describes drivers of change, and discusses how that ecosystem may be altered in the future. This book also explores the drivers of CaliforniaÕs ecological patterns and the history of the stateÕs various ecosystems, outlining how the challenges of climate change and invasive species and opportunities for regulation and stewardship could potentially affect the stateÕs ecosystems. The text explicitly incorporates both human impacts and conservation and restoration efforts and shows how ecosystems support human well-being. Edited by two esteemed ecosystem ecologists and with overviews by leading experts on each ecosystem, this definitive work will be indispensable for natural resource management and conservation professionals as well as for undergraduate or graduate students of CaliforniaÕs environment and curious naturalists.
Author: Jacob P. Kritzer Publisher: Elsevier ISBN: 0080454712 Category : Science Languages : en Pages : 573
Book Description
Technological improvements have greatly increased the ability of marine scientists to collect and analyze data over large spatial scales, and the resultant insights attainable from interpreting those data vastly increase understanding of poplation dynamics, evolution and biogeography. Marine Metapopulations provides a synthesis of existing information and understanding, and frames the most important future directions and issues. - First book to systematically apply metapopulation theory directly to marine systems - Contributions from leading international ecologists and fisheries biologists - Perspectives on a broad array of marine organisms and ecosystems, from coastal estuaries to shallow reefs to deep-sea hydrothermal vents - Critical science for improved management of marine resources - Paves the way for future research on large-scale spatial ecology of marine systems
Author: Matt R. Cole
Author: Matt R. Cole
Author: Cole Matt R.
Author: Don Box
Author: Scott O'Dell
Author: Karthikeyan NG
Author: Matt R. Cole
Author: Radek Silhavy
Author: Harold Mooney
Author: Pierre Gernez
Author: Jacob P. Kritzer