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Author: Antonio Chagoury Publisher: John Wiley & Sons ISBN: 1118035445 Category : Computers Languages : en Pages : 37
Book Description
This Wrox Blox will give you a high-level overview of the core Membership Provider and its default implementation, (ASP.NET Membership), and demonstrate how to build and configure your own custom provider. The Provider Model is a design pattern introduced in .NET to provide a simple way to extend API functionality. DotNetNuke uses this architecture to allow some of its core functionality to be replaced without modifying core code. While this Wrox Blox describes how to develop a custom DotNetNuke Membership Provider, it also provides some general information about the .NET Framework’s (2.0 and above) Provider Model, the ASP.NET Membership Provider included in the System.Web.Security namespace, and how they relate to DotNetNuke’s core framework. It also discusses reasons to consider writing a custom provider and gives some guidance as to when doing so is recommended and when it may not be a good choice. Because this is an advanced DotNetNuke development topic, readers should already know how to install the source code version of DotNetNuke on your development environment. Therefore, this Wrox Blox does not provide a step-by-step guide on how to do that. If readers need help in setting up a DotNetNuke development environment, visit www.dotnetnuke.com and click on the documentation or forum areas. All code samples accompanying this Wrox Blox are written in VB.NET, although a C# translation of the same code will yield the same functional results. Table of Contents The Provider Model 1 ASP.NET Membership Provider and DotNetNuke 2 Why Build a Custom Membership Provider? 5 Building the Custom Membership Provider 6 Setting Up DotNetNuke 6 Setting Up the Sample CRM Database 7 Putting It All Together 13 Wrapping Up 21 About Antonio Chagoury 22
Author: Antonio Chagoury Publisher: John Wiley & Sons ISBN: 1118035445 Category : Computers Languages : en Pages : 37
Book Description
This Wrox Blox will give you a high-level overview of the core Membership Provider and its default implementation, (ASP.NET Membership), and demonstrate how to build and configure your own custom provider. The Provider Model is a design pattern introduced in .NET to provide a simple way to extend API functionality. DotNetNuke uses this architecture to allow some of its core functionality to be replaced without modifying core code. While this Wrox Blox describes how to develop a custom DotNetNuke Membership Provider, it also provides some general information about the .NET Framework’s (2.0 and above) Provider Model, the ASP.NET Membership Provider included in the System.Web.Security namespace, and how they relate to DotNetNuke’s core framework. It also discusses reasons to consider writing a custom provider and gives some guidance as to when doing so is recommended and when it may not be a good choice. Because this is an advanced DotNetNuke development topic, readers should already know how to install the source code version of DotNetNuke on your development environment. Therefore, this Wrox Blox does not provide a step-by-step guide on how to do that. If readers need help in setting up a DotNetNuke development environment, visit www.dotnetnuke.com and click on the documentation or forum areas. All code samples accompanying this Wrox Blox are written in VB.NET, although a C# translation of the same code will yield the same functional results. Table of Contents The Provider Model 1 ASP.NET Membership Provider and DotNetNuke 2 Why Build a Custom Membership Provider? 5 Building the Custom Membership Provider 6 Setting Up DotNetNuke 6 Setting Up the Sample CRM Database 7 Putting It All Together 13 Wrapping Up 21 About Antonio Chagoury 22
Author: Michael Collier Publisher: Microsoft Press ISBN: 0735697302 Category : Computers Languages : en Pages : 400
Book Description
Microsoft Azure Essentials from Microsoft Press is a series of free ebooks designed to help you advance your technical skills with Microsoft Azure. The first ebook in the series, Microsoft Azure Essentials: Fundamentals of Azure, introduces developers and IT professionals to the wide range of capabilities in Azure. The authors - both Microsoft MVPs in Azure - present both conceptual and how-to content for key areas, including: Azure Websites and Azure Cloud Services Azure Virtual Machines Azure Storage Azure Virtual Networks Databases Azure Active Directory Management tools Business scenarios Watch Microsoft Press’s blog and Twitter (@MicrosoftPress) to learn about other free ebooks in the “Microsoft Azure Essentials” series.
Author: Vivienne Sze Publisher: Springer Nature ISBN: 3031017668 Category : Technology & Engineering Languages : en Pages : 254
Book Description
This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.
Author: Luca Pietro Giovanni Antiga Publisher: Simon and Schuster ISBN: 1638354073 Category : Computers Languages : en Pages : 518
Book Description
“We finally have the definitive treatise on PyTorch! It covers the basics and abstractions in great detail. I hope this book becomes your extended reference document.” —Soumith Chintala, co-creator of PyTorch Key Features Written by PyTorch’s creator and key contributors Develop deep learning models in a familiar Pythonic way Use PyTorch to build an image classifier for cancer detection Diagnose problems with your neural network and improve training with data augmentation Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About The Book Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands. Instantly familiar to anyone who knows Python data tools like NumPy and Scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. It’s great for building quick models, and it scales smoothly from laptop to enterprise. Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch. After covering the basics, you’ll learn best practices for the entire deep learning pipeline, tackling advanced projects as your PyTorch skills become more sophisticated. All code samples are easy to explore in downloadable Jupyter notebooks. What You Will Learn Understanding deep learning data structures such as tensors and neural networks Best practices for the PyTorch Tensor API, loading data in Python, and visualizing results Implementing modules and loss functions Utilizing pretrained models from PyTorch Hub Methods for training networks with limited inputs Sifting through unreliable results to diagnose and fix problems in your neural network Improve your results with augmented data, better model architecture, and fine tuning This Book Is Written For For Python programmers with an interest in machine learning. No experience with PyTorch or other deep learning frameworks is required. About The Authors Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software. Luca Antiga is co-founder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch. Thomas Viehmann is a Machine Learning and PyTorch speciality trainer and consultant based in Munich, Germany and a PyTorch core developer. Table of Contents PART 1 - CORE PYTORCH 1 Introducing deep learning and the PyTorch Library 2 Pretrained networks 3 It starts with a tensor 4 Real-world data representation using tensors 5 The mechanics of learning 6 Using a neural network to fit the data 7 Telling birds from airplanes: Learning from images 8 Using convolutions to generalize PART 2 - LEARNING FROM IMAGES IN THE REAL WORLD: EARLY DETECTION OF LUNG CANCER 9 Using PyTorch to fight cancer 10 Combining data sources into a unified dataset 11 Training a classification model to detect suspected tumors 12 Improving training with metrics and augmentation 13 Using segmentation to find suspected nodules 14 End-to-end nodule analysis, and where to go next PART 3 - DEPLOYMENT 15 Deploying to production
Author: Ashish Ranjan Jha Publisher: Packt Publishing Ltd ISBN: 1789616409 Category : Computers Languages : en Pages : 450
Book Description
Master advanced techniques and algorithms for deep learning with PyTorch using real-world examples Key Features Understand how to use PyTorch 1.x to build advanced neural network models Learn to perform a wide range of tasks by implementing deep learning algorithms and techniques Gain expertise in domains such as computer vision, NLP, Deep RL, Explainable AI, and much more Book DescriptionDeep learning is driving the AI revolution, and PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch book will help you uncover expert techniques to get the most out of your data and build complex neural network models. The book starts with a quick overview of PyTorch and explores using convolutional neural network (CNN) architectures for image classification. You'll then work with recurrent neural network (RNN) architectures and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation using generative models and explore the world of generative adversarial networks (GANs). You'll not only build and train your own deep reinforcement learning models in PyTorch but also deploy PyTorch models to production using expert tips and techniques. Finally, you'll get to grips with training large models efficiently in a distributed manner, searching neural architectures effectively with AutoML, and rapidly prototyping models using PyTorch and fast.ai. By the end of this PyTorch book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.What you will learn Implement text and music generating models using PyTorch Build a deep Q-network (DQN) model in PyTorch Export universal PyTorch models using Open Neural Network Exchange (ONNX) Become well-versed with rapid prototyping using PyTorch with fast.ai Perform neural architecture search effectively using AutoML Easily interpret machine learning (ML) models written in PyTorch using Captum Design ResNets, LSTMs, Transformers, and more using PyTorch Find out how to use PyTorch for distributed training using the torch.distributed API Who this book is for This book is for data scientists, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning paradigms using PyTorch 1.x. Working knowledge of deep learning with Python programming is required.
Author: Patricia Pulliam Phillips Publisher: Association for Talent Development ISBN: 1607285851 Category : Business & Economics Languages : en Pages : 251
Book Description
A follow-on to ASTD's best-selling ASTD Handbook for Workplace Learning Professionals, the ASTD Handbook of Measuring and Evaluating Training includes more than 20 chapters written by preeminent practitioners in the learning evaluation field. This practical, how-to handbook covers best practices of learning evaluation and includes information about using technology and evaluating e-learning. Broad subject areas are evaluation planning, data collection, data analysis, and measurement and evaluation at work.
Author: Jeremy Howard Publisher: O'Reilly Media ISBN: 1492045497 Category : Computers Languages : en Pages : 624
Book Description
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Author: Daniel T. Larose Publisher: John Wiley & Sons ISBN: 1118868676 Category : Computers Languages : en Pages : 827
Book Description
Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, www.dataminingconsultant, with exclusive password-protected instructor content Data Mining and Predictive Analytics will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.
Author: Barry Briggs Publisher: Microsoft Press ISBN: 1509301992 Category : Computers Languages : en Pages : 228
Book Description
How do you start? How should you build a plan for cloud migration for your entire portfolio? How will your organization be affected by these changes? This book, based on real-world cloud experiences by enterprise IT teams, seeks to provide the answers to these questions. Here, you’ll see what makes the cloud so compelling to enterprises; with which applications you should start your cloud journey; how your organization will change, and how skill sets will evolve; how to measure progress; how to think about security, compliance, and business buy-in; and how to exploit the ever-growing feature set that the cloud offers to gain strategic and competitive advantage.
Author: Antonio Chagoury
Author: Antonio Chagoury
Author: Michael Collier
Author: Vivienne Sze
Author: Luca Pietro Giovanni Antiga
Author: Ashish Ranjan Jha
Author: Patricia Pulliam Phillips
Author: Jeremy Howard
Author: Daniel T. Larose
Author: Lon Luvois Fuller
Author: Barry Briggs