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Impact Mapping: Making a Big Impact with Software Products and Projects

Impact Mapping: Making a Big Impact with Software Products and Projects

Software is everywhere today, but countless software products and projects die a slow death without ever making any impact. The result is a tremendous amount of time and money wasted due to wrong assumptions, lack of focus, poor communication of objectives, lack of understanding and misalignment with overall goals. There has to be a better way to deliver! This handbook is a practical guide to impact mapping, a simple yet incredibly effective method for collaborative strategic planning that helps organisations make an impact with software. Impact mapping helps to create better plans and roadmaps that ensure alignment of business and delivery, and are easily adaptable to change. Impact mapping fits nicely into several current trends in software product management and release planning, including goal-oriented requirements engineering, frequent iterative delivery, agile and lean software methods, lean startup product development cycles, and design thinking. About the Author Gojko Adzic is a strategic software delivery consultant who works with ambitious teams to improve the quality of their software products and processes. Gojko won the 2012 Jolt Award for the best book, was voted by peers as the most influential agile testing professional in 2011, and his blog won the UK Agile Award for the best online publication in 2010.

ARM System Developer's Guide: Designing and Optimizing System Software

ARM System Developer's Guide: Designing and Optimizing System Software

Over the last ten years, the ARM architecture has become one of the most pervasive architectures in the world, with more than 2 billion ARM-based processors embedded in products ranging from cell phones to automotive braking systems. A world-wide community of ARM developers in semiconductor and product design companies includes software developers, system designers and hardware engineers. To date no book has directly addressed their need to develop the system and software for an ARM-based system. This text fills that gap.

This book provides a comprehensive description of the operation of the ARM core from a developer's perspective with a clear emphasis on software. It demonstrates not only how to write efficient ARM software in C and assembly but also how to optimize code. Example code throughout the book can be integrated into commercial products or used as templates to enable quick creation of productive software.

The book covers both the ARM and Thumb instruction sets, covers Intel's XScale processors, outlines distinctions among the versions of the ARM architecture, demonstrates how to implement DSP algorithms, explains exception and interrupt handling, describes the cache technologies that surround the ARM cores, as well as the most efficient memory management techniques. A final chapter looks forward to the future of the ARM architecture considering ARMv6, the latest change to the instruction set, which has been designed to improve the DSP and media processing capabilities of the architecture.

Features
  • No other book describes the ARM core from a system and software perspective
  • Author team combines extensive ARM software engineering experience with an in-depth knowledge of ARM developer needs
  • Practical, executable code is fully explained in the book and available on the publishers Web site
  • Includes a simple embedded operating system

Modern Embedded Computing: Designing Connected, Pervasive, Media-Rich Systems

Modern Embedded Computing: Designing Connected, Pervasive, Media-Rich Systems

Modern Embedded Computing: Designing Connected, Pervasive, Media-Rich Systems provides a thorough understanding of the platform architecture of modern embedded computing systems that drive mobile devices. The book offers a comprehensive view of developing a framework for embedded systems-on-chips. Examples feature the Intel Atom processor, which is used in high-end mobile devices such as e-readers, Internet-enabled TVs, tablets, and net books.

This is a unique book in terms of its approach - moving towards consumer. It teaches readers how to design embedded processors for systems that support gaming, in-vehicle infotainment, medical records retrieval, point-of-sale purchasing, networking, digital storage, and many more retail, consumer and industrial applications. Beginning with a discussion of embedded platform architecture and Intel Atom-specific architecture, modular chapters cover system boot-up, operating systems, power optimization, graphics and multi-media, connectivity, and platform tuning. Companion lab materials complement the chapters, offering hands-on embedded design experience.

This text will appeal not only to professional embedded system designers but also to students in computer architecture, electrical engineering, and embedded system design.



  • Learn embedded systems design with the Intel Atom Processor, based on the dominant PC chip architecture. Examples use Atom and offer comparisons to other platforms
  • Design embedded processors for systems that support gaming, in-vehicle infotainment, medical records retrieval, point-of-sale purchasing, networking, digital storage, and many more retail, consumer and industrial applications
  • Explore companion lab materials online that offer hands-on embedded design experience

R in Action, 2ed (MANNING)

R in Action, 2ed (MANNING)

R in Action, Second Edition teaches you how to use the R language by presenting examples relevant to scientific, technical and business developers. Focusing on practical solutions, the book offers a crash course in statistics, including elegant methods for dealing with messy and incomplete data. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. And this expanded second edition includes new chapters on forecasting, data mining and dynamic report writing.

Software Quality Engineering: Testing, Quality Assurance and Quantifiable Improvement

Software Quality Engineering: Testing, Quality Assurance and Quantifiable Improvement

This book tells you how to meet people's expectations with quality planning, software quality, automation, execution, validation, verification, measurement and analysis and support.

Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)

Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)

Data mining is the task of extracting precious information from masses of raw data. The results of data mining could find many different uses and more and more companies are investing in this technology. Data Mining: Concepts And Techniques (The Morgan Kaufmann Series In Data Management Systems) explains all the fundamental tools and techniques involved in the process and also goes into many advanced techniques.

This book not only introduces the fundamentals of data mining, it also explores new and emerging tools and techniques. It explains basic data mining concepts like OLAP, concept description, data preprocessing, classification and prediction, association rules and cluster analysis. It then presents advanced data mining techniques like extracting information from varied and complex sources other than just relational databases. This includes multimedia databases, object databases, time-series databases and spatial databases. It also looks at harvesting data from varied sources on the world wide web and extracting useful information from it.

Data Mining: Concepts And Techniques (The Morgan Kaufmann Series in Data Management Systems) is arranged in such way that the chapters stand as independent units. This makes it flexible as a classroom material, as instructors can choose the chapters they want and present the lessons in the order they prefer.

This third revised edition of the book was brought out by Morgan Kaufmann Publishers In in 2011 in hardcover.

Key Features:

  • Numerous algorithms are presented in pseudo code that can be can be applied in real world projects.
  • A number of pedagogical resources ae available for instructors in the companion website.

Linear Algebra and Learning from Data

Linear Algebra and Learning from Data

Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.

Python Deep Learning: Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow, 2nd Edition

Python Deep Learning: Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow, 2nd Edition

Learn advanced state-of-the-art deep learning techniques and their applications using popular Python libraries Key Features Build a strong foundation in neural networks and deep learning with Python libraries Explore advanced deep learning techniques and their applications across computer vision and NLP Learn how a computer can navigate in complex environments with reinforcement learning Book DescriptionWith the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you'll explore deep learning, and learn how to put machine learning to use in your projects. This second edition of Python Deep Learning will get you up to speed with deep learning, deep neural networks, and how to train them with high-performance algorithms and popular Python frameworks. You'll uncover different neural network architectures, such as convolutional networks, recurrent neural networks, long short-term memory (LSTM) networks, and capsule networks. You'll also learn how to solve problems in the fields of computer vision, natural language processing (NLP), and speech recognition. You'll study generative model approaches such as variational autoencoders and Generative Adversarial Networks (GANs) to generate images. As you delve into newly evolved areas of reinforcement learning, you'll gain an understanding of state-of-the-art algorithms that are the main components behind popular games Go, Atari, and Dota. By the end of the book, you will be well-versed with the theory of deep learning along with its real-world applications. What you will learn Grasp the mathematical theory behind neural networks and deep learning processes Investigate and resolve computer vision challenges using convolutional networks and capsule networks Solve generative tasks using variational autoencoders and Generative Adversarial Networks Implement complex NLP tasks using recurrent networks (LSTM and GRU) and attention models Explore reinforcement learning and understand how agents behave in a complex environment Get up to date with applications of deep learning in autonomous vehicles Who this book is forThis book is for data science practitioners, machine learning engineers, and those interested in deep learning who have a basic foundation in machine learning and some Python programming experience. A background in mathematics and conceptual understanding of calculus and statistics will help you gain maximum benefit from this book.

Intelligent Projects Using Python: 9 real-world AI projects leveraging machine learning and deep learning with TensorFlow and Keras

Intelligent Projects Using Python: 9 real-world AI projects leveraging machine learning and deep learning with TensorFlow and Keras

Implement machine learning and deep learning methodologies to build smart, cognitive AI projects using Python Key Features A go-to guide to help you master AI algorithms and concepts 8 real-world projects tackling different challenges in healthcare, e-commerce, and surveillance Use TensorFlow, Keras, and other Python libraries to implement smart AI applications Book DescriptionThis book will be a perfect companion if you want to build insightful projects from leading AI domains using Python. The book covers detailed implementation of projects from all the core disciplines of AI. We start by covering the basics of how to create smart systems using machine learning and deep learning techniques. You will assimilate various neural network architectures such as CNN, RNN, LSTM, to solve critical new world challenges. You will learn to train a model to detect diabetic retinopathy conditions in the human eye and create an intelligent system for performing a video-to-text translation. You will use the transfer learning technique in the healthcare domain and implement style transfer using GANs. Later you will learn to build AI-based recommendation systems, a mobile app for sentiment analysis and a powerful chatbot for carrying customer services. You will implement AI techniques in the cybersecurity domain to generate Captchas. Later you will train and build autonomous vehicles to self-drive using reinforcement learning. You will be using libraries from the Python ecosystem such as TensorFlow, Keras and more to bring the core aspects of machine learning, deep learning, and AI. By the end of this book, you will be skilled to build your own smart models for tackling any kind of AI problems without any hassle. What you will learn Build an intelligent machine translation system using seq-2-seq neural translation machines Create AI applications using GAN and deploy smart mobile apps using TensorFlow Translate videos into text using CNN and RNN Implement smart AI Chatbots, and integrate and extend them in several domains Create smart reinforcement, learning-based applications using Q-Learning Break and generate CAPTCHA using Deep Learning and Adversarial Learning Who this book is forThis book is intended for data scientists, machine learning professionals, and deep learning practitioners who are ready to extend their knowledge and potential in AI. If you want to build real-life smart systems to play a crucial role in every complex domain, then this book is what you need. Knowledge of Python programming and a familiarity with basic machine learning and deep learning concepts are expected to help you get the most out of the book

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