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Author: Dale Michelson
Publisher: First Rank Publishing
ISBN:
Category :
Languages : en
Pages : 14
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Book Description
As the technology market continues its dynamic movement through the 21st century, major tech conglomerates like Google Inc. have undertaken to create new devices that model this thrust. In recent months, the company has announced the launch and release of many modern and trendy devices. Among the new releases is the new Google smartphone; the Google Pixel 2. The new device, which was announced in mid-2017 and released in early October 2017, has been lauded for its features. The Pixel 2 boasts many features modelled by its 2016 predecessor, the Pixel. It however, brings upgraded, user friendly features designed to fit the needs of both the savvy and the novice smartphone user. The purpose of this book is to educate users on the facts and features of Google’s new Pixel 2 smartphone. It is hoped that the content will also serve to provide the necessary instructions needed to effectively use and enjoy these basic features.
Author: Dale Michelson
Publisher: First Rank Publishing
ISBN:
Category :
Languages : en
Pages : 14
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Book Description
As the technology market continues its dynamic movement through the 21st century, major tech conglomerates like Google Inc. have undertaken to create new devices that model this thrust. In recent months, the company has announced the launch and release of many modern and trendy devices. Among the new releases is the new Google smartphone; the Google Pixel 2. The new device, which was announced in mid-2017 and released in early October 2017, has been lauded for its features. The Pixel 2 boasts many features modelled by its 2016 predecessor, the Pixel. It however, brings upgraded, user friendly features designed to fit the needs of both the savvy and the novice smartphone user. The purpose of this book is to educate users on the facts and features of Google’s new Pixel 2 smartphone. It is hoped that the content will also serve to provide the necessary instructions needed to effectively use and enjoy these basic features.
Author: Dale Michelson
Publisher: First Rank Publishing
ISBN:
Category :
Languages : en
Pages : 15
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Book Description
With the continued movement of the technology sector, major technology firms such as Google Inc. have taken the move to create cutting edge and trendy features that are in keeping with this rapid movement. Within the last few months, Google Inc. has announced the launch and release of many modern and trendy devices. Among the new releases is Google’s new version of earphones; the Google Pixel Buds. The new device, which is scheduled for release to the public in late 2017, was designed to complement the newly released Pixel 2 and Pixel XL smartphones. The new Buds have already received rave reviews which include commendations on its power, comfort –fit for the ear as well as its trendy appearance. The purpose of this book is to educate users on the facts and features of Google’s new Pixel Buds. The hope is that the content shared will serve to provide the necessary instructions needed to effectively use and enjoy the basic features of the device.
Author: Dale Michelson
Publisher: First Rank Publishing
ISBN:
Category :
Languages : en
Pages : 14
Get Book Here
Book Description
As the technology market continues its dynamic movement through the 21st century, major tech conglomerates like Google Inc. have undertaken to create new devices that model this thrust. In recent months, the company has announced the launch and release of many modern and trendy devices. Among the new releases is the new Google smartphone; the Google Pixel 2. The new device, which was announced in mid-2017 and released in early October 2017, has been lauded for its features. The Pixel 2 boasts many features modelled by its 2016 predecessor, the Pixel. It however, brings upgraded, user friendly features designed to fit the needs of both the savvy and the novice smartphone user. The purpose of this book is to educate users on the facts and features of Google’s new Pixel 2 smartphone. It is hoped that the content will also serve to provide the necessary instructions needed to effectively use and enjoy these basic features.
Author: Steven Walryn
Publisher: Silver Starz
ISBN:
Category : Computers
Languages : en
Pages : 123
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Book Description
New to the Google Pixel family are the Pixel 6 and Pixel 6 Pro. They look newer, have higher-quality cameras, and perform better. If you're looking for a premium smartphone with a fantastic camera and other impressive features, look no further than the Google Pixel 6. The phone's aesthetic is also top-notch. The HDR10+-compatible AMOLED screen provides exceptionally rich hues. Comparable to the Google Pixel 6, the Google Pixel Pro is an even more high-end smartphone. In addition to the capabilities of the Google Pixel 6, this device also includes wireless charging, dual front-facing speakers, and a larger battery. There is not enough of a price gap between these two phones for one to be chosen over the other. The Pixel 6 has an 8-megapixel front-facing camera and earpiece nestled into a notch in the display's upper right corner. The 5.6-inch OLED screen has a resolution of 1440 x 3120 pixels and an aspect ratio of 18.5:9. Features include a 3,430mAh battery, wireless charging, and a Qualcomm Snapdragon 845 system-on-chip (SoC). In addition to its stereo speakers on either side of the device's bottom edge, it also features a pair of front-firing stereo speakers and the ability to play Dolby Atmos soundtracks.
Author: Konstantin Titov
Publisher: Konstantin Titov
ISBN:
Category : Computers
Languages : en
Pages : 228
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Book Description
Master Machine Learning Fundamentals Whether you’re an aspiring data scientist, business professional, or curious learner, Machine Learning Fundamentals in Action is your essential guide to the world of machine learning. Packed with practical examples and real-world applications, this book helps you navigate key concepts and techniques transforming industries today. Unlock the Power of Machine Learning Discover every step, from data preparation to building and deploying models, with clear and actionable insights. Who Is This Book For? Aspiring Data Scientists: Build a solid foundation in ML concepts. Business Professionals: Use data-driven decisions to solve challenges. Developers and Engineers: Get hands-on experience with model-building techniques. Curious Learners: Understand ML with easy, step-by-step explanations. What You’ll Learn: Core ML principles and real-world applications Types of ML: Supervised, Unsupervised, and Reinforcement Learning Advanced topics: Neural networks, deep learning, and more How to deploy models and avoid common pitfalls Start your machine learning journey today!
Author: Dr. Priyadarshini Karthikeyan
Publisher: DENTOMED PUBLICATION HOUSE
ISBN: 9391369243
Category : Medical
Languages : en
Pages : 141
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Book Description
Author: Katharina Morik
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110785943
Category : Science
Languages : en
Pages : 506
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Book Description
Machine learning is part of Artificial Intelligence since its beginning. Certainly, not learning would only allow the perfect being to show intelligent behavior. All others, be it humans or machines, need to learn in order to enhance their capabilities. In the eighties of the last century, learning from examples and modeling human learning strategies have been investigated in concert. The formal statistical basis of many learning methods has been put forward later on and is still an integral part of machine learning. Neural networks have always been in the toolbox of methods. Integrating all the pre-processing, exploitation of kernel functions, and transformation steps of a machine learning process into the architecture of a deep neural network increased the performance of this model type considerably. Modern machine learning is challenged on the one hand by the amount of data and on the other hand by the demand of real-time inference. This leads to an interest in computing architectures and modern processors. For a long time, the machine learning research could take the von-Neumann architecture for granted. All algorithms were designed for the classical CPU. Issues of implementation on a particular architecture have been ignored. This is no longer possible. The time for independently investigating machine learning and computational architecture is over. Computing architecture has experienced a similarly rampant development from mainframe or personal computers in the last century to now very large compute clusters on the one hand and ubiquitous computing of embedded systems in the Internet of Things on the other hand. Cyber-physical systems’ sensors produce a huge amount of streaming data which need to be stored and analyzed. Their actuators need to react in real-time. This clearly establishes a close connection with machine learning. Cyber-physical systems and systems in the Internet of Things consist of diverse components, heterogeneous both in hard- and software. Modern multi-core systems, graphic processors, memory technologies and hardware-software codesign offer opportunities for better implementations of machine learning models. Machine learning and embedded systems together now form a field of research which tackles leading edge problems in machine learning, algorithm engineering, and embedded systems. Machine learning today needs to make the resource demands of learning and inference meet the resource constraints of used computer architecture and platforms. A large variety of algorithms for the same learning method and, moreover, diverse implementations of an algorithm for particular computing architectures optimize learning with respect to resource efficiency while keeping some guarantees of accuracy. The trade-off between a decreased energy consumption and an increased error rate, to just give an example, needs to be theoretically shown for training a model and the model inference. Pruning and quantization are ways of reducing the resource requirements by either compressing or approximating the model. In addition to memory and energy consumption, timeliness is an important issue, since many embedded systems are integrated into large products that interact with the physical world. If the results are delivered too late, they may have become useless. As a result, real-time guarantees are needed for such systems. To efficiently utilize the available resources, e.g., processing power, memory, and accelerators, with respect to response time, energy consumption, and power dissipation, different scheduling algorithms and resource management strategies need to be developed. This book series addresses machine learning under resource constraints as well as the application of the described methods in various domains of science and engineering. Turning big data into smart data requires many steps of data analysis: methods for extracting and selecting features, filtering and cleaning the data, joining heterogeneous sources, aggregating the data, and learning predictions need to scale up. The algorithms are challenged on the one hand by high-throughput data, gigantic data sets like in astrophysics, on the other hand by high dimensions like in genetic data. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are applied to program executions in order to save resources. The three books will have the following subtopics: Volume 1: Machine Learning under Resource Constraints - Fundamentals Volume 2: Machine Learning and Physics under Resource Constraints - Discovery Volume 3: Machine Learning under Resource Constraints - Applications Volume 1 establishes the foundations of this new field (Machine Learning under Resource Constraints). It goes through all the steps from data collection, their summary and clustering, to the different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Several machine learning methods are inspected with respect to their resource requirements and how to enhance their scalability on diverse computing architectures ranging from embedded systems to large computing clusters.
Author: John D. Kelleher
Publisher: MIT Press
ISBN: 0262361108
Category : Computers
Languages : en
Pages : 853
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Book Description
The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.
Author: John Paul Mueller
Publisher: John Wiley & Sons
ISBN: 1119543037
Category : Computers
Languages : en
Pages : 370
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Book Description
Take a deep dive into deep learning Deep learning provides the means for discerning patterns in the data that drive online business and social media outlets. Deep Learning for Dummies gives you the information you need to take the mystery out of the topic—and all of the underlying technologies associated with it. In no time, you’ll make sense of those increasingly confusing algorithms, and find a simple and safe environment to experiment with deep learning. The book develops a sense of precisely what deep learning can do at a high level and then provides examples of the major deep learning application types. Includes sample code Provides real-world examples within the approachable text Offers hands-on activities to make learning easier Shows you how to use Deep Learning more effectively with the right tools This book is perfect for those who want to better understand the basis of the underlying technologies that we use each and every day.
Author: Kaizhu Huang
Publisher: Springer
ISBN: 303006073X
Category : Medical
Languages : en
Pages : 168
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Book Description
The purpose of this edited volume is to provide a comprehensive overview on the fundamentals of deep learning, introduce the widely-used learning architectures and algorithms, present its latest theoretical progress, discuss the most popular deep learning platforms and data sets, and describe how many deep learning methodologies have brought great breakthroughs in various applications of text, image, video, speech and audio processing. Deep learning (DL) has been widely considered as the next generation of machine learning methodology. DL attracts much attention and also achieves great success in pattern recognition, computer vision, data mining, and knowledge discovery due to its great capability in learning high-level abstract features from vast amount of data. This new book will not only attempt to provide a general roadmap or guidance to the current deep learning methodologies, but also present the challenges and envision new perspectives which may lead to further breakthroughs in this field. This book will serve as a useful reference for senior (undergraduate or graduate) students in computer science, statistics, electrical engineering, as well as others interested in studying or exploring the potential of exploiting deep learning algorithms. It will also be of special interest to researchers in the area of AI, pattern recognition, machine learning and related areas, alongside engineers interested in applying deep learning models in existing or new practical applications.
