What topics are covered in 'Practical Deep Learning for Cloud, Mobile, and Edge'?

This book explores a wide array of topics including foundational concepts in deep learning, hands-on projects that utilize artificial intelligence and computer vision, and practical implementations using popular frameworks such as Python, Keras, and TensorFlow. It emphasizes real-world applications across devices like mobile and edge computing, offering readers a comprehensive guide to deploying deep learning solutions in various environments.

Who is the target audience for this book?

The target audience includes software developers, data scientists, and AI enthusiasts looking to deepen their understanding of deep learning. It is suitable for both beginners and intermediate users who want to apply their knowledge to practical projects. Scenarios such as building AI applications for mobile platforms or edge devices make it an invaluable resource for professionals seeking to innovate in the tech industry.

What programming languages and tools are featured in this book?

The primary programming language featured in this book is Python. It discusses tools and libraries such as Keras and TensorFlow, which are essential for building and training deep learning models. By leveraging these technologies, readers can effectively implement machine learning algorithms in cloud, mobile, and edge computing contexts, addressing modern challenges in AI development.

Are there hands-on projects included in the book?

Yes, 'Practical Deep Learning for Cloud, Mobile, and Edge' includes several hands-on projects. Each project is designed to help readers apply theoretical concepts in practical scenarios, such as creating a computer vision application or deploying a model to a mobile platform. These projects not only reinforce learning but also demonstrate how to tackle real-world problems using AI.

What level of experience do I need to start reading this book?

While the book is accessible to beginners, a basic understanding of programming in Python and some familiarity with machine learning concepts will enhance the learning experience. Readers with a background in software development or data analysis will find it easier to grasp advanced topics and project implementations detailed within the chapters.

Is this book suitable for learning about cloud-based AI applications?

Absolutely. The book contains specific sections dedicated to cloud-based AI applications, demonstrating how to deploy models efficiently in cloud environments. This knowledge is crucial for anyone looking to leverage cloud computing capabilities for scalability in AI projects, making it particularly beneficial for businesses and tech professionals.

How does this book address computer vision?

Computer vision is a major focus of this book, with various projects designed to illustrate real applications of the technology. It covers techniques for image processing, object detection, and recognition tasks, providing a thorough understanding of how to develop effective computer vision solutions using Keras and TensorFlow, which are vital in industries such as healthcare, automotive, and retail.

What are some real-world applications of the projects in this book?

The projects span a range of real-world applications, including facial recognition systems, image classification for mobile applications, and real-time video analysis for security systems. By engaging in these projects, readers will learn to solve problems that require deep learning, ultimately preparing them for careers in sectors like technology, finance, and manufacturing.

Can this book help integrate AI with edge computing?

Yes, the book provides insights into integrating AI with edge computing, discussing how to optimize models for performance and efficiency in environments with limited resources. This is particularly useful for developing applications for IoT devices, allowing for smarter and more responsive technology solutions that operate closer to the source of data generation.

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Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow

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Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow

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What Stands Out

Hands-On Projects

Engage with real-world AI and computer vision projects, enhancing your practical skills and understanding of deep learning applications across various platforms including cloud, mobile, and edge devices.

Framework Variety

Utilize popular frameworks like Python, Keras, and TensorFlow, ensuring that learners can adapt lessons to different contexts and keep pace with industry standards in AI development.

Comprehensive Coverage

Explore a broad spectrum of deep learning topics tailored for practical applications, empowering readers to build robust AI solutions that address specific user challenges effectively.

Product Details

Teaches how to build practical deep learning applications for cloud, mobile, browsers, and edge devices using Python, Keras & TensorFlow
Authors guide through transforming an idea into real-world usable applications
Covers training, tuning, and deploying computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite
Explores a variety of AI development projects for devices like Raspberry Pi, Jetson Nano, and Google Coral
Offers practical tips for maximizing model accuracy, speed, debugging, and scaling to millions of users
Features content contributed by industry experts and veterans

Publisher	O'Reilly Media
Publication date	November 26, 2019
Edition	1st
Language	English
Print length	620 pages
ISBN-10	149203486X
ISBN-13	978-1492034865
Item Weight	2.2 pounds (1 kg)
Dimensions	7 x 1.25 x 9 inches (17.8 x 3.2 x 22.9 cm)

Who Should Buy?

Aspiring Data Scientists
Ideal for those wanting hands-on projects to apply machine learning concepts practically using Python and TensorFlow.
AI Developers
Perfect for developers looking to implement deep learning solutions in cloud, mobile, and edge computing scenarios.
Students in AI
Beneficial for students seeking practical applications of theoretical AI knowledge in real-world scenarios.

Beginners in Programming
Not suitable for users without a programming background, as it requires basic knowledge of Python.

Product Description

Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow

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Intelligence & Semantics Editorial Review

Practical Deep Learning for Cloud, Mobile, and Edge is a highly recommended book for AI practitioners. It provides practical knowledge and insights for building real-world AI and computer-vision projects. The book covers a wide range of topics, from theoretical concepts to the process of building and scaling practical applications. It also includes best practices, checklists, and case studies. The content is comprehensive, covering TensorFlow, Keras, CoreML, and cloud concepts for Google Cloud, AWS, and Azure. Overall, this book offers a different approach to AI than most online courses and is a valuable resource for both beginners and experienced professionals.

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Pros

Practical knowledge and insights for building real-world AI and computer-vision projects
Covers a wide range of topics, including theoretical concepts, best practices, checklists, and case studies
Comprehensive coverage of TensorFlow, Keras, CoreML, and cloud concepts for major platforms
Suitable for both beginners and experienced professionals

Cons

Outdated and lacking outside common knowledge

Product Price History

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Features & Benefits

Step-by-step guide for building practical deep learning applications.
Train, tune, and deploy computer vision models.
Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral.
Explore fun projects and 40+ industry case studies.
Learn transfer learning to train models in minutes.
Discover practical tips for maximizing model accuracy, debugging, and scaling.

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Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow

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