Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow 1st Edition, Kindle Edition

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ISBN-13: 978-1492034865
ISBN-10: 149203486X
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Editorial Reviews

Review

"Practical leads the title for good reason. For today's ML practices in industry, two priorities loom: staff needs upskilling and models need fine-tuning. This book fast-tracks both." -- Paco Nathan, Founder, Derwen AI, formerly Director at O'Reilly Media.            

From the Author

Using approachable language as well as ready-to-run fun projects in computer vision, the book starts off with simple classifiers assuming no knowledge of machine learning and AI, gradually building in complexity, improving accuracy and speed, scaling to millions of users, deploying on a wide variety of hardware and software, eventually culminating in using reinforcement learning to build a miniature self-driving car.
Nearly every chapter begins with a motivating example, establishes the questions upfront that one might ask through the process of building a solution, and discusses multiple approaches to solve the problem, each with varying levels of complexity and effort involved. If you are seeking a quick solution, you might end up just reading a few pages of a chapter and be done. Someone wanting to gain a deeper understanding of the subject should read the entire chapter. Of course, everyone should peruse the case studies at the end of each chapter for two reasons--they are fun to read and they showcase how people in the industry are using the concepts discussed in the chapter to build real products (over 40 discussed).
We also discuss many of the practical concerns faced by deep learning practitioners and industry professionals in building real-world applications using the cloud, browsers, mobile, and edge devices. We compiled a number of practical "tips and tricks", as well as life-lessons in this book to encourage our readers to build applications that can make someone's day just a little bit better.
To the Backend/Frontend/Mobile Software DeveloperYou are quite likely a proficient programmer already. Even if Python is an unfamiliar language to you, we expect that you will be able to pick it up easily and get started in no time. Best of all, we don't expect you to have any background in machine learning and AI; that's what we are here for! We believe that you will gain value from the book's focus in the following areas:
  • How to build user-facing AI products.
  • How to train models quickly.
  • How to minimize the code and effort required in prototyping.
  • How to make models more performant and energy-efficient.
  • How to operationalize and scale, and estimate the costs involved.
  • Discover how AI is applied in the industry with 40+ case studies.
  • Develop a broad-spectrum knowledge of deep learning.
  • Develop a generalized skill set that can be applied on new frameworks (e.g., PyTorch), domains (e.g., healthcare, robotics), input modalities (e.g., video, audio, text), and tasks (e.g., image segmentation, one-shot learning).
To the Data ScientistYou might already be proficient at machine learning and potentially know how to train deep learning models. Good news! You can further enrich your skillset and deepen your knowledge in the field in order to then build real products.  This book will help inform your everyday work and beyond by covering how to:
  • Speed up your training, including on multi-node clusters.
  • Build an intuition for developing and debugging models, including hyperparameter tuning, thus dramatically improving model accuracy.
  • Understand how your model works, uncover bias in the data, and automatically determine the best hyperparameters as well as model architecture using AutoML.
  • Learn tips and tricks used by other data scientists, including gathering data quickly, tracking your experiments in an organized manner, sharing your models with the world, and being up to date on the best available models for your task.
  • Use tools to deploy and scale your best model to real users, and even automatically (without involving a dev-ops team).
To the StudentThis is a great time to be considering a career in AI--this is turning out to be the next revolution in technology after the internet and smartphones. A lot of strides have been made, and a lot remains to be discovered. We hope that this book can serve as your first step in whetting your appetite for a career in AI and, even better, developing deeper theoretical knowledge. And the best part is that you don't have to spend a lot of money to buy expensive hardware. In fact, you can train on powerful hardware entirely for free from your web browser (thank you, Google Colab!). With this book, we hope you will:
  • Aspire to a career in AI by developing a portfolio of interesting projects.
  • Learn from industry practices to help prepare for internships and job opportunities.
  • Unleash your creativity by building fun applications like an autonomous car.
  • Prepare for interviews for jobs in the AI field.
  • Become an AI for Good champion by using your creativity to solve the most pressing problems faced by humanity.
To the TeacherWe believe that this book can nicely supplement your coursework with fun, real-world projects. We've covered every step of the deep learning pipeline in detail, along with techniques on how to execute each step effectively and efficiently. Each of the projects we present in the book can make for great collaborative or individual work in the classroom throughout the semester.
To the Robotics EnthusiastRobotics is exciting. If you're a robotics enthusiast, we don't really need to convince you that adding intelligence to robots is the way to go. Increasingly capable hardware platforms such as Raspberry Pi, NVIDIA Jetson Nano, Google Coral, Intel Movidius, PYNQ-Z2, and others are helping drive innovation in the robotics space. As we grow towards Industry 4.0, (some of) these platforms will become more and more relevant and ubiquitous. With this book, you will:
  • Learn how to build and train AI, and then bring it to the edge.
  • Benchmarking and compare edge devices on performance, size, power, battery and costs.
  • Understand how to choose the optimal AI algorithm and device for a given scenario.
  • Learn on how other makers are building creative robots and machines.
  • Learn how to further progress in the field and showcase your work.

Product details

  • Publication Date : October 14, 2019
  • File Size : 102494 KB
  • Print Length : 620 pages
  • Word Wise : Not Enabled
  • Publisher : O'Reilly Media; 1st Edition (October 14, 2019)
  • Language: : English
  • ASIN : B07Z7957PL
  • Text-to-Speech : Enabled
  • Simultaneous Device Usage : Unlimited
  • X-Ray : Not Enabled
  • Enhanced Typesetting : Enabled
  • Lending : Not Enabled
  • Customer Reviews:
    4.6 out of 5 stars 59 ratings

Customer reviews

4.6 out of 5 stars
4.6 out of 5
59 global ratings
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Reviewed in the United States on November 18, 2019
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Gourav Sengupta
5.0 out of 5 stars super
Reviewed in the United Kingdom on May 11, 2020
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GM
5.0 out of 5 stars Great book to learn practical application of deep learning
Reviewed in Canada on January 1, 2020
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Sibte
5.0 out of 5 stars Great guide for learning about AI/ML
Reviewed in Canada on July 6, 2020
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Cliente Amazon
1.0 out of 5 stars MUY MALA CALIDAD DE IMPRESION
Reviewed in Spain on September 23, 2020
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Mohit
5.0 out of 5 stars Easy to read but still thorough.
Reviewed in Canada on November 26, 2019
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