web site hit counter Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & Tensorflow - Ebooks PDF Online
Hot Best Seller

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

Availability: Ready to download

Whether you're a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach Whether you're a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral Explore fun projects, from Silicon Valley's Not Hotdog app to 40+ industry case studies Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning Use transfer learning to train models in minutes Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users


Compare

Whether you're a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach Whether you're a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral Explore fun projects, from Silicon Valley's Not Hotdog app to 40+ industry case studies Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning Use transfer learning to train models in minutes Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users

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

  1. 4 out of 5

    Paco Nathan

    Excellent into to deep learning, based on open source tools and practical use cases. Loved the book so much that I got to be a technical reviewer :)

  2. 5 out of 5

    Jorge Ayala

    I'm a web developer who is interested in Deep learning and all its potential applications to real-world problems. Usually, when it comes to finding proper resources to learn the fundamentals of DL, you get a bunch of tutorials, online courses, books, and papers that focus on the explanation of the core algorithms that fuel neural networks. I have no doubts that it is of extreme importance to get these fundamentals right from the very beginning, especially if you want to start your career serious I'm a web developer who is interested in Deep learning and all its potential applications to real-world problems. Usually, when it comes to finding proper resources to learn the fundamentals of DL, you get a bunch of tutorials, online courses, books, and papers that focus on the explanation of the core algorithms that fuel neural networks. I have no doubts that it is of extreme importance to get these fundamentals right from the very beginning, especially if you want to start your career seriously; but if you are like me, someone who thinks in terms of functions, APIs, objects, etc., it can be quite demotivating to spend so much time reading content that might not seem "useful" at first sight: you need to get your hands dirty, you need to see that bloody compiler giving you error messages, you need to see that what you have done is executing and after a lot of work, finally getting something clickable. If you are that person, this book is perfect for you. But what is great about this book is the fact that it is MUCH MORE THAN A RECIPE OF CODE FOR DIFFERENT USE CASES, IT IS A HIGH-QUALITY TOUR THROUGH THE ENTIRE DEEP LEARNING LANDSCAPE and the practical examples are means to explain each theory, concept, algorithm to the down-to-earth software developer in a practical manner. In all honesty, I think the title of the book fails to show it, so the authors should consider a better one for the next editions. Do you remember that HOT-DOG-NOT-HOT-DOG app from the Silicon Valley series? Strangely, it was a success. What about that "SeeFood" app, the Shazam for food? Well perhaps both ideas are not so "edgy" but certainly, you will have the right skills to code both apps after reading the book. Finally, the authors are brilliant and very approachable, you can contact them and you will likely get a warm response from them.

  3. 5 out of 5

    Krishnan Ajay

    TLDR; Best book to understand and use Deep Learning in the industry. This book provides a bridge between what’s taught in courses and what is actually done in the industry. I was just finishing my Junior year with a lot of theoretical knowledge of Deep Learning but not enough practical experience to showcase this during interviews. Most resources on the internet only gave a theoretical understanding through equations, whereas this book has been completely practical and goal-oriented since its fir TLDR; Best book to understand and use Deep Learning in the industry. This book provides a bridge between what’s taught in courses and what is actually done in the industry. I was just finishing my Junior year with a lot of theoretical knowledge of Deep Learning but not enough practical experience to showcase this during interviews. Most resources on the internet only gave a theoretical understanding through equations, whereas this book has been completely practical and goal-oriented since its first page. This book does an amazing job at telling you what exactly you need in order to deploy your model, scale it further and analyze its performance as it grows. Each chapter becomes a separate project by itself on different areas in Deep Learning. The authors also made this book extremely beginner-friendly and easy to understand such that implementing and testing every concept was not half as complex as it was in theory. There are cheatsheets present at every chapter providing tips and techniques for every situation an ML Engineer building AI for an edge device might find themselves in. After an in-depth explanation of a certain process/concept in every chapter, there is also a Case Studies section about the same concept being deployed in a popular Silicon Valley startup / a tech giant with an explanation about how this concept works in the company’s use-case. My favorite chapters were those on building our own Self Driving car and Building an Autonomous Car using Reinforcement Learning with AWS DeepRacer. These chapters gave me clear insight into how AI works in the field of Self Driving Vehicles and also pointed me towards implementing them. The book teaches you how to work with all the state-of-the-art libraries, and tools such as TensorFlow, Keras, TFLite for Android, CoreML for iPhone, and more, suited for edge devices. The information on making full use of edge devices up giving me a huge boost in interviews and subsequent interviews too. 10/10 would recommend if you’re looking to apply all the theory you’ve learned from courses into real life.

  4. 4 out of 5

    Satyarth Praveen

    Have some idea regarding deep learning but not enough experience with the projects and deployment? This is the book you NEED! This is a brilliant book to gain hands-on experience with some deep learning projects and answer any of your deployment related questions - this includes scalability, efficiency, and beautiful visualizations. I have bought this book for my sister and recommend it to anyone willing to get started with AI projects. I have been using code snippets from this book for a lot of Have some idea regarding deep learning but not enough experience with the projects and deployment? This is the book you NEED! This is a brilliant book to gain hands-on experience with some deep learning projects and answer any of your deployment related questions - this includes scalability, efficiency, and beautiful visualizations. I have bought this book for my sister and recommend it to anyone willing to get started with AI projects. I have been using code snippets from this book for a lot of my deep-learning projects. Plus plus plus, the book is easy to read and engages the reader with random puns!

  5. 5 out of 5

    Rudy Venguswamy

    This book provide a deeply informative and influential toolkit for teams and individual practitioners of machine learning and artificial intelligence to update their skill set to the more modern standards of AI in industry. It condenses the subject matter expertise of the authors ranging including training optimal models, item similarity, model deployment, and computer vision interpretability into easily understood concepts coupled with code that I was able to apply and tweak for my own projects This book provide a deeply informative and influential toolkit for teams and individual practitioners of machine learning and artificial intelligence to update their skill set to the more modern standards of AI in industry. It condenses the subject matter expertise of the authors ranging including training optimal models, item similarity, model deployment, and computer vision interpretability into easily understood concepts coupled with code that I was able to apply and tweak for my own projects. As a recent graduate in the field of machine learning, understanding how I could apply computer vision to my projects, both in school and in work, was quite valuable to me and provides an organized framework through which I could think about how to design and tackle a computer vision problem using ML. In my circle in particular, this book has become a go-to guide for computer vision and ML development. Our team was able to get up to speed and build a reverse image search pipeline for NASA and through the examples provided on efficient code design, were able to upgrade our tool to scale to petabyte-levels of information. Unlike the purely theoretical literature that is available in abundance on computer vision, this practical guide contains the authors' insights on bringing these theoretical concepts to industry-readiness and, at least in my team, has had a disproportionate influence on our machine learning strategy and deployment.

  6. 4 out of 5

    Fanyue Xia

    Being a first-year undergraduate student without much prior knowledge of artificial intelligence, this book really brought my confidence up. Different from other books and courses about AI I had studied before, this book focuses on practical applications with the help of Python, TensorFlow and Keras ecosystem. Almost every chapter starts with an interesting real-life scenario and problems needed to be solved. It then includes step by step instructions on how to approach the problem with complemen Being a first-year undergraduate student without much prior knowledge of artificial intelligence, this book really brought my confidence up. Different from other books and courses about AI I had studied before, this book focuses on practical applications with the help of Python, TensorFlow and Keras ecosystem. Almost every chapter starts with an interesting real-life scenario and problems needed to be solved. It then includes step by step instructions on how to approach the problem with complementary codes and explanation as well as graphical demonstrations of various APIs. For me, it is a rewarding experience to take advantage of numerous resources recommended by the book, such as experimenting with the codes and data myself on Google Colab, exploring other related projects being developed on Github, and training my own model using Google Cloud Vision. Moreover, case studies at the end of each chapter, which elaborates on how the methods presented are being employed by big companies to develop important real-life applications, sufficiently establish the power and availability of machine learning, making me determined to explore machine learning further as my additional major and potential career option.  Overall, this book is a fantastic guide for readers to understand the concept of machine learning, to equip themselves with the ability to really contribute to the machine learning community, and to keep up with its leading-edge research and application.

  7. 5 out of 5

    Tina

    I am not a computer science engineer and did not know anything about ML. However, after reading this book, I can run and train my own customized models to solve problems related to my field (management). The book is much fun to read and the examples used help the user connect things to the real world and not just computer geekery. I would recommend this book to anyone irrespective of their experience with Machine Learning. It is interesting, informative, insightful, and encouraging for anyone to I am not a computer science engineer and did not know anything about ML. However, after reading this book, I can run and train my own customized models to solve problems related to my field (management). The book is much fun to read and the examples used help the user connect things to the real world and not just computer geekery. I would recommend this book to anyone irrespective of their experience with Machine Learning. It is interesting, informative, insightful, and encouraging for anyone to tackle this interesting field.

  8. 4 out of 5

    Nishant

    Awesome Content! I have just read couple of chapters till now, the way they have explained the ML concepts is just superb any newbie would be able to understand and relate to.

  9. 4 out of 5

    Dhiraj Srivastava

    I am reviewing this book after reading the first 14 chapters out of a total of 17 chapters. I had a good experience with ML and DL through online websites such as coursera, udemy and my undergrad projects. But I never worked to bring my deep learning model from jupyter notebook to real-world application. I am currently pursuing my Master's in AI in agriculture science at Virginia Tech USA. As a part of my research, I have to build real-world deep learning applications. I was looking for a book w I am reviewing this book after reading the first 14 chapters out of a total of 17 chapters. I had a good experience with ML and DL through online websites such as coursera, udemy and my undergrad projects. But I never worked to bring my deep learning model from jupyter notebook to real-world application. I am currently pursuing my Master's in AI in agriculture science at Virginia Tech USA. As a part of my research, I have to build real-world deep learning applications. I was looking for a book which can build my foundation to deploy the models for a real-world scenario. This book bridged the gap between my theoretical knowledge and practical use. This book even helped me to write a research proposal and this research proposal got accepted by my university committee for financial support. I feel whether you are a beginner, average or experienced, you must go through this book if you are looking for a book which can help you to learn the techniques for building robust and efficient Ai apps. This book shows you the path to improve speed and accuracy as well as scaling to millions of users. With 30+ case studies and industry examples, you will become confident in solving any kind of industry problem. This book also focuses on Responsible AI. Some of the chapters that helped me are : Real time object classification on iOS, developing android apps with tensorflow lite, maximizing speed and accuracy of tensorflow ( a handy checklist). I highly recommend you to read this book if you are looking to solve and deploy real-world AI problems.

  10. 5 out of 5

    Aaron

    Exceptional book for learning the fundamentals of AI and how to program it yourself! I’m a geography student with great interest in Artificial Intelligence and remote sensing. Three months ago, I was only familiar with high level machine learning terms, but I did not know much about Deep Learning. This book changed that, teaching me the theoretical foundation of AI and Deep Learning and enabled me to write my own applications with Python using Tensorflow and Keras within weeks! Especially the git Exceptional book for learning the fundamentals of AI and how to program it yourself! I’m a geography student with great interest in Artificial Intelligence and remote sensing. Three months ago, I was only familiar with high level machine learning terms, but I did not know much about Deep Learning. This book changed that, teaching me the theoretical foundation of AI and Deep Learning and enabled me to write my own applications with Python using Tensorflow and Keras within weeks! Especially the git repository for this book is extremely useful, combining the practical part with the theoretical explanations in the book from the beginning. Post that, I was able to finish 8 weeks of Coursera TensorFlow Specialization (2 courses) in 8 days, because I was already familiar with it. With the knowledge that I have gained working through this book, I got accepted as an AI researcher at SpaceML, detecting anomalies in remote sensing data from videos of earth in order to create alerts using Deep Learning (using Convolutional LSTM autoencoders). Summarizing, I highly recommend this book to anyone who wants to quickly learn the fundamentals of Deep Learning and start programming it today!

  11. 4 out of 5

    Erica

    This book is a great introduction to AI and I would recommend it to anyone that is interested in getting started with AI. It does a great job covering concepts in a way that is easy to understand for those with little to no background knowledge. The problems in each chapter get you thinking about how to use what you just learned. The book walks you through tackling small parts of the problem until you complete it. There are code snippets and links to outside resources to support you as you solve This book is a great introduction to AI and I would recommend it to anyone that is interested in getting started with AI. It does a great job covering concepts in a way that is easy to understand for those with little to no background knowledge. The problems in each chapter get you thinking about how to use what you just learned. The book walks you through tackling small parts of the problem until you complete it. There are code snippets and links to outside resources to support you as you solve the problem. As someone that only had knowledge of Python, I found this feature of the book to be helpful in getting started with frameworks and APIs that I was unfamiliar with. This is what sets this book apart from others because rather than only a focus on theory, you're able to learn, experiment with it on you own, and be coding right away. I also wanted to point out how the book pinpoints case studies with big names that you have likely heard of before. Once again, it emphasizes that what you learn from this book has practical applications to real-world situations which is something I enjoyed reading about. I would recommend this book for those that want to learn more about AI, read about the applications of the concepts you are learning, and get coding.

  12. 4 out of 5

    Hada

    Great guidebook for beginners to realize practical applications of deep learning. I am a public officer supporting small and medium-sized enterprises in Japan for digital transformation, and used this book as increase my breadth of full AI lifecycle knowledge. There are many deep learning books that explain theories and looks at academic papers, but that’s so far from needs of practical use right now, especially for beginners. If you would like to visually see each step in training and deployment Great guidebook for beginners to realize practical applications of deep learning. I am a public officer supporting small and medium-sized enterprises in Japan for digital transformation, and used this book as increase my breadth of full AI lifecycle knowledge. There are many deep learning books that explain theories and looks at academic papers, but that’s so far from needs of practical use right now, especially for beginners. If you would like to visually see each step in training and deployment and get useful information about practical application of deep learning, this book is one of the best to read. For example, you will be able to find out photos and graphics comparing which framework to use, how to scale up representative power of embeddings etc which help you practical deep learning use cases to understand easily. Furthermore, you will be able to access and use free or reasonable services provided by IT giants like Colab, Github repo, which induces your interest and increase motivation. This book must contribute prevalence of deep learning and the digital transformation which is necessary for the near future society.

  13. 4 out of 5

    Shaveen Kumar

    I have read several books on deep learning but I found this one particularly fun to read. Right from the beginning the authors keep the content interesting with relatable examples and as the book progresses, there is a very natural progression in difficulty while still remaining very understandable. For beginners to deep learning, I would definitely recommend this as one of the best choices to start with because the examples provided are very interesting and hands on and are explained in a manne I have read several books on deep learning but I found this one particularly fun to read. Right from the beginning the authors keep the content interesting with relatable examples and as the book progresses, there is a very natural progression in difficulty while still remaining very understandable. For beginners to deep learning, I would definitely recommend this as one of the best choices to start with because the examples provided are very interesting and hands on and are explained in a manner that is very simple to understand. I also really liked how the authors focused on visualizing what the network sees right from the beginning of the book. I feel this is very important and makes the reader more invested in learning about how the network actually works rather that considering it a black box that magically produces results. I am personally a PyTorch user, but that didn't stop me from going through and running the examples. There is a well maintained github repository as well. I highly recommend this book.

  14. 5 out of 5

    Zoe

    I have a computer science background but I am a newbie to Deep Learning. I was a little bit hesitant to read this book because a book title with the word 'practical' sometimes means that you should already have somewhat background knowledge beforehand. Well, the book wasn't super easy for me but it was definitely more approachable than I expected. I really enjoyed the sense of humor in this book. It made my reading time more relaxing - just like I got a fun and friendly next door professional as I have a computer science background but I am a newbie to Deep Learning. I was a little bit hesitant to read this book because a book title with the word 'practical' sometimes means that you should already have somewhat background knowledge beforehand. Well, the book wasn't super easy for me but it was definitely more approachable than I expected. I really enjoyed the sense of humor in this book. It made my reading time more relaxing - just like I got a fun and friendly next door professional as my tutor. I sometimes got lost when reading detailed explanations, but for the most times the easy examples and clear summaries in this book saved me from confusion :) Also I loved the fact that I can easily access the codes used in the book and run them without an expensive computer setting. I actually got to know that the Korean version of this book is now in a library in my neighborhood, so I'm gonna try it out for reviewing too.

  15. 4 out of 5

    Prashanth Mangipudi

    Firstly, really thankful for the contents of this book for my work projects. While I was wondering how to optimize the deep learning model computation, I've come across a TensorFlow User Group Summit keynote session by the author in which he explained techniques to improve training and inference. Based on that, I got to see the book which contains details in more depth. I'd say this book is a weapon for anyone who's looking to build deep learning models for edge devices or planning to deploy the Firstly, really thankful for the contents of this book for my work projects. While I was wondering how to optimize the deep learning model computation, I've come across a TensorFlow User Group Summit keynote session by the author in which he explained techniques to improve training and inference. Based on that, I got to see the book which contains details in more depth. I'd say this book is a weapon for anyone who's looking to build deep learning models for edge devices or planning to deploy their model on the cloud with less latency. Also, the authors were kind enough to respond to queries and guidance on the path to getting better at deep learning. I should also mention the image search optimization techniques that are said in this book are pretty useful. If you are the one who loves to scale your deep learning model while not losing much accuracy or generalization capabilities, this is the book for you.

  16. 5 out of 5

    Vishwanath

    Plenty of examples and links for more research. The material is too vast enough to make an all encompassing book but this delivers in terms of practical tips. Its evident the authors are practitioners with over 50+ practical tips provided as promised in the description that will find a place in any serious ML engineer repertoire. The consolidated list of tips are worth the book alone. Excellent comparisons of Raspberry Pi, Jetson Nano, and Google Coral. The reinforcement learning sections could Plenty of examples and links for more research. The material is too vast enough to make an all encompassing book but this delivers in terms of practical tips. Its evident the authors are practitioners with over 50+ practical tips provided as promised in the description that will find a place in any serious ML engineer repertoire. The consolidated list of tips are worth the book alone. Excellent comparisons of Raspberry Pi, Jetson Nano, and Google Coral. The reinforcement learning sections could have used some more practical examples in areas like q-learning but overall great read and reference material.

  17. 4 out of 5

    Ladi O

    There are a lot of good machine learning books for machine learning theory, however I haven't found many good ones for the practical day to day aspects for building deep learning production models. What I liked most about this book is that it indeed is very practical. This book does a good job of providing actionable advice for ML engineers/data scientist. Chapters 6 and 7, for instance, are immediately helpful especially if you are relatively new to the tensorflow/keras framework. This should d There are a lot of good machine learning books for machine learning theory, however I haven't found many good ones for the practical day to day aspects for building deep learning production models. What I liked most about this book is that it indeed is very practical. This book does a good job of providing actionable advice for ML engineers/data scientist. Chapters 6 and 7, for instance, are immediately helpful especially if you are relatively new to the tensorflow/keras framework. This should definitely be in your data science library. Highly recommend

  18. 5 out of 5

    Sterling

    A rather good and approachable introduction to AI/ML and deep learning. Once of the major facets that I appreciate about this book is the fact that everything is stored in GitHub and has a matching Google Colab notebook (free GPU!), making it easy to follow along and learn by doing. All in all, the content has been quite useful, rich in application, easy to follow along with (no excuses since Google Colab doesn't use local resources), and I've accomplished the goal I've set out with — highly recom A rather good and approachable introduction to AI/ML and deep learning. Once of the major facets that I appreciate about this book is the fact that everything is stored in GitHub and has a matching Google Colab notebook (free GPU!), making it easy to follow along and learn by doing. All in all, the content has been quite useful, rich in application, easy to follow along with (no excuses since Google Colab doesn't use local resources), and I've accomplished the goal I've set out with — highly recommend!

  19. 5 out of 5

    Arman Sidhu

    Being a CS major, I always wanted to get myself familiar with Machine Learning and AI. But I was confused about what resource would be the best to get started. The "learn by doing projects" approach of this book never made me feel like I was just reading a book. I recently won a hackathon by using the practical knowledge I gained from this book. Being a CS major, I always wanted to get myself familiar with Machine Learning and AI. But I was confused about what resource would be the best to get started. The "learn by doing projects" approach of this book never made me feel like I was just reading a book. I recently won a hackathon by using the practical knowledge I gained from this book.

  20. 5 out of 5

    Geetika Sharma

    The book is definitely one of the best resources that gives insights about applied Deep Learning that too using open-source tools and plenty of examples. Would definitely recommend this to anyone looking to use Deep Learning in the industry.

  21. 5 out of 5

    Rohit Taneja

    really well structured book which helps understand not only fundamentals, but also deep-dives into the real use-cases, which help connects the theory to how DL is applied for practical tasks. Highly recommended.

  22. 5 out of 5

    Pranav Kant

    I went through this book as part of my ML course in university and found it very insightful. The practical approach in this book was a nice complement to the theory I was studying in class.

  23. 4 out of 5

    Charanjeet

    Great book for getting you started in practical deep learning. Neat explaination and very easy to understand. Highly recommended to all deep learning enthusiast.

  24. 4 out of 5

    Filippo Garolla

  25. 5 out of 5

    Aashima

  26. 5 out of 5

    Habeeb Shopeju

  27. 5 out of 5

    Prof. Dr.

  28. 4 out of 5

    Giannis Mel

  29. 4 out of 5

    Josep-Angel Herrero Bajo

  30. 4 out of 5

    paul

Add a review

Your email address will not be published. Required fields are marked *

Loading...
We use cookies to give you the best online experience. By using our website you agree to our use of cookies in accordance with our cookie policy.