!!> PDF / Epub ✅ Hands-On Computer Vision with TensorFlow 2: Leverage deep learning to create powerful image processing apps with TensorFlow 2.0 and Keras ❤ Autor Benjamin Planche – Andy-palmer.co.uk
A Practical Guide To Building High Performance Systems For Object Detection, Segmentation, Video Processing, Smartphone Applications, And. Key Features Discover How To Build, Train, And Serve Your Own Deep Neural Networks With TensorFlow 2 And Keras Apply Modern Solutions To A Wide Range Of Applications Such As Object Detection And Video Analysis Learn How To Run Your Models On Mobile Devices And Webpages And Improve Their Performance Book Description Computer Vision Solutions Are Becoming Increasingly Common, Making Their Way In Fields Such As Health, Automobile, Social Media, And Robotics This Book Will Help You Explore TensorFlow 2, The Brand New Version Of Google S Open Source Framework For Machine Learning You Will Understand How To Benefit From Using Convolutional Neural Networks CNNs For Visual TasksHands On Computer Vision With TensorFlow 2 Starts With The Fundamentals Of Computer Vision And Deep Learning, Teaching You How To Build A Neural Network From Scratch You Will Discover The Features That Have Made TensorFlow The Most Widely Used AI Library, Along With Its Intuitive Keras Interface, And Move On To Building, Training, And Deploying CNNs Efficiently Complete With Concrete Code Examples, The Book Demonstrates How To Classify Images With Modern Solutions, Such As Inception And ResNet, And Extract Specific Content Using You Only Look Once YOLO , Mask R CNN, And U Net You Will Also Build Generative Adversarial Networks GANs And Variational Auto Encoders VAEs To Create And Edit Images, And LSTMs To Analyze Videos In The Process, You Will Acquire Advanced Insights Into Transfer Learning, Data Augmentation, Domain Adaptation, And Mobile And Web Deployment, Among Other Key ConceptsBy The End Of The Book, You Will Have Both The Theoretical Understanding And Practical Skills To Solve Advanced Computer Vision Problems With TensorFlow 2.0. What You Will Learn Create Your Own Neural Networks From Scratch Classify Images With Modern Architectures Including Inception And ResNet Detect And Segment Objects In Images With YOLO, Mask R CNN, And U Net Tackle Problems In Developing Self Driving Cars And Facial Emotion Recognition Systems Boost Your Application S Performance With Transfer Learning, GANs, And Domain Adaptation Use Recurrent Neural Networks For Video Analysis Optimize And Deploy Your Networks On Mobile Devices And In The Browser Who This Book Is For If You Re New To Deep Learning And Have Some Background In Python Programming And Image Processing, Like Reading Writing Image Files And Editing Pixels, This Book Is For You Even If You Re An Expert Curious About The New TensorFlow 2 Features, You Ll Find This Book UsefulWhile Some Theoretical Explanations Require Knowledge In Algebra And Calculus, The Book Covers Concrete Examples For Learners Focused On Practical Applications Such As Visual Recognition For Self Driving Cars And Smartphone Apps.Table Of Contents Computer Vision And Neural Networks TensorFlow Basics And Training A Model Modern Neural Networks Influential Classification Tools Object Detection Models Enhancing And Segmenting Images Training On Complex And Scarce Datasets Video And Recurrent Neural Networks Optimizing Models And Deploying On Mobile Devices Appendix The book provides a clear mathematical background for understanding neural networks The theoretical explanations are illustrated by applications inspired from historical image processing riddles This makes it quite interesting to follow the book as it is correlated to real life problems and doesn t take shortcuts by oversimplifying things.I had no prior experience with Python, so it was quite challenging for me to get started But even so I went trough the first chapter without any major issue.It worked best for me to juggle back and forth between the book to get the theoretical understanding and the Jupiter Notebook online code to apply the concepts and follow the program examples The reviewers are careless There are a lot of typos in the book Packt let me down.