Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python. This book starts by explaining the traditional machine-learning pipeline, where you will analyze an image dataset. Along the way you will cover artificial neural networks (ANNs), building one from scratch in Python, before optimizing it using genetic algorithms.
For automating the process, the book highlights the limitations of traditional hand-crafted features for computer vision and why the CNN deep-learning model is the state-of-art solution. CNNs are discussed from scratch to demonstrate how they are different and more efficient than the fully connected ANN (FCNN). You will implement a CNN in Python to give you a full understanding of the model.
After consolidating the basics, you will use TensorFlow to build a practical image-recognition model that you will deploy to a web server using Flask, making it accessible over the Internet. Using Kivy and NumPy, you will create cross-platform data science applications with low overheads.
This book will help you apply deep learning and computer vision concepts from scratch, step-by-step from conception to production.
What You Will Learn
Understand how ANNs and CNNs work Create computer vision applications and CNNs from scratch using PythonFollow a deep learning project from conception to production using TensorFlowUse NumPy with Kivy to build cross-platform data science applications
Who This Book Is For
Data scientists, machine learning and deep learning engineers, software developers.
Автор: Ranjan Sumit, Senthamilarasu S. Название: Applied Deep Learning and Computer Vision for Self-Driving Cars: Build autonomous vehicles using deep neural networks and behavior-cloning techniques ISBN: 1838646302 ISBN-13(EAN): 9781838646301 Издательство: Неизвестно Рейтинг: Цена: 60070.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book teaches you the different techniques and methodologies associated while implementing deep learning solutions in self-driving cars. You will use real-world examples to implement various neural network architectures to develop your own autonomous and automated vehicle using the Python environment.
Автор: Venkateswara Hemanth, Panchanathan Sethuraman Название: Domain Adaptation in Computer Vision with Deep Learning ISBN: 3030455289 ISBN-13(EAN): 9783030455286 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Preface.- Part I: Introduction.- Chapter 1: Introduction to Domain Adaptation.- Chapter 2: Shallow Domain Adaptation.- Part II: Domain Alignment in the Feature Space.- Chapter 3: d-SNE: Domain Adaptation using Stochastic Neighborhood Embedding.- Chapter 4: Deep Hashing Network for Unsupervised Domain Adaptation.- Chapter 5: Re-weighted Adversarial Adaptation Network for Unsupervised Domain Adaptation.- Part III: Domain Alignment in the Image Space.- Chapter 6: Unsupervised Domain Adaptation with Duplex Generative Adversarial Network.- Chapter 7: Domain Adaptation via Image to Image Translation.- Chapter 8: Domain Adaptation via Image Style Transfer.- Part IV: Future Directions in Domain Adaptation.- Chapter 9: Towards Scalable Image Classifier Learning with Noisy Labels via Domain Adaptation.- Chapter 10: Adversarial Learning Approach for Open Set Domain Adaptation.- Chapter 11: Universal Domain Adaptation.- Chapter 12: Multi-source Domain Adaptation by Deep CockTail Networks.- Chapter 13: Zero-Shot Task Transfer.
Автор: Bradley Efron and Trevor Hastie Название: Computer Age Statistical Inference ISBN: 1107149894 ISBN-13(EAN): 9781107149892 Издательство: Cambridge Academ Рейтинг: Цена: 60190.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.
Автор: Hartley, Zisserman Название: Multiple View Geometry in Computer Vision ISBN: 0521540518 ISBN-13(EAN): 9780521540513 Издательство: Cambridge Academ Рейтинг: Цена: 91860.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The theory and practice of scene reconstruction are described in detail in a unified framework. The new edition features an extended introduction covering the key ideas in the book (which itself has been updated with additional examples and appendices) and significant new results which have appeared since the first edition.
Автор: Barfoot Timothy D Название: State Estimation for Robotics ISBN: 1107159393 ISBN-13(EAN): 9781107159396 Издательство: Cambridge Academ Рейтинг: Цена: 95030.00 T Наличие на складе: Невозможна поставка. Описание: This book is intended for students and practitioners of robotics who are interested in using noisy sensor data to estimate the position, orientation, and other state variables of robots as they move through the three-dimensional world. It covers classical and modern techniques commonly used in robotics today.
Автор: Kristin J. Dana Название: Computational Texture and Patterns: From Textons to Deep Learning ISBN: 1681730111 ISBN-13(EAN): 9781681730110 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 51750.00 T Наличие на складе: Невозможна поставка. Описание: Visual pattern analysis is a fundamental tool in mining data for knowledge. Computational representations for patterns and texture allow us to summarize, store, compare, and label in order to learn about the physical world. Our ability to capture visual imagery with cameras and sensors has resulted in vast amounts of raw data, but using this information effectively in a task-specific manner requires sophisticated computational representations. We enumerate specific desirable traits for these representations: (1) intraclass invariance—to support recognition; (2) illumination and geometric invariance for robustness to imaging conditions; (3) support for prediction and synthesis to use the model to infer continuation of the pattern; (4) support for change detection to detect anomalies and perturbations; and (5) support for physics-based interpretation to infer system properties from appearance. In recent years, computer vision has undergone a metamorphosis with classic algorithms adapting to new trends in deep learning. This text provides a tour of algorithm evolution including pattern recognition, segmentation and synthesis. We consider the general relevance and prominence of visual pattern analysis and applications that rely on computational models.
Автор: Le Lu; Yefeng Zheng; Gustavo Carneiro; Lin Yang Название: Deep Learning and Convolutional Neural Networks for Medical Image Computing ISBN: 3319827138 ISBN-13(EAN): 9783319827131 Издательство: Springer Рейтинг: Цена: 149060.00 T Наличие на складе: Поставка под заказ. Описание: This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database.
Автор: Siddhartha Bhattacharyya, Vaclav Snasel, Aboul Ella Hassanien, Satadal Saha, B. K. Tripathy Название: Deep Learning: Research and Applications ISBN: 3110670798 ISBN-13(EAN): 9783110670790 Издательство: Walter de Gruyter Цена: 136310.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book will focus on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it would provide an insight of deep neural networks in action with illustrative coding examples. Moreover, the book will also provide video demonstrations on each chapter. Deep learning is a new area of machine learning research, which has been introduced with the objective of moving ML closer to one of its original goals, i.e. artificial intelligence. Deep learning was developed as an ML approach to deal with complex input-output mappings. While traditional methods successfully solve problems where final value is a simple function of input data, deep learning techniques are able to capture composite relations between non immediately related fields, for example between air pressure recordings and english words, millions of pixels and textual description, brand-related news and future stock prices and almost all real world problems. Deep learning is a class of nature inspired machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The learning may be supervised (e.g., classification) and/or unsupervised (e.g., pattern analysis) manners. These algorithms learn multiple levels of representations that correspond to different levels of abstraction by resorting to some form of gradient descent for training via backpropagation. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas. They may also include latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep boltzmann machines. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision, automatic speech recognition (ASR) and human action recognition. The unique features of this book include: • tutorials on deep learning framework with focus on tensor flow, keras etc. • video demonstration of each chapter for enabling the readers to have a good understanding of the chapter contents. • a score of worked out examples on real life applications. • illustrative diagrams • coding examples
Автор: Chakraborty, Debarati B Название: Granular video computing: with rough sets, deep learning and in iot ISBN: 981122711X ISBN-13(EAN): 9789811227110 Издательство: World Scientific Publishing Рейтинг: Цена: 84480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This volume links the concept of granular computing using deep learning and the Internet of Things to object tracking for video analysis. It describes how uncertainties, involved in the task of video processing, could be handled in rough set theoretic granular computing frameworks. Issues such as object tracking from videos in constrained situations, occlusion/overlapping handling, measuring of the reliability of tracking methods, object recognition and linguistic interpretation in video scenes, and event prediction from videos, are the addressed in this volume. The book also looks at ways to reduce data dependency in the context of unsupervised (without manual interaction/ labeled data/ prior information) training.This book may be used both as a textbook and reference book for graduate students and researchers in computer science, electrical engineering, system science, data science, and information technology, and is recommended for both students and practitioners working in computer vision, machine learning, video analytics, image analytics, artificial intelligence, system design, rough set theory, granular computing, and soft computing.
Автор: Albena Dimitrova Mihovska, Roshani Raut Название: Handbook of Research on the Impact of Deep Learning and IoT on Multi-Industry Applications ISBN: 1799875113 ISBN-13(EAN): 9781799875116 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 260570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Deep learning, as a recent AI technique, has proven itself efficient in solving many real-world problems. Deep learning algorithms are efficient, high performing, and an effective standard for solving these problems. In addition, with IoT, deep learning is in many emerging and developing domains of computer technology. Deep learning algorithms have brought a revolution in computer vision applications by introducing an efficient solution to several image processing-related problems that have long remained unresolved or moderately solved. Various significant IoT technologies in various industries, such as education, health, transportation, and security, combine IoT with deep learning for complex problem solving and the supported interaction between human beings and their surroundings.
The Handbook of Research on the Impact of Deep Learning and IoT on Multi-Industry Applications provides insights on how deep learning, together with IoT, impacts various sectors such as healthcare, agriculture, cyber security, and social media analysis applications. The chapters present solutions to various real-world problems using these methods from various researchers' points of view. While highlighting topics such as medical diagnosis, power consumption, livestock management, security, and social media analysis, this book is ideal for IT specialists, technologists, security analysts, medical practitioners, imaging specialists, diagnosticians, academicians, researchers, industrial experts, scientists, and undergraduate and postgraduate students who are working in the field of computer engineering, electronics, and electrical engineering.
Автор: Verdhan Vaibhav Название: Computer Vision Using Deep Learning: Neural Network Architectures with Python and Keras ISBN: 1484266153 ISBN-13(EAN): 9781484266151 Издательство: Springer Цена: 32600.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Chapter 1 Introduction to Computer Vision and Deep Learning.- Chapter 2 Nuts and Bolts of Deep Learning for Computer Vision.- Chapter 3 Image Classification using LeNet.- Chapter 4 VGGNet and AlexNext Networks.- Chapter 5 Object Detection Using Deep Learning.- Chapter 6 Facial Recognition and Gesture Recognition.- Chapter 7 Video Analytics Using Deep Learning.- Chapter 8 End-to-end Model Development.- Appendix.
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