Descargar Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using TensorFlow and Keras de Rajalingappaa Shanmugamani Ebooks, PDF, ePub

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Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using TensorFlow and Keras de Rajalingappaa Shanmugamani

Descripción - Reseña del editor Learn how to model and train advanced neural networks to implement a variety of Computer Vision tasksKey FeaturesTrain different kinds of deep learning model from scratch to solve specific problems in Computer VisionCombine the power of Python, Keras, and TensorFlow to build deep learning models for object detection, image classification, similarity learning, image captioning, and moreIncludes tips on optimizing and improving the performance of your models under various constraintsBook DescriptionDeep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning. In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more. You will also explore their applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation.What you will learnSet up an environment for deep learning with Python, TensorFlow, and KerasDefine and train a model for image and video classificationUse features from a pre-trained Convolutional Neural Network model for image retrievalUnderstand and implement object detection using the real-world Pedestrian Detection scenarioLearn about various problems in image captioning and how to overcome them by training images and text togetherImplement similarity matching and train a model for face recognitionUnderstand the concept of generative models and use them for image generationDeploy your deep learning models and optimize them for high performanceWho This Book Is ForThis book is targeted at data scientists and Computer Vision practitioners who wish to apply the concepts of Deep Learning to overcome any problem related to Computer Vision. A basic knowledge of programming in Python-and some understanding of machine learning concepts-is required to get the best out of this book.Table of ContentsIntroduction to Deep LearningImage ClassificationImage RetrievalObject DetectionSemantic SegmentationSimilarity LearningGenerative ModelsImage CaptioningVideo ClassificationDeployment Biografía del autor Rajalingappaa Shanmugamani is currently working as a Deep Learning Lead at SAP, Singapore. Previously, he has worked and consulted at various startups for developing computer vision products. He has a Masters from Indian Institute of Technology - Madras where his thesis was based on applications of computer vision in the manufacturing industry. He has published articles in peer-reviewed journals and conferences and applied for few patents in the area of machine learning. In his spare time, he coaches programming and machine learning to school students and engineers.

Detalles del Libro

• Name: Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using TensorFlow and Keras
  
• Autor: Rajalingappaa Shanmugamani
  
• Categoria: Libros,Libros universitarios y de estudios superiores,Ciencias informáticas
  
• Tamaño del archivo: 18 MB
  
• Tipos de archivo: PDF Document
  
• Descargada: 714 times
  
• Idioma: Español
  
• Archivos de estado: AVAILABLE
  

Descarga Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using TensorFlow and Keras de Rajalingappaa Shanmugamani Libro PDF

Deep Learning for Computer Vision - Rajalingappa ~ Deep Learning for Computer Vision. Expert techniques to train advanced neural networks using TensorFlow and Keras. Rajalingappaa Shanmugamani BIRMINGHAM . Keras can be installed separately or used within TensorFlow itself using the tf.keras API. In this book, we will use the tf.keras API.

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Deep Learning for Computer Vision: Shanmugamani ~ Learn how to model and train advanced neural networks to implement a variety of Computer Vision tasksKey FeaturesTrain different kinds of deep learning model from scratch to solve specific problems in Computer VisionCombine the power of Python, Keras, and TensorFlow to build deep learning models for object detection, image classification, similarity learning, image captioning, and moreIncludes .

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Deep Learning with TensorFlow 2 and Keras: Regression ~ Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available.

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Imagenet Bundle Deep Learning For Computer Vision With Python ~ ImageNet Bundle eBook, videos, source code, etc. Addressing Challenges in Deep Learning for Computer Vision Challenge Managing large sets of labeled images Resizing, Data augmentation Background in neural networks (deep learning) Computation intensive task (requires GPU) Solution imageSet or imageDataStore to handle large sets of images imresize, imcrop, imadjust, imageInputLayer, etc. Click .

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MATLAB for Deep Learning - MATLAB & Simulink ~ With just a few lines of MATLAB code, you can build deep learning models and perform deep learning tasks.

Mafijul Bhuiyan - Computer vision researcher - Huawei ~ Experienced in implementing and customizing different deep neural network architectures used in computer vision and natural language processing. Developed scientific software packages for solving data science machine learning problems in C, C++17, MATLAB, Python (SciPy/NumPy/OpenCV), Pytorch, and Keras.

Age Detection using Facial Images: traditional Machine ~ Two approaches: traditional Machine Learning vs. Deep Learning — Heeding to my instructor’s suggestion, I attempted to build a solution for this project using two separate approaches. Most of the resources I came across online dove straight into deep learning and neural networks to build solutions to this problem (which you may say should be the right approach anyway).

Faizan Ishfaq - Machine Learning and Computer Vision ~ • Development of Machine Learning/Computer Vision based intelligent solutions and prototypes. • Implementation of Deep Learning based models for computer vision problems. • Development of a real-time object tracking and activities recognition tool for automated surveillance system based on Python, OpenCV, Darknet-YOLO.