{"product_id":"advanced-applied-deep-learning-convolutional-neural-networks-and-object-detection-paperback","title":"Advanced Applied Deep Learning: Convolutional Neural Networks and Object Detection - Paperback","description":"\u003cp\u003eby \u003cb\u003eUmberto Michelucci\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eDevelop and optimize deep learning models with advanced architectures. This book teaches you the intricate details and subtleties of the algorithms that are at the core of convolutional neural networks. In \u003ci\u003eAdvanced Applied Deep Learning\u003c\/i\u003e, you will study advanced topics on CNN and object detection using Keras and TensorFlow. \u003c\/p\u003e Along the way, you will look at the fundamental operations in CNN, such as convolution and pooling, and then look at more advanced architectures such as inception networks, resnets, and many more. While the book discusses theoretical topics, you will discover how to work efficiently with Keras with many tricks and tips, including how to customize logging in Keras with custom callback classes, what is eager execution, and how to use it in your models. \u003cp\u003e\u003c\/p\u003e Finally, you will study how object detection works, and build a complete implementation of the YOLO (you only look once) algorithm in Keras and TensorFlow. By the end of the book you will have implemented various models in Keras and learned many advanced tricks that will bring your skills to the next level.\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eWhat You Will Learn\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eSee how convolutional neural networks and object detection work\u003c\/li\u003e\n\u003cli\u003eSave weights and models on disk\u003c\/li\u003e\n\u003cli\u003ePause training and restart it at a later stage \u003c\/li\u003e\n\u003cli\u003eUse hardware acceleration (GPUs) in your code\u003c\/li\u003e\n\u003cli\u003eWork with the Dataset TensorFlow abstraction and use pre-trained models and transfer learning\u003c\/li\u003e\n\u003cli\u003eRemove and add layers to pre-trained networks to adapt them to your specific project\u003c\/li\u003e\n\u003cli\u003eApply pre-trained models such as Alexnet and VGG16 to new datasets\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003eWho This Book Is For\u003c\/b\u003e\u003c\/p\u003e Scientists and researchers with intermediate-to-advanced Python and machine learning know-how. Additionally, intermediate knowledge of Keras and TensorFlow is expected. \u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eDevelop and optimize deep learning models with advanced architectures. This book teaches you the intricate details and subtleties of the algorithms that are at the core of convolutional neural networks. In \u003ci\u003eAdvanced Applied Deep Learning\u003c\/i\u003e, you will study advanced topics on CNN and object detection using Keras and TensorFlow. \u003c\/p\u003e\u003cp\u003eAlong the way, you will look at the fundamental operations in CNN, such as convolution and pooling, and then look at more advanced architectures such as inception networks, resnets, and many more. While the book discusses theoretical topics, you will discover how to work efficiently with Keras with many tricks and tips, including how to customize logging in Keras with custom callback classes, what is eager execution, and how to use it in your models.\u003c\/p\u003e\u003cp\u003eFinally, you will study how object detection works, and build a complete implementation of the YOLO (you only look once) algorithm in Keras and TensorFlow. By the end of the book you will have implemented various models in Keras and learned many advanced tricks that will bring your skills to the next level.\u003c\/p\u003e\u003cbr\u003e\u003cp\u003eYou will: \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eSee how convolutional neural networks and object detection work\u003c\/li\u003e\n\u003cli\u003eSave weights and models on disk\u003c\/li\u003e\n\u003cli\u003ePause training and restart it at a later stage\u003c\/li\u003e\n\u003cli\u003eUse hardware acceleration (GPUs) in your code\u003c\/li\u003e\n\u003cli\u003eWork with the Dataset TensorFlow abstraction and use pre-trained models and transfer learning\u003c\/li\u003e\n\u003cli\u003eRemove and add layers to pre-trained networks to adapt them to your specific project\u003c\/li\u003e\n\u003cli\u003eApply pre-trained models such as Alexnet and VGG16 to new datasets\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003eUmberto Michelucci studied physics and mathematics. He is an expert in numerical simulation, statistics, data science, and machine learning. In addition to several years of research experience at the George Washington University (USA) and the University of Augsburg (DE), he has 15 years of practical experience in the fields of data warehouse, data science, and machine learning. His last book \u003ci\u003eApplied Deep Learning - A Case-Based Approach to Understanding Deep Neural Networks \u003c\/i\u003ewas published by Apress in 2018. He is very active in research in the field of artificial intelligence and publishes his research results regularly in leading journals and gives regular talks at international conferences.\u003cbr\u003eHe teaches as a lecturer at the Zurich University of Applied Sciences and at the HWZ University of Applied Sciences in Business Administration. He is also responsible for AI, research, and new technologies at Helsana Vesicherung AG.\u003cbr\u003eHe recently founded TOELT LLC, a company aiming to develop new and modern teaching, coaching, and research methods for AI, to make AI technologies and research accessible to everyone.\u003cbr\u003e\u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 285\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.64 x 9.21 x 6.14 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e September 29, 2019\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":42717113909311,"sku":"9781484249758","price":58.3,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0105\/8226\/1823\/files\/b34dbd4ea0eae726e729e975c6d478d6.webp?v=1765077349","url":"https:\/\/dhlswag.com\/products\/advanced-applied-deep-learning-convolutional-neural-networks-and-object-detection-paperback","provider":"BBB","version":"1.0","type":"link"}