45 products were found matching your search for Guide to Convolutional Neural in 1 shops:

This book covers the fundamentals in designing and deploying techniques using deep architectures. It is intended to serve as a beginner's guide to engineers or students who want to have a quick start on learning and/or building deep learning systems. This book provides a good theoretical and practical understanding and a complete toolkit of basic information and knowledge required to understand and build convolutional neural networks (CNN) from scratch. The book focuses explicitly on convolutional neural networks, filtering out other material that co-occur in many deep learning books on CNN topics.

Develop 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 Advanced Applied Deep Learning, you will study advanced topics on CNN and object detection using Keras and TensorFlow.  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. 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.What You Will Learn See how convolutional neural networks and object detection workSave weights and models on diskPause training and restart it at a later stage Use hardware acceleration (GPUs) in your codeWork with the Dataset TensorFlow abstraction and use pre-trained models and transfer learningRemove and add layers to pre-trained networks to adapt them to your specific projectApply pre-trained models such as Alexnet and VGG16 to new datasets   Who This Book Is For Scientists and researchers with intermediate-to-advanced Python and machine learning know-how. Additionally, intermediate knowledge of Keras and TensorFlow is expected.

Develop 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 Advanced Applied Deep Learning, you will study advanced topics on CNN and object detection using Keras and TensorFlow.  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. 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.What You Will Learn See how convolutional neural networks and object detection workSave weights and models on diskPause training and restart it at a later stage Use hardware acceleration (GPUs) in your codeWork with the Dataset TensorFlow abstraction and use pre-trained models and transfer learningRemove and add layers to pre-trained networks to adapt them to your specific projectApply pre-trained models such as Alexnet and VGG16 to new datasets   Who This Book Is For Scientists and researchers with intermediate-to-advanced Python and machine learning know-how. Additionally, intermediate knowledge of Keras and TensorFlow is expected.

Unread book in perfect condition.

Unread book in perfect condition.

This book covers the fundamentals in designing and deploying techniques using deep architectures. It is intended to serve as a beginner's guide to engineers or students who want to have a quick start on learning and/or building deep learning systems. This book provides a good theoretical and practical understanding and a complete toolkit of basic information and knowledge required to understand and build convolutional neural networks (CNN) from scratch. The book focuses explicitly on convolutional neural networks, filtering out other material that co-occur in many deep learning books on CNN topics.

Most items will be dispatched the same or the next working day. An apparently unread copy in perfect condition. Dust cover is intact with no nicks or tears. Spine has no signs of creasing. Pages are clean and not marred by notes or folds of any kind.

New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Book is in NEW condition. 0.65

This is the first practical guide that enables you to actually work with artificial neural networks on your personal computer. It provides basic information on neural networks, as well as the following special features: source code listings in C**actual case studies in a wide range of applications, including radar signal detection, stock market prediction, musical composition, ship pattern recognition, and biopotential waveform classification**CASE tools for neural networks and hybrid expert system/neural networks**practical hints and suggestions on when and how to use neural network tools to solve real-world problems.

Book is in NEW condition. 0.92

Bringing mindfulness techniques to your psychotherapeutic work with clients. An integrated state of mindful awareness is crucial to achieving mental health. Daniel J. Siegel, an internationally recognized expert on mindfulness and therapy, reveals practical techniques that enable readers to harness their energies to promote healthy minds within themselves and their clients. He charts the nine integrative functions that emerge from the profoundly interconnecting circuits of the brain, including bodily regulation, attunement, emotional balance, response flexibility, fear extinction, insight, empathy, morality, and intuition. A practical, direct-immersion, high-emotion, low-techno-speak book, The Mindful Therapist engages readers in a personal and professional journey into the ideas and process of mindful integration that lie at the heart of health and nurturing relationships.

Pages are clean and are not marred by notes or folds of any kind. ~ ThriftBooks: Read More, Spend Less 0.71

Item in very good condition! Textbooks may not include supplemental items i.e. CDs, access codes etc.

Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB Deep Learning employs MATLAB as the underlying programming language and tool for the examples and case studies in this book.  With this book, you'll be able to tackle some of today's real world big data, smart bots, and other complex data problems. You’ll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage.What You'll LearnUse MATLAB for deep learningDiscover neural networks and multi-layer neural networksWork with convolution and pooling layersBuild a MNIST example with these layersWho This Book Is ForThose who want to learn deep learning using MATLAB. Some MATLAB experience may be useful.

Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB Deep Learning employs MATLAB as the underlying programming language and tool for the examples and case studies in this book.  With this book, you'll be able to tackle some of today's real world big data, smart bots, and other complex data problems. You’ll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage.What You'll LearnUse MATLAB for deep learningDiscover neural networks and multi-layer neural networksWork with convolution and pooling layersBuild a MNIST example with these layersWho This Book Is ForThose who want to learn deep learning using MATLAB. Some MATLAB experience may be useful.

Neural Networks in C++ An Object-Oriented Framework for Building Connectionist Systems Extremely useful, this valuable guide concentrates on the practical side of building neural network applications. Written with a wealth of useful examples in C++, the book provides you with the nuts-and-bolts guidelines for hands-on development of real-world connectionist systems. Neural Networks in C++ also: Includes a complete C++ class library especially designed for neural network applications Fully covers three major application areas of neural network programming—image recognition, text processing, and forecasting Provides introductory material on neural networks Covers the basics on an object-oriented framework for connectionist systems

A comprehensive guide to the conceptual, mathematical, and implementational aspects of analyzing electrical brain signals, including data from MEG, EEG, and LFP recordings.This book offers a comprehensive guide to the theory and practice of analyzing electrical brain signals. It explains the conceptual, mathematical, and implementational (via Matlab programming) aspects of time-, time-frequency- and synchronization-based analyses of magnetoencephalography (MEG), electroencephalography (EEG), and local field potential (LFP) recordings from humans and nonhuman animals. It is the only book on the topic that covers both the theoretical background and the implementation in language that can be understood by readers without extensive formal training in mathematics, including cognitive scientists, neuroscientists, and psychologists.Readers who go through the book chapter by chapter and implement the examples in Matlab will develop an understanding of why and how analyses are performed, how to interpret results, what the methodological issues are, and how to perform single-subject-level and group-level analyses. Researchers who are familiar with using automated programs to perform advanced analyses will learn what happens when they click the “analyze now” button.The book provides sample data and downloadable Matlab code. Each of the 38 chapters covers one analysis topic, and these topics progress from simple to advanced. Most chapters conclude with exercises that further develop the material covered in the chapter. Many of the methods presented (including convolution, the Fourier transform, and Euler's formula) are fundamental and form the groundwork for other advanced data analysis methods. Readers who master the methods in the book will be well prepared to learn other approaches.

This book is a practical guide to classification learning systems and their applications. These computer programs learn from sample data and make predictions for new cases, sometimes exceeding the performance of humans.Practical learning systems from statistical pattern recognition, neural networks, and machine learning are presented. The authors examine prominent methods from each area, using an engineering approach and taking the practitioner's viewpoint. Intuitive explanations with a minimum of mathematics make the material accessible to anyone--regardless of experience or special interests.The underlying concepts of the learning methods are discussed with fully worked-out examples: their strengths and weaknesses, and the estimation of their future performance on specific applications. Throughout, the authors offer their own recommendations for selecting and applying learning methods such as linear discriminants, back-propagation neural networks, or decision trees. Learning systems are then contrasted with their rule-based counterparts from expert systems.

Stahl's Illustrated Violence is a concise and highly illustrated guide to the underlying neurobiology, genetic predisposition and management of aggressive behaviours in patients with psychiatric disorders. All of the titles in the Stahl's Illustrated series are designed to be fun. Concepts are illustrated by full-color images that will be familiar to readers of Stahl's Essential Psychopharmacology, 4th edition, and The Prescriber's Guide. The visual learner will find that these books make psychopharmacology concepts easy to master, while the non-visual learner will enjoy a shortened text version of complex psychopharmacology concepts. Each chapter builds on previous ones, synthesizing information from basic biology and diagnostics to building treatment plans and dealing with complications and comorbidities. Novices may want to begin by looking through all the graphics and gaining a feel for the visual vocabulary. Readers more familiar with these topics should find that going back and forth between images and text provides an interaction with which to vividly conceptualize complex pharmacologies. Each book ends with a Suggested Reading section to help guide more in-depth learning about particular concepts.