Tensorflow neural network design (25 resultados)

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paperback. Condición: New. Language:Chinese.Paperback. Pub Date: 2024-09 Pages: 168 Publisher: Tsinghua University Press This book first introduces deep learning and compares it with other machine learning models. explaining techniques complementary to TensorFlow for creating deep learning models. such as Panda. Scikit-Learn. and NumPy. It then introduces supervised deep learning models. building shallow neural networks using multiple perceptrons in a single layer. and creating real-world applications using TensorFlow . …

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Taschenbuch. Condición: Neu. Automated Deep Learning Using Neural Network Intelligence | Develop and Design Pytorch and Tensorflow Models Using Python | Ivan Gridin | Taschenbuch | xvii | Englisch | 2022 | Apress | EAN 9781484281482 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Condición: Hervorragend. Zustand: Hervorragend | Seiten: 384 | Sprache: Englisch | Produktart: Bücher | Intermediate-Advanced user level.

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PF. Condición: New. This item is printed on demand. Brand new book, sourced directly from publisher. Dispatch time is 6-7 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Optimize, develop, and design PyTorch and TensorFlow models for a specific problem using the Microsoft Neural Network Intelligence (NNI) toolkit. This book includes practical examples illustrating automated deep learning approaches and provides techniques to facilitate your deep learning model development. The first chapters of this book cover the basics of NNI toolkit usage and methods for solving hyper-parameter optimization tasks. You will understand the black-box function maximization problem using NNI, and know how to prepare a TensorFlow or PyTorch model for hyper-parameter tuning, launch an experiment, and interpret the results. The book dives into optimization tuners and the search algorithms they are based on: Evolution search, Annealing search, and the Bayesian Optimization approach. The Neural Architecture Search is covered and you will learn how to develop deep learning models from scratch. Multi-trial and one-shot searching approaches of automatic neural network design are presented. The book teaches you how to construct a search space and launch an architecture search using the latest state-of-the-art exploration strategies: Efficient Neural Architecture Search (ENAS) and Differential Architectural Search (DARTS). You will learn how to automate the construction of a neural network architecture for a particular problem and dataset. The book focuses on model compression and feature engineering methods that are essential in automated deep learning. It also includes performance techniques that allow the creation of large-scale distributive training platforms using NNI. After reading this book, you will know how to use the full toolkit of automated deep learning methods. The techniques and practical examples presented in this book will allow you to bring your neural network routines to a higher level.What You Will LearnKnow the basic concepts of optimization tuners, search space, and trialsApply different hyper-parameter optimization algorithms to develop effective neural networksConstruct new deep learning models from scratchExecute the automated Neural Architecture Search to create state-of-the-art deep learning modelsCompress the model to eliminate unnecessary deep learning layersWho This Book Is ForIntermediate to advanced data scientists and machine learning engineers involved in deep learning and practical neural network development.…

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Explore the world of neural networks by building powerful deep learning models using the R ecosystem Key Features:- Get to grips with the fundamentals of deep learning and neural networks - Use R 3.5 and its libraries and APIs to build deep learning models for computer vision and text processing - Implement effective deep learning systems in R with the help of end-to-end projects Book Description:Deep learning has a range of practical applications in several domains, while R is the preferred language for designing and deploying deep learning models.This Learning Path introduces you to the basics of deep learning and even teaches you to build a neural network model from scratch. As you make your way through the chapters, you'll explore deep learning libraries and understand how to create deep learning models for a variety of challenges, right from anomaly detection to recommendation systems. The Learning Path will then help you cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud, in addition to model optimization, overfitting, and data augmentation. Through real-world projects, you'll also get up to speed with training convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs) in R.By the end of this Learning Path, you'll be well-versed with deep learning and have the skills you need to implement a number of deep learning concepts in your research work or projects. What You Will Learn:- Implement credit card fraud detection with autoencoders - Train neural networks to perform handwritten digit recognition using MXNet - Reconstruct images using variational autoencoders - Explore the applications of autoencoder neural networks in clustering and dimensionality reduction - Create natural language processing (NLP) models using Keras and TensorFlow in R - Prevent models from overfitting the data to improve generalizability - Build shallow neural network prediction models Who this book is for:This Learning Path is for aspiring data scientists, data analysts, machine learning developers, and deep learning enthusiasts who are well versed in machine learning concepts and are looking to explore the deep learning paradigm using R. A fundamental understanding of R programming and familiarity with the basic concepts of deep learning are necessary to get the most out of this Learning Path.…

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Taschenbuch. Condición: Neu. Deep Learning with R for Beginners | Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet | Mark Hodnett (u. a.) | Taschenbuch | Kartoniert / Broschiert | Englisch | 2019 | Packt Publishing | EAN 9781838642709 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. …