Michelucci, Umberto Applied deep learning 9781484237892

Варианты приобретения

Цена: 46570.00T
Кол-во:
о цене
Наличие: Невозможна поставка.

в Мои желания

Автор: Michelucci, Umberto
Название: Applied deep learning
ISBN: 9781484237892
Издательство: Springer
Классификация:

Функциональный анализ и преобразования

Компьютерное программирование / разработка по

Парадигмы программирования / Обучение программированию

Языки программирования и описания сценариев: общие сведения

Базы данных

Искусственный интеллект

ISBN-10: 1484237897
Обложка/Формат: Paperback
Страницы: 410
Вес: 0.82 кг.
Дата издания: 08.09.2018
Язык: English
Издание: 1st ed.
Иллюстрации: 58 illustrations, color; 117 illustrations, black and white; xviii, 371 p. 175 illus., 58 illus. in color.
Размер: 180 x 254 x 28
Читательская аудитория: General (us: trade)
Подзаголовок: A case-based approach to understanding deep neural networks
Ссылка на Издательство: Link
Рейтинг:
Поставляется из: Германии
Описание: Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You’ll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and Swish), seeing how to perform linear and logistic regression using TensorFlow, and choosing the right cost function. The next section talks about more complicated neural network architectures with several layers and neurons and explores the problem of random initialization of weights. An entire chapter is dedicated to a complete overview of neural network error analysis, giving examples of solving problems originating from variance, bias, overfitting, and datasets coming from different distributions. Applied Deep Learning also discusses how to implement logistic regression completely from scratch without using any Python library except NumPy, to let you appreciate how libraries such as TensorFlow allow quick and efficient experiments. Case studies for each method are included to put into practice all theoretical information. You’ll discover tips and tricks for writing optimized Python code (for example vectorizing loops with NumPy). What You Will LearnImplement advanced techniques in the right way in Python and TensorFlowDebug and optimize advanced methods (such as dropout and regularization)Carry out error analysis (to realize if one has a bias problem, a variance problem, a data offset problem, and so on)Set up a machine learning project focused on deep learning on a complex datasetWho This Book Is ForReaders with a medium understanding of machine learning, linear algebra, calculus, and basic Python programming.
Дополнительное описание: Chapter 1: Introduction.- Chapter 2: Single Neurons.- Chapter 3: Fully connected Neural Network with more neurons.- Chapter 4: Neural networks error analysis.- Chapter 5: Dropout technique.- Chapter 6: Hyper parameters tuning.- Chapter 7: Tensorflo

Applied deep learning, Michelucci, Umberto