Math and Architectures of Deep Learning, Krishnendu Chaudhury, Ananya H. Ashok, Sujay Narumanchi, Devashish

Name: Math and Architectures of Deep Learning, Krishnendu Chaudhury, Ananya H. Ashok, Sujay Narumanchi, Devashish
Brand: Author
SKU: sku255171
Price: 810 UAH
Availability: InStock

Код: sku255171

В наличии

810 ₴

Продавец Ридит

99%

New

Оплатить частями

Доставка

Подписка на доставку Smart
Бесплатно — в отделения Новой почты
Узнать больше
Нова Пошта (Бесплатно при условии)

Оплата и гарантии

Безопасная оплата картой
Без переплат
Prom гарантирует безопасность
Вернем деньги при отказе от посылки
Оплатить частями
Без переплат*, от 405 ₴/мес.
Подробнее
Наложенный платеж
Нова Пошта
Оплата на счет
IBAN UA943052990000026009026215754

Math and Architectures of Deep Learning, Krishnendu Chaudhury, Ananya H. Ashok, Sujay Narumanchi, Devashish - фото 1 - id-p2629994708

Характеристики и описание

Основные

Производитель	Author

Пользовательские характеристики

Друк	чорно-білий
Язык	English
Папір	білий, офсет
Состояние	нова книга

Math and Architectures of Deep Learning, Krishnendu Chaudhury, Ananya H. Ashok, Sujay Narumanchi, Devashish Shankar, more купить книгу в Україні

Обкладинка - м"яка

Рік видання - 2024

Кількість сторінок - 552

Папір - білий, офсет

Про книгу Math and Architectures of Deep Learning, Krishnendu Chaudhury, Ananya H. Ashok, Sujay Narumanchi, Devashish Shankar, more

Shine a spotlight into the deep learning “black box”. This comprehensive and detailed guide reveals the mathematical and architectural concepts behind deep learning models, so you can customize, maintain, and explain them more effectively.

Inside Math and Architectures of Deep Learning you will find:

Math, theory, and programming principles side by side
Linear algebra, vector calculus and multivariate statistics for deep learning
The structure of neural networks
Implementing deep learning architectures with Python and PyTorch
Troubleshooting underperforming models
Working code samples in downloadable Jupyter notebooks

The mathematical paradigms behind deep learning models typically begin as hard-to-read academic papers that leave engineers in the dark about how those models actually function. Math and Architectures of Deep Learning bridges the gap between theory and practice, laying out the math of deep learning side by side with practical implementations in Python and PyTorch. Written by deep learning expert Krishnendu Chaudhury, you’ll peer inside the “black box” to understand how your code is working, and learn to comprehend cutting-edge research you can turn into practical applications.

Foreword by Prith Banerjee.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology

Discover what’s going on inside the black box! To work with deep learning you’ll have to choose the right model, train it, preprocess your data, evaluate performance and accuracy, and deal with uncertainty and variability in the outputs of a deployed solution. This book takes you systematically through the core mathematical concepts you’ll need as a working data scientist: vector calculus, linear algebra, and Bayesian inference, all from a deep learning perspective.

About the book

Math and Architectures of Deep Learning teaches the math, theory, and programming principles of deep learning models laid out side by side, and then puts them into practice with well-annotated Python code. You’ll progress from algebra, calculus, and statistics all the way to state-of-the-art DL architectures taken from the latest research.

What's inside

The core design principles of neural networks
Implementing deep learning with Python and PyTorch
Regularizing and optimizing underperforming models

About the reader

Readers need to know Python and the basics of algebra and calculus.

About the author

Krishnendu Chaudhury is co-founder and CTO of the AI startup Drishti Technologies. He previously spent a decade each at Google and Adobe.

Table of Contents

1 An overview of machine learning and deep learning
2 Vectors, matrices, and tensors in machine learning
3 Classifiers and vector calculus
4 Linear algebraic tools in machine learning
5 Probability distributions in machine learning
6 Bayesian tools for machine learning
7 Function approximation: How neural networks model the world
8 Training neural networks: Forward propagation and backpropagation
9 Loss, optimization, and regularization
10 Convolutions in neural networks
11 Neural networks for image classification and object detection
12 Manifolds, homeomorphism, and neural networks
13 Fully Bayes model parameter estimation
14 Latent space and generative modeling, autoencoders, and variational autoencoders
A Appendix

Math and Architectures of Deep Learning, Krishnendu Chaudhury, Ananya H. Ashok, Sujay Narumanchi, Devashish Shankar, more

Також купити цю книгу Ви можете по посиланню

Был online: Сегодня

Ридит

99% положительных отзывов