Packt Publishing, 2018. — 368 p. — ASIN B078N8RDCP. A comprehensive guide to advanced deep learning techniques, including Autoencoders, GANs, VAEs, and Deep Reinforcement Learning, that drive today's most impressive AI results Key Features Explore the most advanced deep learning techniques that drive modern AI results Implement Deep Neural Networks, Autoencoders, GANs, VAEs,...
Packt Publishing, 2018. — 368 p. — ASIN B078N8RDCP. !Code files only. A comprehensive guide to advanced deep learning techniques, including Autoencoders, GANs, VAEs, and Deep Reinforcement Learning, that drive today's most impressive AI results Key Features Explore the most advanced deep learning techniques that drive modern AI results Implement Deep Neural Networks,...
Packt Publishing, 2019. — 558 p. — ISBN: 1789346649. This book will take you from the basics of neural networks to advanced implementations of architectures using a recipe-based approach. We will learn about how neural networks work and the impact of various hyper parameters on a network's accuracy along with leveraging neural networks for structured and unstructured data....
Packt Publishing, 2019. — 558 p. — ISBN: 1789346649. !Code files only This book will take you from the basics of neural networks to advanced implementations of architectures using a recipe-based approach. We will learn about how neural networks work and the impact of various hyper parameters on a network's accuracy along with leveraging neural networks for structured and...
Packt Publishing, 2019. — 412 p. — ISBN: 978-1-83855-507-8. Take your neural networks to a whole new level with the simplicity and modularity of Keras, the most commonly used high-level neural networks API. Though designing neural networks is a sought-after skill, it is not easy to master. With Keras, you can apply complex machine learning algorithms with minimum code. Applied...
Packt, 2018. — 386 p. — ISBN: 1789536642. Keras 2.x Projects explains how to leverage the power of Keras to build and train state-of-the-art deep learning models through a series of practical projects that look at a range of real-world application areas. To begin with, you will quickly set up a deep learning environment by installing the Keras library. Through each of the...
Packt, 2018. — 386 p. — ISBN: 1789536642. !Code files only. Keras 2.x Projects explains how to leverage the power of Keras to build and train state-of-the-art deep learning models through a series of practical projects that look at a range of real-world application areas. To begin with, you will quickly set up a deep learning environment by installing the Keras library. Through...
3e édition. — Dunod, 2024. — 626 p. — ISBN 9782100847693. L’objectif de cet ouvrage est de vous expliquer les concepts fondamentaux du Deep Learning et de vous montrer, grâce à de nombreux exemples de code accessibles en ligne, comment les mettre en pratique. La 3e édition de cet ouvrage de référence, très remaniée, tient compte des récentes avancées. - Construire et entraîner...
Packt Publishing, 2017. — 318 p. — ISBN: 978-1-78712-842-2. True PDF Get to grips with the basics of Keras to implement fast and efficient deep-learning models This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image...
Independently published, 2021. — 68 p. — ASIN : B0947GKFSZ. This book will introduce you to various supervised and unsupervised deep learning algorithms like the multilayer perceptron, linear regression, and other more advanced deep convolutional and recurrent neural networks. You will also learn about image processing, handwritten recognition, object recognition, and much...
Amazon Digital Services LLC, 2018. — 189 р. This introduction will help you develop a good understanding of deep learning completely from scratch This book covers: Introduction to machine learning and deep learning Math for deep learning explained to the layman How neural networks work: a general overview Activation functions in deep networks Loss functions Weight...
2nd. ed. - Birmingham: Packt Publishing, 2020. - 444 p. - ISBN: 183921757X. Cut through the noise and get real results with a step-by-step approach to understanding deep learning with Keras programming ! Key Features Ideal for those getting started with Keras for the first time . A step-by-step Keras tutorial with exercises and activities that help build key skills. Structured...
3rd ed. — Packt, 2020. — 495 p. — ISBN: 978-1800562967. Discover how to leverage Keras, the powerful and easy-to-use open-source Python library for developing and evaluating deep learning models Key Features Get to grips with various model evaluation metrics, including sensitivity, specificity, and AUC scores Explore advanced concepts such as sequential memory and sequential...
Apress, 2019. — 182 p. Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras. The overall book comprises three sections with...
Reactive Publishing, 2024. — 654 p. In an era where Artificial Intelligence is revolutionizing industries, mastering the tools and techniques to harness its potential is essential. Mastering Keras: Building Advanced Deep Learning Models with Python is your comprehensive guide to diving deep into the world of neural networks and Deep Learning using Keras, one of the most...
Wiley, 2019. — 313 p. — ISBN: 978-1-119-56486-7. Build a Keras model to scale and deploy on a Kubernetes cluster We have seen an exponential growth in the use of Artificial Intelligence (AI) over last few years. AI is becoming the new electricity and is touching every industry from retail to manufacturing to healthcare to entertainment. Within AI, were seeing a particular...
М.: ДМК Пресс, 2017. — 294 с. — ISBN: 978-5-97060-573-8. Книга представляет собой краткое, но обстоятельное введение в современные нейронные сети, искусственный интеллект и технологии глубокого обучения. В ней представлено более 20 работоспособных нейронных сетей, написанных на языке Python с использованием модульной библиотеки Keras, работающей поверх библиотек TensorFlow от...
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