Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide : A practical guide to building neural networks using Microsoft's open source deep learning framework

Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide : A practical guide to building neural networks using Microsoft's open source deep learning framework

Enregistré dans:

Détails bibliographiques
Auteur principal:	Meints, Willem. (Auteur)
Support:	E-Book
Langue:	Anglais
Publié:	Birmingham : Packt Publishing, 2019.
Autres localisations:	Voir dans le Sudoc
Accès en ligne:	Accès à l'E-book

Documents similaires

Deep learning with PyTorch quick start guide : learn to train and deploy neural network models in Python
par: Julian, David.
Publié: (2018)

Deep learning quick reference : useful hacks for training and optimizing deep neural networks with TensorFlow and Keras
par: Bernico, Mike.
Publié: (2018)

Deep learning : practical neural networks with Java
par: Sugomori, Yusuke.
Publié: (2017)

Deep learning essentials : your hands-on guide to the fundamentals of deep learning and neural network modeling
par: Di, Wei.
Publié: (2018)

Hands-On Deep Learning with Go : A practical guide to building and implementing neural network models using Go
par: Seneque, Gareth.
Publié: (2019)

Hands-On Deep Learning with R : A practical guide to designing, building, and improving neural network models using R
par: Pawlus, Michael.
Publié: (2020)

Caffe2 Quick Start Guide : Modular and scalable deep learning made easy
par: Nanjappa, Ashwin.
Publié: (2019)

Deep learning with PyTorch : a practical approach to building neural network models using PyTorch
par: Subramanian, Vishnu (19..-....).
Publié: (2018)

Python deep learning projects : 9 projects demystifying neural network and deep learning models for building intelligent systems
par: Lamons, Matthew.
Publié: (2018)

Getting started with Deep Learning for Natural Language Processing : Learn how to build NLP applications with Deep Learning (English Edition)
par: Patel, Sunil.

Deep learning by example : a hands-on guide to implementing advanced machine learning algorithms and neural networks
par: Menshawy, Ahmed.
Publié: (2018)

Codeless Deep Learning with KNIME : Build, train, and deploy various deep neural network architectures using KNIME Analytics Platform
par: Melcher, Kathrin.
Publié: (2020)

Deep Learning for Robot Perception and Cognition
par: Iosifidis, Alexandros.
Publié: (2022)

Microsoft dynamics NAV development quick start guide : get up and running with Microsoft Dynamics NAV
par: Drogin, Alexander.
Publié: (2018)

Mastering the Microsoft deployment toolkit
par: Stokes, Jeff.
Publié: (2016)

Hands-On Mathematics for Deep Learning : Build a solid mathematical foundation for training eﬃcient deep neural networks
par: Dawani, Jay.
Publié: (2020)

Python deep learning : exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow
Publié: (2019)

Hands-on deep learning for games : leverage the power of neural networks and reinforcement learning to build intelligent games
par: Lanham, Micheal.
Publié: (2019)

Deep learning with TensorFlow : explore neural networks and build intelligent systems with Python
par: Zaccone, Giancarlo.
Publié: (2018)

Keras deep learning cookbook : over 30 recipes for implementing deep neural networks in Python
par: Dua, Rajdeep.
Publié: (2018)

The deep learning with PyTorch workshop : build deep neural networks and artificial intelligence applications with PyTorch
par: Saleh, Hyatt.
Publié: (2020)

Learning Microsoft cognitive services
par: Larsen, Leif.
Publié: (2017)

Machine Learning with Go Quick Start Guide : Hands-on techniques for building supervised and unsupervised machine learning workﬂows
par: Bironneau, Michael.
Publié: (2019)

Python Deep Learning : Understand how deep neural networks work and apply them to real-world tasks
par: Vasilev, Ivan.

Deep learning with Keras : implement neural networks with Keras on Theano and TensorFlow
par: Gulli, Antonio.
Publié: (2017)

Recurrent neural networks with Python quick start guide : sequential learning and language modeling with TensorFlow
par: Kostadinov, Simeon.
Publié: (2018)

Practical Mathematics for AI and Deep Learning : A Concise yet In-Depth Guide on Fundamentals of Computer Vision, NLP, Complex Deep Neural Networks and Machine Learning (English Edition)
par: Ghosh, Tamoghna.

Advanced Deep Learning with R : Become an expert at designing, building, and improving advanced neural network models using R
par: Rai, Bharatendra.
Publié: (2019)

Machine learning with scikit-learn quick start guide : classification, regression, and clustering techniques in Python
par: Jolly, Kevin.
Publié: (2018)

Java Deep Learning Cookbook : Train neural networks for classiﬁcation, NLP, and reinforcement learning using Deeplearning4j
par: Raj, Rahul.
Publié: (2019)

Hands-On Deep Learning Architectures with Python : Create deep neural networks to solve computational problems using TensorFlow and Keras
par: Liu, Yuxi (Hayden).
Publié: (2019)

Deep learning
par: Kelleher, John D. (1974-....).
Publié: (2019)

Deep Learning for Beginners : A beginner's guide to getting up and running with deep learning from scratch using Python
par: Rivas, Pablo.
Publié: (2020)

R deep learning essentials : a step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet
par: Hodnett, Mark.
Publié: (2018)

Java deep learning projects : implement 10 real-world deep learning applications using Deeplearning4j and open source APIs
par: Karim, Rezaul Md..
Publié: (2018)

Machine Learning with R Quick Start Guide : A beginner's guide to implementing machine learning techniques from scratch using R 3.5
par: Pastor Sanz, Iván.
Publié: (2019)

Beginning with Deep Learning using Tensorflow : A Beginners Guide to TensorFlow and Keras for Practicing Deep Learning Principles and Applications
par: Silaparasetty, Mohan Kumar.
Publié: (2022)

Deep learning for computer vision : expert techniques to train advanced neural networks using TensorFlow and Keras
par: Shanmugamani, Rajalingappaa (19..-....).
Publié: (2018)

Truffle Quick Start Guide : Learn the fundamentals of Ethereum development
par: Bhaskar, Nikhil.
Publié: (2018)

Applied Deep Learning and Computer Vision for Self-Driving Cars : Build autonomous vehicles using deep neural networks and behavior-cloning techniques
par: Ranjan, Sumit.
Publié: (2020)

Université catholique de l'Ouest - Bibliothèques - 3 place André Leroy - BP10808 - 49008 ANGERS Cedex 1