Applied Deep Learning : Design and implement your own Neural Networks to solve real-world problems

Applied Deep Learning : Design and implement your own Neural Networks to solve real-world problems

Enregistré dans:

Détails bibliographiques
Auteur principal:	Kumar, Neeraj. (Auteur)
Autres auteurs:	Tekchandani, Rajkumar. (Auteur)
Support:	E-Book
Langue:	Anglais
Publié:	New Delhi : BPB Publications.
Autres localisations:	Voir dans le Sudoc
Accès en ligne:	Accès à l'E-book

Documents similaires

Applied Deep Learning with Keras : Solve complex real-life problems with the simplicity of Keras
par: Bhagwat, Ritesh.
Publié: (2019)

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

Deep Learning for Natural Language Processing : Solve your natural language processing problems with smart deep neural networks
par: Reddy Bokka, Karthiek.
Publié: (2019)

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)

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)

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

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

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

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)

Applied deep learning with Python : use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions
par: Galea, Alex.
Publié: (2018)

Production-Ready Applied Deep Learning : Learn how to construct and deploy complex models in PyTorch and TensorFlow deep learning frameworks
par: Palczewski, Tomasz.
Publié: (2022)

R deep learning projects : master the techniques to design and develop neural network models in R
par: Liu, Yuxi (Hayden).
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)

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

R deep learning cookbook : solve complex neural net problems with TensorFlow, H2O and MXNet
par: Prakash, PKS..
Publié: (2017)

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

The Deep Learning Workshop : Learn the skills you need to develop your own next-generation deep learning models with TensorFlow and Keras
par: Baig, Mirza Rahim.
Publié: (2020)

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

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

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

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

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

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 R for Beginners : Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet
par: Hodnett, Mark.
Publié: (2019)

Hands-On Java Deep Learning for Computer Vision : Implement machine learning and neural network methodologies to perform computer vision-related tasks
par: Ramo, Klevis.
Publié: (2019)

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

Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter : Build scalable real-world projects to implement end-to-end neural networks on Android and iOS
par: Singh, Anubhav.
Publié: (2020)

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

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

Deep Learning from the Basics : Python and Deep Learning: Theory and Implementation
par: Saitoh, Koki.
Publié: (2021)

The Deep Learning with Keras Workshop : Learn how to define and train neural network models with just a few lines of code
par: Moocarme, Matthew.
Publié: (2020)

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

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

Machine Learning and Deep Learning Algorithms : Tools and Techniques Using MATLAB and Python
par: Kumar Pandey, Abhishek.

Deep learning with Hadoop : build, implement and scale distributed deep learning models for large-scale datasets
par: Dev, Dipayan.
Publié: (2017)

Advanced deep learning with Keras : apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more
par: Atienza, Rowel.
Publié: (2018)

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

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

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