Deep learning for computer vision : expert techniques to train advanced neural networks using TensorFlow and Keras

Deep learning for computer vision : expert techniques to train advanced neural networks using TensorFlow and Keras

Enregistré dans:

Détails bibliographiques
Auteur principal:	Shanmugamani, Rajalingappaa (19..-....). (Auteur)
Support:	E-Book
Langue:	Anglais
Publié:	Birmingham : Packt Publishing, 2018.
Autres localisations:	Voir dans le Sudoc
Accès en ligne:	Accès à l'E-book

Documents similaires

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

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

Python deep learning : exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow
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 with R for Beginners : Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet
par: Hodnett, Mark.
Publié: (2019)

Fundamentals of Deep Learning and Computer Vision : A Complete Guide to become an Expert in Deep Learning and Computer Vision
par: Singh, Nikhil.

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)

Advanced Methods and Deep Learning in Computer Vision
par: Davies, E. R..
Publié: (2021)

Elements of Deep Learning for Computer Vision : Explore Deep Neural Network Architectures, PyTorch, Object Detection Algorithms, and Computer Vision Applications for Python Coders (English Edition)
par: Sikka, Bharat (1973-....).

Hands-on transfer learning with Python : implement advanced deep learning and neural network models using TensorFlow and Keras
par: Sarkar, Dipanjan (19..-....).
Publié: (2018)

Hands-On Computer Vision with TensorFlow 2 : Leverage deep learning to create powerful image processing apps with TensorFlow 2.0 and Keras
par: Planche, Benjamin.
Publié: (2019)

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

Mastering Computer Vision with TensorFlow 2.x : Build advanced computer vision applications using machine learning and deep learning techniques
par: Kar, Krishnendu.
Publié: (2020)

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 TensorFlow
par: Zaccone, Giancarlo.
Publié: (2017)

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)

Practical Computer Vision : Extract insightful information from images using TensorFlow, Keras, and OpenCV
par: Dadhich, Abhinav.
Publié: (2018)

Hands-on deep learning with TensorFlow
par: Boxel, Dan Van.
Publié: (2017)

Deep Learning with TensorFlow and Keras : Build and deploy supervised, unsupervised, deep, and reinforcement learning models
par: Kapoor, Amita.
Publié: (2022)

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

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

TensorFlow deep learning projects : 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning
par: Massaron, Luca (19..-....).
Publié: (2018)

Deep learning avec Keras et TensorFlow : mise en oeuvre et cas concrets
par: Géron, Aurélien (19..-....).

Advanced Deep Learning with TensorFlow 2 and Keras : Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more
par: Atienza, Rowel.
Publié: (2020)

Hands-On Python Deep Learning for the Web : Integrating neural network architectures to build smart web apps with Flask, Django, and TensorFlow
par: Singh, Anubhav.
Publié: (2020)

TensorFlow 1.x Deep Learning Cookbook
par: Gulli, Antonio.
Publié: (2017)

Essentials of Deep Learning and AI : Experience Unsupervised Learning, Autoencoders, Feature Engineering, and Time Series Analysis with TensorFlow, Keras, and scikit-learn
par: Soppin, Shashidhar.

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

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

Advanced Deep Learning with Python : Design and implement advanced next-generation AI solutionsusing TensorFlow and PyTorch
par: Vasilev, Ivan.
Publié: (2019)

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)

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

Hands-On Deep Learning Algorithms with Python : Master deep learning algorithms with extensive math by implementing them using TensorFlow
par: Ravichandiran, Sudharsan.
Publié: (2019)

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

Mastering TensorFlow 1.x : advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras
par: Fandango, Armando.
Publié: (2018)

TensorFlow 2.0 Computer Vision Cookbook : Implement machine learning solutions to overcome various computer vision challenges
par: Martinez, Jesus.
Publié: (2021)

Hands-on convolutional neural networks with TensorFlow : solve computer vision problems with modeling in TensorFlow and Python
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)

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