Deep Learning and Parallel Computing Environment for Bioengineering Systems

Deep Learning and Parallel Computing Environment for Bioengineering Systems

Enregistré dans:

Détails bibliographiques
Auteur principal:	Sangaiah, Arun Kumar. (Auteur)
Support:	E-Book
Langue:	Anglais
Publié:	San Diego : Elsevier Science, 2019.
Autres localisations:	Voir dans le Sudoc
Accès en ligne:	Accès à l'E-book

Documents similaires

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

Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications
par: Sangaiah, Arun Kumar.
Publié: (2018)

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

Deep Learning on Edge Computing Devices : Design Challenges of Algorithm and Architecture
par: Zhou, Xichuan.
Publié: (2022)

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

Introduction to parallel computing
par: Grama, Ananth.
Publié: (2003)

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-....).

Intelligent IoT Systems in Personalized Health Care
par: Sangaiah, Arun Kumar.

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)

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

Applied Deep Learning : Design and implement your own Neural Networks to solve real-world problems
par: Kumar, Neeraj.

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

Connected computing environment
par: Hurson, Ali (19..-....).
Publié: (2013)

Deep Learning for Genomics : Data-driven approaches for genomics applications in life sciences and biotechnology
par: Devisetty, Upendra Kumar.
Publié: (2022)

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 Chest Radiographs : Computer-Aided Classification
par: Chandola, Yashvi.

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)

New and future developments in microbial biotechnology and bioengineering : penicillium system properties and applications
par: Gupta, Vijai Kumar.
Publié: (2017)

Topics in Parallel and Distributed Computing : Introducing Concurrency in Undergraduate Courses
par: Prasad, Sushil K..
Publié: (2015)

Deep Learning for Dummies
par: Mueller, John Paul (1958-....).

Enhancing Deep Learning with Bayesian Inference : Create more powerful, robust deep learning systems with Bayesian deep learning in Python
par: Benatan, Matt.

Apache Spark deep learning cookbook : over 80 recipes that streamline deep learning in a distributed environment with Apache Spark
par: Sherif, Ahmed.
Publié: (2018)

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

Trends in deep learning methodologies : algorithms, applications, and systems
par: Piuri, Vincenzo (1960-....).

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

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

Cardiovascular and Respiratory Bioengineering
par: Filipovic, Nenad.

Handbook of food bioengineering.
par: Holban, Alina Maria (19..-....).
Publié: (2018)

Handbook of food bioengineering.
par: Holban, Alina Maria (19..-....).
Publié: (2018)

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

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

Current developments in biotechnology and bioengineering : foundations of biotechnology and bioengineering
Publié: (2016)

Deep Learning for Data Architects : Unleash the power of Python's deep learning algorithms
par: Khandelwal, Shekhar.

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

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)

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)

Deep Learning in Bioinformatics : Techniques and Applications in Practice
par: Izadkhah, Habib.
Publié: (2022)

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)

Principles and Labs for Deep Learning
par: Huang, Shih-Chia.
Publié: (2021)

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