Les cours de formation TensorFlow direct, organisés par des instructeurs locaux, expliquent, par des discussions interactives et des exercices pratiques, comment utiliser le système TensorFlow pour faciliter la recherche en apprentissage automatique et faciliter la transition rapide d’un prototype à un système de production. TensorFlow formation TensorFlow est disponible en "formation sur site en direct" ou "formation en direct à distance". La formation en direct sur site peut être effectuée localement chez le client à France ou dans des centres de formation d'entreprise NobleProg dans France . La formation à distance en direct est réalisée au moyen d'un poste de travail distant et interactif. NobleProg - Votre prestataire de formation local
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Nos Clients témoignent
J'ai vraiment apprécié les réponses claires de Chris à nos questions.
Léo Dubus
Formation: Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple
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J'ai généralement apprécié le formateur compétent.
Sridhar Voorakkara
Formation: Neural Networks Fundamentals using TensorFlow as Example
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J'ai été stupéfait par la qualité de ce cours - je dirais que c'était la norme universitaire.
David Relihan
Formation: Neural Networks Fundamentals using TensorFlow as Example
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Très bon aperçu général. Go historique des raisons pour lesquelles Tensorflow fonctionne comme il le fait.
Kieran Conboy
Formation: Neural Networks Fundamentals using TensorFlow as Example
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J'ai aimé les possibilités de poser des questions et d'obtenir des explications plus approfondies de la théorie.
Sharon Ruane
Formation: Neural Networks Fundamentals using TensorFlow as Example
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Approche très actualisée ou IPC (flux tensoriel, ère, apprendre) pour faire de l'apprentissage automatique.
Paul Lee
Formation: TensorFlow for Image Recognition
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Compte tenu des perspectives de la technologie: quelle technologie / processus pourrait devenir plus important dans le futur; voir à quoi la technologie peut être utilisée
Commerzbank AG
Formation: Neural Networks Fundamentals using TensorFlow as Example
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J'ai bénéficié de la sélection de sujets. Style de formation. Orientation pratique
Commerzbank AG
Formation: Neural Networks Fundamentals using TensorFlow as Example
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Un large éventail de sujets couverts et une connaissance approfondie des dirigeants.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Formation: Understanding Deep Neural Networks
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manque
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Formation: Understanding Deep Neural Networks
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Grandes connaissances théoriques et pratiques des conférenciers. Communicativeness des formateurs. Pendant le cours, vous pouvez poser des questions et obtenir des réponses satisfaisantes.
Kamil Kurek - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Formation: Understanding Deep Neural Networks
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Partie pratique, où nous avons implémenté des algorithmes. Cela a permis une meilleure compréhension du sujet.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Formation: Understanding Deep Neural Networks
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exercices et exemples mis en oeuvre
Paweł Orzechowski - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Formation: Understanding Deep Neural Networks
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Exemples et problèmes discutés.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Formation: Understanding Deep Neural Networks
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Connaissances substantielles, engagement, une manière passionnée de transférer des connaissances. Exemples pratiques après un cours théorique.
Janusz Chrobot - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Formation: Understanding Deep Neural Networks
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Exercices pratiques préparés par M. Maciej
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Formation: Understanding Deep Neural Networks
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Identification humaine et détection de mauvais point de carte de circuit
王 春柱 - 中移物联网
Formation: Deep Learning for NLP (Natural Language Processing)
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faire la démonstration de
中移物联网
Formation: Deep Learning for NLP (Natural Language Processing)
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A propos de la surface faciale.
中移物联网
Formation: Deep Learning for NLP (Natural Language Processing)
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Beaucoup de conseils pratiques
Pawel Dawidowski - ABB Sp. z o.o.
Formation: Deep Learning with TensorFlow
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Beaucoup d'informations liées à la mise en œuvre de solutions
Michał Smolana - ABB Sp. z o.o.
Formation: Deep Learning with TensorFlow
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Une multitude de conseils pratiques et de connaissances du conférencier parmi un large éventail de problèmes d’IA / d’informatique / SQL / IdO.
ABB Sp. z o.o.
Formation: Deep Learning with TensorFlow
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J'ai commencé avec une connaissance quasi nulle et, à la fin, j'ai été capable de construire et de former mes propres réseaux.
Huawei Technologies Duesseldorf GmbH
Formation: TensorFlow for Image Recognition
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Tomasz connaît vraiment bien l’information et le parcours a été bien rythmé.
Raju Krishnamurthy - Google
Formation: TensorFlow Extended (TFX)
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Formateur était très bien informé et ouvert aux questions, aimait dessiner des diagrammes et a expliqué les choses d’une assez bonne façon
Formation: Deep Learning with TensorFlow 2.0
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Formateur était très bien informé et ouvert aux questions, aimait dessiner des diagrammes et a expliqué les choses d’une assez bonne façon
Formation: Deep Learning with TensorFlow 2.0
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Plans de cours TensorFlow
Nom du Cours
Durée
Aperçu
Nom du Cours
Durée
Aperçu
21 hours
Aperçu
TensorFlow est une API de deuxième génération de la bibliothèque de logiciels open source de Go ogle pour Deep Learning . Le système est conçu pour faciliter la recherche en apprentissage automatique et faciliter la transition rapide d'un prototype de recherche à un système de production.
Public
Ce cours est destiné aux ingénieurs souhaitant utiliser TensorFlow pour leurs projets d' Deep Learning .
Une fois ce cours terminé, les délégués:
- comprendre la structure et les mécanismes de déploiement de TensorFlow
- être capable d'effectuer des tâches d'installation / environnement de production / architecture et configuration
- être capable d'évaluer la qualité du code, effectuer le débogage, la surveillance
- être capable de mettre en œuvre une production avancée comme des modèles de formation, la création de graphiques et la journalisation
Public
Ce cours est destiné aux ingénieurs souhaitant utiliser TensorFlow pour leurs projets d' Deep Learning .
Une fois ce cours terminé, les délégués:
- comprendre la structure et les mécanismes de déploiement de TensorFlow
- être capable d'effectuer des tâches d'installation / environnement de production / architecture et configuration
- être capable d'évaluer la qualité du code, effectuer le débogage, la surveillance
- être capable de mettre en œuvre une production avancée comme des modèles de formation, la création de graphiques et la journalisation
28 hours
Aperçu
Ce cours explore, avec des exemples spécifiques, l’application de Tensor Flow aux objectifs de la reconnaissance d’image.
Public
Ce cours est destiné aux ingénieurs qui souhaitent utiliser TensorFlow aux fins de la reconnaissance d'image.
Une fois ce cours terminé, les délégués seront en mesure de:
- comprendre la structure et les mécanismes de déploiement de TensorFlow
- effectuer des tâches d'installation / environnement de production / architecture et configuration
- évaluer la qualité du code, effectuer le débogage, la surveillance
- implémenter une production avancée telle que des modèles de formation, la création de graphiques et l'enregistrement
Public
Ce cours est destiné aux ingénieurs qui souhaitent utiliser TensorFlow aux fins de la reconnaissance d'image.
Une fois ce cours terminé, les délégués seront en mesure de:
- comprendre la structure et les mécanismes de déploiement de TensorFlow
- effectuer des tâches d'installation / environnement de production / architecture et configuration
- évaluer la qualité du code, effectuer le débogage, la surveillance
- implémenter une production avancée telle que des modèles de formation, la création de graphiques et l'enregistrement
35 hours
Aperçu
TensorFlow™ is an open source software library for numerical computation using data flow graphs.
SyntaxNet is a neural-network Natural Language Processing framework for TensorFlow.
Word2Vec is used for learning vector representations of words, called "word embeddings". Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (Chapter 3.1 and 3.2 in Mikolov et al.).
Used in tandem, SyntaxNet and Word2Vec allows users to generate Learned Embedding models from Natural Language input.
Audience
This course is targeted at Developers and engineers who intend to work with SyntaxNet and Word2Vec models in their TensorFlow graphs.
After completing this course, delegates will:
- understand TensorFlow’s structure and deployment mechanisms
- be able to carry out installation / production environment / architecture tasks and configuration
- be able to assess code quality, perform debugging, monitoring
- be able to implement advanced production like training models, embedding terms, building graphs and logging
SyntaxNet is a neural-network Natural Language Processing framework for TensorFlow.
Word2Vec is used for learning vector representations of words, called "word embeddings". Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (Chapter 3.1 and 3.2 in Mikolov et al.).
Used in tandem, SyntaxNet and Word2Vec allows users to generate Learned Embedding models from Natural Language input.
Audience
This course is targeted at Developers and engineers who intend to work with SyntaxNet and Word2Vec models in their TensorFlow graphs.
After completing this course, delegates will:
- understand TensorFlow’s structure and deployment mechanisms
- be able to carry out installation / production environment / architecture tasks and configuration
- be able to assess code quality, perform debugging, monitoring
- be able to implement advanced production like training models, embedding terms, building graphs and logging
21 hours
Aperçu
Audience
This course is suitable for Deep Learning researchers and engineers interested in utilizing available tools (mostly open source) for analyzing computer images
This course provide working examples.
This course is suitable for Deep Learning researchers and engineers interested in utilizing available tools (mostly open source) for analyzing computer images
This course provide working examples.
28 hours
Aperçu
Ce cours vous donnera des connaissances sur les réseaux de neurones et plus généralement sur les algorithmes d’apprentissage automatique, d’apprentissage approfondi (algorithmes et applications).
Cette formation met davantage l'accent sur les principes fondamentaux, mais vous aidera à choisir la technologie TensorFlow : TensorFlow , Caffe , Teano, DeepDrive, Keras , etc. Les exemples sont réalisés dans TensorFlow .
Cette formation met davantage l'accent sur les principes fondamentaux, mais vous aidera à choisir la technologie TensorFlow : TensorFlow , Caffe , Teano, DeepDrive, Keras , etc. Les exemples sont réalisés dans TensorFlow .
7 hours
Aperçu
In this instructor-led, live training in France, participants will learn how to take advantage of the innovations in TPU processors to maximize the performance of their own AI applications.
By the end of the training, participants will be able to:
- Train various types of neural networks on large amounts of data.
- Use TPUs to speed up the inference process by up to two orders of magnitude.
- Utilize TPUs to process intensive applications such as image search, cloud vision and photos.
By the end of the training, participants will be able to:
- Train various types of neural networks on large amounts of data.
- Use TPUs to speed up the inference process by up to two orders of magnitude.
- Utilize TPUs to process intensive applications such as image search, cloud vision and photos.
14 hours
Aperçu
Embedding Projector is an open-source web application for visualizing the data used to train machine learning systems. Created by Google, it is part of TensorFlow.
This instructor-led, live training introduces the concepts behind Embedding Projector and walks participants through the setup of a demo project.
By the end of this training, participants will be able to:
- Explore how data is being interpreted by machine learning models
- Navigate through 3D and 2D views of data to understand how a machine learning algorithm interprets it
- Understand the concepts behind Embeddings and their role in representing mathematical vectors for images, words and numerals.
- Explore the properties of a specific embedding to understand the behavior of a model
- Apply Embedding Project to real-world use cases such building a song recommendation system for music lovers
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
This instructor-led, live training introduces the concepts behind Embedding Projector and walks participants through the setup of a demo project.
By the end of this training, participants will be able to:
- Explore how data is being interpreted by machine learning models
- Navigate through 3D and 2D views of data to understand how a machine learning algorithm interprets it
- Understand the concepts behind Embeddings and their role in representing mathematical vectors for images, words and numerals.
- Explore the properties of a specific embedding to understand the behavior of a model
- Apply Embedding Project to real-world use cases such building a song recommendation system for music lovers
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
7 hours
Aperçu
In this instructor-led, live training in France (online or onsite), participants will learn how to configure and use TensorFlow Serving to deploy and manage ML models in a production environment.
By the end of this training, participants will be able to:
- Train, export and serve various TensorFlow models.
- Test and deploy algorithms using a single architecture and set of APIs.
- Extend TensorFlow Serving to serve other types of models beyond TensorFlow models.
By the end of this training, participants will be able to:
- Train, export and serve various TensorFlow models.
- Test and deploy algorithms using a single architecture and set of APIs.
- Extend TensorFlow Serving to serve other types of models beyond TensorFlow models.
35 hours
Aperçu
This course begins with giving you conceptual knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications).
Part-1(40%) of this training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.
Part-2(20%) of this training introduces Theano - a python library that makes writing deep learning models easy.
Part-3(40%) of the training would be extensively based on Tensorflow - 2nd Generation API of Google's open source software library for Deep Learning. The examples and handson would all be made in TensorFlow.
Audience
This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects
After completing this course, delegates will:
-
have a good understanding on deep neural networks(DNN), CNN and RNN
-
understand TensorFlow’s structure and deployment mechanisms
-
be able to carry out installation / production environment / architecture tasks and configuration
-
be able to assess code quality, perform debugging, monitoring
-
be able to implement advanced production like training models, building graphs and logging
Part-1(40%) of this training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.
Part-2(20%) of this training introduces Theano - a python library that makes writing deep learning models easy.
Part-3(40%) of the training would be extensively based on Tensorflow - 2nd Generation API of Google's open source software library for Deep Learning. The examples and handson would all be made in TensorFlow.
Audience
This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects
After completing this course, delegates will:
-
have a good understanding on deep neural networks(DNN), CNN and RNN
-
understand TensorFlow’s structure and deployment mechanisms
-
be able to carry out installation / production environment / architecture tasks and configuration
-
be able to assess code quality, perform debugging, monitoring
-
be able to implement advanced production like training models, building graphs and logging
28 hours
Aperçu
In this instructor-led, live training in France, participants will learn to use Python libraries for NLP as they create an application that processes a set of pictures and generates captions.
By the end of this training, participants will be able to:
- Design and code DL for NLP using Python libraries.
- Create Python code that reads a substantially huge collection of pictures and generates keywords.
- Create Python Code that generates captions from the detected keywords.
By the end of this training, participants will be able to:
- Design and code DL for NLP using Python libraries.
- Create Python code that reads a substantially huge collection of pictures and generates keywords.
- Create Python Code that generates captions from the detected keywords.
28 hours
Aperçu
This is a 4 day course introducing AI and it's application. There is an option to have an additional day to undertake an AI project on completion of this course.
21 hours
Aperçu
This instructor-led, live training in France (online or onsite) is aimed at developers and data scientists who wish to use Tensorflow 2.x to build predictors, classifiers, generative models, neural networks and so on.
By the end of this training, participants will be able to:
- Install and configure TensorFlow 2.x.
- Understand the benefits of TensorFlow 2.x over previous versions.
- Build deep learning models.
- Implement an advanced image classifier.
- Deploy a deep learning model to the cloud, mobile and IoT devices.
By the end of this training, participants will be able to:
- Install and configure TensorFlow 2.x.
- Understand the benefits of TensorFlow 2.x over previous versions.
- Build deep learning models.
- Implement an advanced image classifier.
- Deploy a deep learning model to the cloud, mobile and IoT devices.
14 hours
Aperçu
This instructor-led, live training in France (online or onsite) is aimed at data scientists who wish to use TensorFlow.js to identify patterns and generate predictions through machine learning models.
By the end of this training, participants will be able to:
- Build and train machine learning models with TensorFlow.js.
- Run existing machine learning models in the browser or under Node.js.
- Retrain pre-existing machine learning using custom data.
By the end of this training, participants will be able to:
- Build and train machine learning models with TensorFlow.js.
- Run existing machine learning models in the browser or under Node.js.
- Retrain pre-existing machine learning using custom data.
14 hours
Aperçu
This instructor-led, live training in France (online or onsite) is aimed at data scientists who wish to use TensorFlow to analyze potential fraud data.
By the end of this training, participants will be able to:
- Create a fraud detection model in Python and TensorFlow.
- Build linear regressions and linear regression models to predict fraud.
- Develop an end-to-end AI application for analyzing fraud data.
By the end of this training, participants will be able to:
- Create a fraud detection model in Python and TensorFlow.
- Build linear regressions and linear regression models to predict fraud.
- Develop an end-to-end AI application for analyzing fraud data.
21 hours
Aperçu
This instructor-led, live training in France (online or onsite) is aimed at data scientists who wish to go from training a single ML model to deploying many ML models to production.
By the end of this training, participants will be able to:
- Install and configure TFX and supporting third-party tools.
- Use TFX to create and manage a complete ML production pipeline.
- Work with TFX components to carry out modeling, training, serving inference, and managing deployments.
- Deploy machine learning features to web applications, mobile applications, IoT devices and more.
By the end of this training, participants will be able to:
- Install and configure TFX and supporting third-party tools.
- Use TFX to create and manage a complete ML production pipeline.
- Work with TFX components to carry out modeling, training, serving inference, and managing deployments.
- Deploy machine learning features to web applications, mobile applications, IoT devices and more.
21 hours
Aperçu
This instructor-led, live training in France (online or onsite) is aimed at developers who wish to use TensorFlow Lite to deploy deep learning models on embedded devices.
By the end of this training, participants will be able to:
- Install and configure Tensorflow Lite on an embedded device.
- Understand the concepts and components underlying TensorFlow Lite.
- Convert existing models to TensorFlow Lite format for execution on embedded devices.
- Work within the limitations of small devices and TensorFlow Lite, while learning how to expand the scope of operations that can be run.
- Deploy a deep learning model on an embedded device running Linux.
By the end of this training, participants will be able to:
- Install and configure Tensorflow Lite on an embedded device.
- Understand the concepts and components underlying TensorFlow Lite.
- Convert existing models to TensorFlow Lite format for execution on embedded devices.
- Work within the limitations of small devices and TensorFlow Lite, while learning how to expand the scope of operations that can be run.
- Deploy a deep learning model on an embedded device running Linux.
21 hours
Aperçu
This instructor-led, live training in France (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop mobile applications with deep learning capabilities.
By the end of this training, participants will be able to:
- Install and configure TensorFlow Lite.
- Understand the principles behind TensorFlow, machine learning and deep learning.
- Load TensorFlow Models onto an Android device.
- Enable deep learning and machine learning functionality such as computer vision and natural language recognition in a mobile application.
By the end of this training, participants will be able to:
- Install and configure TensorFlow Lite.
- Understand the principles behind TensorFlow, machine learning and deep learning.
- Load TensorFlow Models onto an Android device.
- Enable deep learning and machine learning functionality such as computer vision and natural language recognition in a mobile application.
21 hours
Aperçu
This instructor-led, live training in France (online or onsite) is aimed at engineers who wish to write, load and run machine learning models on very small embedded devices.
By the end of this training, participants will be able to:
- Install TensorFlow Lite.
- Load machine learning models onto an embedded device to enable it to detect speech, classify images, etc.
- Add AI to hardware devices without relying on network connectivity.
By the end of this training, participants will be able to:
- Install TensorFlow Lite.
- Load machine learning models onto an embedded device to enable it to detect speech, classify images, etc.
- Add AI to hardware devices without relying on network connectivity.
21 hours
Aperçu
This instructor-led, live training in (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop iOS mobile applications with deep learning capabilities.
By the end of this training, participants will be able to:
- Install and configure TensorFlow Lite.
- Understand the principles behind TensorFlow and machine learning on mobile devices.
- Load TensorFlow Models onto an iOS device.
- Run an iOS application capable of detecting and classifying an object captured through the device's camera.
By the end of this training, participants will be able to:
- Install and configure TensorFlow Lite.
- Understand the principles behind TensorFlow and machine learning on mobile devices.
- Load TensorFlow Models onto an iOS device.
- Run an iOS application capable of detecting and classifying an object captured through the device's camera.
Prochains cours TensorFlow
15-11-2021 09:30
21 heures
29-11-2021 09:30
21 heures
30-11-2021 09:30
21 heures
15-12-2021 09:30
21 heures
20-12-2021 09:30
21 heures
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