Lokale, von Lehrern geleitete Live- TensorFlow Schulungen demonstrieren anhand interaktiver Diskussionen und praktischer Übungen, wie das TensorFlow System zur Erleichterung der Forschung im Bereich des maschinellen Lernens und zum schnellen und einfachen Übergang vom Forschungsprototyp zum Produktionssystem eingesetzt werden kann. TensorFlow Training ist als "Onsite-Live-Training" oder "Remote-Live-Training" verfügbar. Vor-Ort-Live-Schulungen können vor Ort beim Kunden in Berlin durchgeführt werden Deutschland oder in NobleProg Firmenschulungszentren in Deutschland . Das Remote-Live-Training erfolgt über einen interaktiven Remote-Desktop. NobleProg - Ihr lokaler Schulungsanbieter
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Erfahrungsberichte
Voraussichtlicher Stand der Technologie: Welche Technologie / Prozess könnte in Zukunft wichtiger werden? sehen Sie, wofür die Technologie verwendet werden kann.
Commerzbank AG
Kurs: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Ich habe von der Themenauswahl profitiert. Art des Trainings. Praxisorientierung.
Commerzbank AG
Kurs: Neural Networks Fundamentals using TensorFlow as Example
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Ich habe mit nahezu null Wissen angefangen und am Ende konnte ich meine eigenen Netzwerke aufbauen und trainieren.
Huawei Technologies Duesseldorf GmbH
Kurs: TensorFlow for Image Recognition
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Ich habe die kristallklaren Antworten von Chris auf unsere Fragen sehr geschätzt.
Léo Dubus
Kurs: Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple
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Ich genoss im Allgemeinen den sachkundigen Trainer.
Sridhar Voorakkara
Kurs: Neural Networks Fundamentals using TensorFlow as Example
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Ich war erstaunt über den Standard dieser Klasse - ich würde sagen, dass es Universitätsstandard war.
David Relihan
Kurs: Neural Networks Fundamentals using TensorFlow as Example
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Sehr gute Rundumübersicht. Go od Hintergrund in warum Tensorflow arbeitet , wie es der Fall ist.
Kieran Conboy
Kurs: Neural Networks Fundamentals using TensorFlow as Example
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Ich mochte die Gelegenheiten, Fragen zu stellen und tiefergehende Erklärungen der Theorie zu bekommen.
Sharon Ruane
Kurs: Neural Networks Fundamentals using TensorFlow as Example
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Sehr aktualisierter Ansatz oder CPI (Tensor Flow, Ära, lernen), um maschinelles Lernen zu tun.
Paul Lee
Kurs: TensorFlow for Image Recognition
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Eine breite Palette von Themen abgedeckt und umfangreiches Wissen der Führungskräfte.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurs: Understanding Deep Neural Networks
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Mangel
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurs: Understanding Deep Neural Networks
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Großes theoretisches und praktisches Wissen der Dozenten. Kommunikativität von Trainern. Während des Kurses können Sie Fragen stellen und zufriedenstellende Antworten erhalten.
Kamil Kurek - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurs: Understanding Deep Neural Networks
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Praktischer Teil, bei dem wir Algorithmen implementiert haben. Dies ermöglichte ein besseres Verständnis des Themas.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurs: Understanding Deep Neural Networks
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Übungen und Beispiele auf ihnen implementiert
Paweł Orzechowski - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurs: Understanding Deep Neural Networks
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Beispiele und diskutierte Themen.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurs: Understanding Deep Neural Networks
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Substanzielles Wissen, Engagement, eine leidenschaftliche Art Wissen zu vermitteln. Praktische Beispiele nach einem theoretischen Vortrag.
Janusz Chrobot - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurs: Understanding Deep Neural Networks
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Praktische Übungen von Herrn Maciej
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurs: Understanding Deep Neural Networks
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Personalausweis und Leiterplattenminderkennung
王 春柱 - 中移物联网
Kurs: Deep Learning for NLP (Natural Language Processing)
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beweisen
中移物联网
Kurs: Deep Learning for NLP (Natural Language Processing)
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Über Gesichtsbereich.
中移物联网
Kurs: Deep Learning for NLP (Natural Language Processing)
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Viele praktische Tipps
Pawel Dawidowski - ABB Sp. z o.o.
Kurs: Deep Learning with TensorFlow
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Viele Informationen rund um die Umsetzung von Lösungen
Michał Smolana - ABB Sp. z o.o.
Kurs: Deep Learning with TensorFlow
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Eine Vielzahl von praktischen Tipps und Kenntnissen des Dozenten aus einer Vielzahl von AI / IT / SQL / IoT-Themen.
ABB Sp. z o.o.
Kurs: Deep Learning with TensorFlow
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Tomasz kennt die Informationen wirklich gut und der Kurs war gut gelaufen.
Raju Krishnamurthy - Google
Kurs: TensorFlow Extended (TFX)
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Trainer war sehr sachkundig und offen für Fragen, mochte Diagramme zeichnen und erklärt Dinge in einer ziemlich guten Art und Weise
Kurs: Deep Learning with TensorFlow 2.0
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Trainer war sehr sachkundig und offen für Fragen, mochte Diagramme zeichnen und erklärt Dinge in einer ziemlich guten Art und Weise
Kurs: Deep Learning with TensorFlow 2.0
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TensorFlow Kurspläne
Name des Kurses
Dauer
Überblick
Name des Kurses
Dauer
Überblick
21 Stunden
Überblick
TensorFlow is a 2nd Generation API of Google's open source software library for Deep Learning. The system is designed to facilitate research in machine learning, and to make it quick and easy to transition from research prototype to production system.
Audience
This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects
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, building graphs and logging
Audience
This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects
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, building graphs and logging
28 Stunden
Überblick
This course explores, with specific examples, the application of Tensor Flow to the purposes of image recognition
Audience
This course is intended for engineers seeking to utilize TensorFlow for the purposes of Image Recognition
After completing this course, delegates will be able to:
- understand TensorFlow’s structure and deployment mechanisms
- carry out installation / production environment / architecture tasks and configuration
- assess code quality, perform debugging, monitoring
- implement advanced production like training models, building graphs and logging
Audience
This course is intended for engineers seeking to utilize TensorFlow for the purposes of Image Recognition
After completing this course, delegates will be able to:
- understand TensorFlow’s structure and deployment mechanisms
- carry out installation / production environment / architecture tasks and configuration
- assess code quality, perform debugging, monitoring
- implement advanced production like training models, building graphs and logging
35 Stunden
Überblick
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 Stunden
Überblick
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 Stunden
Überblick
This course will give you knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications).
This training is more focus on fundamentals, but will help you to choose the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow.
This training is more focus on fundamentals, but will help you to choose the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow.
7 Stunden
Überblick
In this instructor-led, live training in Deutschland, 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 Stunden
Überblick
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 Stunden
Überblick
In this instructor-led, live training in Deutschland (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 Stunden
Überblick
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 Stunden
Überblick
In this instructor-led, live training in Deutschland, 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 Stunden
Überblick
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 Stunden
Überblick
This instructor-led, live training in Deutschland (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 Stunden
Überblick
This instructor-led, live training in Deutschland (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 Stunden
Überblick
This instructor-led, live training in Deutschland (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 Stunden
Überblick
This instructor-led, live training in Deutschland (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 Stunden
Überblick
This instructor-led, live training in Deutschland (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 Stunden
Überblick
This instructor-led, live training in Deutschland (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 Stunden
Überblick
This instructor-led, live training in Deutschland (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 Stunden
Überblick
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.
Zukünftige TensorFlow Kurse
10-11-2021 09:30
7 hours
16-11-2021 09:30
7 hours
15-12-2021 09:30
14 hours
20-12-2021 09:30
35 hours
21-12-2021 09:30
28 hours
TensorFlow Schulung, TensorFlow boot camp, TensorFlow Abendkurse, TensorFlow Wochenendkurse, TensorFlow Kurs, TensorFlow Training, TensorFlow Seminar, TensorFlow Seminare, TensorFlow Privatkurs, TensorFlow Coaching, TensorFlow Lehrer
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