A helyi, oktatók által vezetett élő TensorFlow képzési tanfolyamok interaktív beszélgetés és gyakorlati gyakorlat révén demonstrálják, hogyan lehet a TensorFlow rendszert felhasználni a gépi tanulás során végzett kutatások megkönnyítésére, valamint a kutatási prototípusról a termelési rendszerre való gyors és egyszerű átmenet elősegítésére. TensorFlow edzés „helyszíni élő TensorFlow ” vagy „távoli élő TensorFlow ” érhető el. A helyszíni élő képzéseket helyi ügyfelek telephelyén lehet végrehajtani Magyarország vagy a NobleProg vállalati képzési központjain Magyarország . A távoli élő képzést interaktív, távoli asztalon végzik. NobleProg - a helyi képzési szolgáltató
Machine Translated
Vélemények
Nagyra értékelem Krisztus kristálytiszta válaszát a kérdéseinkre.
Léo Dubus
Kurzus: Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple
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Általában élveztem a jól képzett edzőt.
Sridhar Voorakkara
Kurzus: Neural Networks Fundamentals using TensorFlow as Example
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Meglepődtem ennek az osztálynak a normáljához - azt mondanám, hogy egyetemes volt.
David Relihan
Kurzus: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Nagyon jó átfogó áttekintés. Go a hátteret arról, hogy a Tensorflow miért működik úgy, ahogy működik.
Kieran Conboy
Kurzus: Neural Networks Fundamentals using TensorFlow as Example
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Szeretem a kérdéseket feltenni és mélyebben magyarázni az elméletet.
Sharon Ruane
Kurzus: Neural Networks Fundamentals using TensorFlow as Example
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Nagyon frissített megközelítés vagy fogyasztói árindex (tenzor áramlás, korszak, tanulás) a gépi tanuláshoz.
Paul Lee
Kurzus: TensorFlow for Image Recognition
Machine Translated
Tekintettel a technológia kilátásaira: milyen technológia / folyamat válhat a jövőben fontosabbá; nézd meg, milyen technológiát lehet használni.
Commerzbank AG
Kurzus: Neural Networks Fundamentals using TensorFlow as Example
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A témaválasztás előnyeit élveztem. A képzés stílusa. Gyakorlati tájékozódás.
Commerzbank AG
Kurzus: Neural Networks Fundamentals using TensorFlow as Example
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A témák széles köre és a vezetők jelentős ismerete.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurzus: Understanding Deep Neural Networks
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hiány
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurzus: Understanding Deep Neural Networks
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Az előadók nagy elméleti és gyakorlati ismerete. Az oktatók kommunikációja. A tanfolyam során kérdéseket tehet fel, és kielégítő választ kaphat.
Kamil Kurek - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurzus: Understanding Deep Neural Networks
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Gyakorlati rész, ahol algoritmusokat vezettünk be. Ez lehetővé tette a téma jobb megértését.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurzus: Understanding Deep Neural Networks
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gyakorlatokat és a rájuk végrehajtott példákat
Paweł Orzechowski - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurzus: Understanding Deep Neural Networks
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Példák és kérdések.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurzus: Understanding Deep Neural Networks
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Az anyagi tudás, elkötelezettség, a tudás átadásának szenvedélyes módja. Gyakorlati példák elméleti előadás után.
Janusz Chrobot - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurzus: Understanding Deep Neural Networks
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Maciej úr gyakorlati gyakorlatok
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurzus: Understanding Deep Neural Networks
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Az emberi azonosítás és áramköri rossz pont detektálás
王 春柱 - 中移物联网
Kurzus: Deep Learning for NLP (Natural Language Processing)
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Bizonyítani
中移物联网
Kurzus: Deep Learning for NLP (Natural Language Processing)
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Körülbelül szembenéz terület.
中移物联网
Kurzus: Deep Learning for NLP (Natural Language Processing)
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Sok praktikus tipp
Pawel Dawidowski - ABB Sp. z o.o.
Kurzus: Deep Learning with TensorFlow
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Rengeteg információ a megoldások megvalósításával kapcsolatban
Michał Smolana - ABB Sp. z o.o.
Kurzus: Deep Learning with TensorFlow
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Az AI / IT / SQL / IoT témakörök széles köréből számos praktikus tipp és ismeret az előadónak.
ABB Sp. z o.o.
Kurzus: Deep Learning with TensorFlow
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A nulla közeli tudással kezdtem, és végül képesek voltam saját hálózatokat felépíteni és kiképzni.
Huawei Technologies Duesseldorf GmbH
Kurzus: TensorFlow for Image Recognition
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Tomasz igazán jól ismeri az információt, és a tanfolyam jól haladt.
Raju Krishnamurthy - Google
Kurzus: TensorFlow Extended (TFX)
Machine Translated
A tréner nagyon jól tájékozott és nyitott volt a kérdésekre, szeretett diagramokat rajzolni és nagyon jó módon elmagyarázni a dolgokat
Kurzus: Deep Learning with TensorFlow 2.0
Machine Translated
A tréner nagyon jól tájékozott és nyitott volt a kérdésekre, szeretett diagramokat rajzolni és nagyon jó módon elmagyarázni a dolgokat
Kurzus: Deep Learning with TensorFlow 2.0
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TensorFlow Course Outlines
Kurzusnév
Időtartalma
Összefoglaló
Kurzusnév
Időtartalma
Összefoglaló
21 hours
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 hours
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 hours
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
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
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 hours
In this instructor-led, live training in Magyarország, 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
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
In this instructor-led, live training in Magyarország (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
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
In this instructor-led, live training in Magyarország, 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
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
This instructor-led, live training in Magyarország (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
This instructor-led, live training in Magyarország (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
This instructor-led, live training in Magyarország (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
This instructor-led, live training in Magyarország (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
This instructor-led, live training in Magyarország (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
This instructor-led, live training in Magyarország (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
This instructor-led, live training in Magyarország (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
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.
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21 hours
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21 hours
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