Praktyczne szkolenia na żywo z TensorFlow to kursy pokazujące, jak korzystać z systemu TensorFlow, aby ułatwić projekty badawcze w uczeniu maszynowym i ułatwić przejście od prototypu badawczego do systemu produkcyjnego.
Szkolenie TensorFlow jest dostępne jako "szkolenie stacjonarne" lub "szkolenie online na żywo".
Szkolenie stacjonarne może odbywać się lokalnie w siedzibie klienta w Polsce lub w ośrodkach szkoleniowych NobleProg w Polsce. Zdalne szkolenie online odbywa się za pomocą interaktywnego, zdalnego pulpitu DaDesktop .
NobleProg -- Twój lokalny dostawca szkoleń.
Zapytaj konsultanta NobleProg o dedykowane szkolenie dla Twojego zespołu.
Opinie uczestników
Szeroki zakres poruszanych tematów oraz duża wiedza merytoryczna prowadzących.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Szkolenie: Understanding Deep Neural Networks
Brak
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Szkolenie: Understanding Deep Neural Networks
Duża wiedza teoretyczna i praktyczna prowadzących. Komunikatywność prowadzących. W trakcie kursu można było zadawać pytania i uzyskać satysfakcjonujące odpowiedzi.
Kamil Kurek - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Szkolenie: Understanding Deep Neural Networks
Część praktyczna, gdzie implementowaliśmy algorytmy. Pozwoliło to na lepsze zrozumienie tematu.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Szkolenie: Understanding Deep Neural Networks
ćwiczenia i przykłady na nich realizowane
Paweł Orzechowski - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Szkolenie: Understanding Deep Neural Networks
Przykłady i omawiane zagadnienia.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Szkolenie: Understanding Deep Neural Networks
Wiedza merytoryczna, zaangażowanie, pasjonujący sposób przekazywania wiedzy. Przykłady praktyczne po wykładzie teoretycznym.
Janusz Chrobot - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Szkolenie: Understanding Deep Neural Networks
Praktyczne ćwiczenia przygotowane przez Pana Macieja
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Szkolenie: Understanding Deep Neural Networks
Dużo wskazówek praktycznych
Pawel Dawidowski - ABB Sp. z o.o.
Szkolenie: Deep Learning with TensorFlow
Dużo informacji związanych z wdrożeniami rozwiązań
Michał Smolana - ABB Sp. z o.o.
Szkolenie: Deep Learning with TensorFlow
Mnogość praktycznych wskazówek i wiedza prowadzącego z szerokiej gamy zagadnień AI / IT / SQL / IoT.
ABB Sp. z o.o.
Szkolenie: Deep Learning with TensorFlow
Naprawdę doceniam krystalicznie czyste odpowiedzi Chrisa na nasze pytania.
Léo Dubus
Szkolenie: Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple
Machine Translated
Ogólnie byłem zadowolony z kompetentnego trenera.
Sridhar Voorakkara
Szkolenie: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Byłem zdumiony standardem tej klasy - powiedziałbym, że był to standard uniwersytecki.
David Relihan
Szkolenie: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Bardzo dobry ogólny przegląd. Go dlaczego Tensorflow działa tak, jak działa.
Kieran Conboy
Szkolenie: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Spodobały mi się możliwości zadawania pytań i dokładniejszego wyjaśnienia teorii.
Sharon Ruane
Szkolenie: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Bardzo zaktualizowane podejście lub CPI (tensor flow, era, learn) do uczenia maszynowego.
Paul Lee
Szkolenie: TensorFlow for Image Recognition
Machine Translated
Perspektywa technologii: jaka technologia / proces może stać się ważniejsza w przyszłości; zobacz, do czego można wykorzystać tę technologię.
Commerzbank AG
Szkolenie: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Miałem korzyści z wyboru tematu. Styl szkolenia. Poćwicz orientację.
Commerzbank AG
Szkolenie: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Informacje o obszarze powierzchni.
中移物联网
Szkolenie: Deep Learning for NLP (Natural Language Processing)
Machine Translated
Zacząłem od wiedzy bliskiej zeru, a pod koniec mogłem budować i szkolić własne sieci.
Huawei Technologies Duesseldorf GmbH
Szkolenie: TensorFlow for Image Recognition
Machine Translated
Ali Kattan - TWPI
Szkolenie: Natural Language Processing with TensorFlow
Machine Translated
Ibrahim Mohammedameen - TWPI
Szkolenie: Natural Language Processing with TensorFlow
Machine Translated
Usama Adam - TWPI
Szkolenie: Natural Language Processing with TensorFlow
Machine Translated
Nercia Utbildning AB
Szkolenie: Deep Learning with TensorFlow 2.0
Machine Translated
Tomasz naprawdę dobrze zna informacje, a kurs był dobrze rozpędzony.
Raju Krishnamurthy - Google
Szkolenie: TensorFlow Extended (TFX)
Machine Translated
Robert Baker
Szkolenie: Deep Learning with TensorFlow 2.0
Machine Translated
Trener był bardzo kompetentny i otwarty na pytania, lubił rysować diagramy i wyjaśniał rzeczy w całkiem dobry sposób
Szkolenie: Deep Learning with TensorFlow 2.0
Machine Translated
Trener był bardzo kompetentny i otwarty na pytania, lubił rysować diagramy i wyjaśniał rzeczy w całkiem dobry sposób
Szkolenie: Deep Learning with TensorFlow 2.0
Machine Translated
Plany szkoleń z technologii TensorFlow
Nazwa kursu
Czas trwania
Opis
Nazwa kursu
Czas trwania
Opis
21 godzin
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 godzin
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 godzin
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 godzin
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 godzin
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 godzin
In this instructor-led, live training in w Polsce, 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 godzin
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 godzin
In this instructor-led, live training in w Polsce (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 godzin
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 godzin
In this instructor-led, live training in w Polsce, 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 godzin
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 godzin
This instructor-led, live training in w Polsce (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 godzin
This instructor-led, live training in w Polsce (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.
21 godzin
This instructor-led, live training in w Polsce (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.
14 godzin
This instructor-led, live training in w Polsce (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.
Last Updated:
Nadchodzące szkolenia z technologii TensorFlow
Applied AI from Scratch
14-02-2022 09:00
Warszawa, ul. Złota 3 / 11
28 godzin
TensorFlow Extended (TFX)
21-02-2022 09:00
Warszawa, ul. Złota 3 / 11
21 godzin
Fraud Detection with Python and TensorFlow
23-02-2022 09:00
Warszawa, ul. Złota 3 / 11
14 godzin
Machine Learning with TensorFlow.js
24-02-2022 09:00
Warszawa, ul. Złota 3 / 11
14 godzin
Deep Learning with TensorFlow 2
14-03-2022 09:00
Warszawa, ul. Złota 3 / 11
21 godzin
Other regions
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Konsultacje
Szkolenie TensorFlow, TensorFlow boot camp, Szkolenia Zdalne TensorFlow, szkolenie wieczorowe TensorFlow, szkolenie weekendowe TensorFlow, Kurs TensorFlow,Kursy TensorFlow, Trener TensorFlow, instruktor TensorFlow, kurs zdalny TensorFlow, edukacja zdalna TensorFlow, nauczanie wirtualne TensorFlow, lekcje UML, nauka przez internet TensorFlow, e-learning TensorFlow, kurs online TensorFlow, wykładowca TensorFlow
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