ローカルのインストラクター主導のライブTensorFlowトレーニングコースでは、インタラクティブなディスカッションと実践的な実践を通じて、 TensorFlowシステムを使用して機械学習の研究を促進し、研究プロトタイプから生産システムへの移行を迅速かつ容易にする方法を実演します。 TensorFlowトレーニングは、「オンサイトライブトレーニング」または「リモートライブトレーニング」として利用できます。オンサイトのライブトレーニングは、お客様の施設でローカルに実施できます。 日本またはのNobleProg企業トレーニングセンター日本 。リモートライブトレーニングは、インタラクティブなリモートデスクトップ経由で実行されます。 NobleProg-ローカルトレーニングプロバイダー
Machine Translated
お客様の声
私たちの質問に対するクリスの明確な答えを本当に感謝しています。
Léo Dubus
コース: Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple
Machine Translated
私は一般的に知識のあるトレーナーを楽しんだ。
Sridhar Voorakkara
コース: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
私はこのクラスのスタンダードに驚いていました。私はそれが大学の標準であると言います。
David Relihan
コース: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
全体的に非常に良い概要です。 GoそれがないようTensorflowが動作する理由にODの背景を。
Kieran Conboy
コース: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
私は質問をする機会が好きで、理論の深い説明をもっと得た。
Sharon Ruane
コース: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
機械学習を行うための非常に更新されたアプローチまたはCPI（テンソルフロー、時代遅れ、学習）。
Paul Lee
コース: TensorFlow for Image Recognition
Machine Translated
技術の見通し：将来どのような技術/プロセスが重要になるか。どのような技術が使用されるのかを見てください。
Commerzbank AG
コース: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
私はトピック選択の恩恵を受けました。トレーニングのスタイル。オリエンテーションを練習する。
Commerzbank AG
コース: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
トピックの広い範囲とリーダーの実質的な知識をカバーしています。
ING Bank Śląski S.A.; Kamil Kurek Programowanie
コース: Understanding Deep Neural Networks
Machine Translated
不足
ING Bank Śląski S.A.; Kamil Kurek Programowanie
コース: Understanding Deep Neural Networks
Machine Translated
講師の大きな理論的、実践的な知識。トレーナーのコミュニケーション。コース中に、あなたは質問し、満足のいく答えを得ることができます。
Kamil Kurek - ING Bank Śląski S.A.; Kamil Kurek Programowanie
コース: Understanding Deep Neural Networks
Machine Translated
アルゴリズムを実装した実用的な部分。これにより、トピックの理解を深めることができました。
ING Bank Śląski S.A.; Kamil Kurek Programowanie
コース: Understanding Deep Neural Networks
Machine Translated
演習とその上で実装された例
Paweł Orzechowski - ING Bank Śląski S.A.; Kamil Kurek Programowanie
コース: Understanding Deep Neural Networks
Machine Translated
議論された事例と問題。
ING Bank Śląski S.A.; Kamil Kurek Programowanie
コース: Understanding Deep Neural Networks
Machine Translated
実質的な知識、コミットメント、知識を伝達する情熱的な方法。理論的講義後の実践例
Janusz Chrobot - ING Bank Śląski S.A.; Kamil Kurek Programowanie
コース: Understanding Deep Neural Networks
Machine Translated
Maciej氏が実際に作成した演習
ING Bank Śląski S.A.; Kamil Kurek Programowanie
コース: Understanding Deep Neural Networks
Machine Translated
人間の識別と回路基板の不良点検出
王 春柱 - 中移物联网
コース: Deep Learning for NLP (Natural Language Processing)
Machine Translated
示す
中移物联网
コース: Deep Learning for NLP (Natural Language Processing)
Machine Translated
フェイスエリアについて。
中移物联网
コース: Deep Learning for NLP (Natural Language Processing)
Machine Translated
実用的なヒントがたくさん
Pawel Dawidowski - ABB Sp. z o.o.
コース: Deep Learning with TensorFlow
Machine Translated
ソリューションの実装に関する多くの情報
Michał Smolana - ABB Sp. z o.o.
コース: Deep Learning with TensorFlow
Machine Translated
幅広いAI / IT / SQL / IoT問題からの講師の実用的なヒントと知識の数々。
ABB Sp. z o.o.
コース: Deep Learning with TensorFlow
Machine Translated
ゼロに近い知識から始めて、最終的には自分のネットワークを構築して訓練することができました。
Huawei Technologies Duesseldorf GmbH
コース: TensorFlow for Image Recognition
Machine Translated
トマシュは本当によく情報を知っているし、コースはよくペースだった。
Raju Krishnamurthy - Google
コース: TensorFlow Extended (TFX)
Machine Translated
トレーナーは非常に知識があり、質問にオープンで、図を描くのが好きで、かなり良い方法で物事を説明しました
コース: Deep Learning with TensorFlow 2.0
Machine Translated
トレーナーは非常に知識があり、質問にオープンで、図を描くのが好きで、かなり良い方法で物事を説明しました
コース: Deep Learning with TensorFlow 2.0
Machine Translated
TensorFlowコース概要
コース名
期間
概要
コース名
期間
概要
21 時間
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 時間
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 時間
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 時間
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 時間
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 時間
In this instructor-led, live training in 日本, 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 時間
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 時間
In this instructor-led, live training in 日本 (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 時間
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 時間
In this instructor-led, live training in 日本, 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 時間
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 時間
This instructor-led, live training in 日本 (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 時間
This instructor-led, live training in 日本 (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 時間
This instructor-led, live training in 日本 (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 時間
This instructor-led, live training in 日本 (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:
今後のTensorFlowコース
Understanding Deep Neural Networks
07-02-2022 10:00
35 時間
Deep Learning for NLP (Natural Language Processing)
14-02-2022 10:00
28 時間
TensorFlow Serving
14-03-2022 10:00
7 時間
Embedding Projector: Visualizing Your Training Data
24-03-2022 10:00
14 時間
TPU Programming: Building Neural Network Applications on Tensor Processing Units
28-03-2022 10:00
7 時間
週末TensorFlowコース, 夜のTensorFlowトレーニング, TensorFlowブートキャンプ, TensorFlow インストラクターよる, 週末TensorFlowトレーニング, 夜のTensorFlowコース, TensorFlow指導, TensorFlowインストラクター, TensorFlowレーナー, TensorFlowレーナーコース, TensorFlowクラス, TensorFlowオンサイト, TensorFlowプライベートコース, TensorFlow1対1のトレーニング
.jpg "procter & gamble (p&g)")
_ireland.gif "environmental protection agency (epa) ireland")
.png "european space agency (esa)")
