TensorFlow is a 2nd Generation API of Google's open source software library for Deep Learning.
NobleProg live TensorFlow training courses demonstrate through hands-on practice how to use the TensorFlow system to facilitate research in machine learning, and to make it quick and easy to transition from research prototype to production system.
TensorFlow training is available in various formats, including onsite live training and live instructor-led training using an interactive, remote desktop. Local TensorFlow training can be carried out live on customer premises or in NobleProg local corporate training centers.
Topic selection. Style of training. Practice orientation
Commerzbank AG
Course Name: Neural Networks Fundamentals using TensorFlow as Example
Topic selection. Style of training. Practice orientation
Commerzbank AG
Course Name: Neural Networks Fundamentals using TensorFlow as Example
Given outlook of the technology: what technology/process might become more important in the future; see, what the technology can be used for
Commerzbank AG
Course Name: Neural Networks Fundamentals using TensorFlow as Example
Very updated approach or api (tensorflow, kera, tflearn) to do machine learning
Paul Lee - Hong Kong Productivity Council
Course Name: TensorFlow for Image Recognition
I liked the opportunities to ask questions and get more in depth explanations of the theory.
Sharon Ruane - INTEL R&D IRELAND LIMITED
Course Name: Neural Networks Fundamentals using TensorFlow as Example
Very good all round overview.Good background into why Tensorflow operates as it does.
Kieran Conboy - INTEL R&D IRELAND LIMITED
Course Name: Neural Networks Fundamentals using TensorFlow as Example
I was amazed at the standard of this class - I would say that it was university standard.
David Relihan - INTEL R&D IRELAND LIMITED
Course Name: Neural Networks Fundamentals using TensorFlow as Example
Knowledgeable trainer
Sridhar Voorakkara - INTEL R&D IRELAND LIMITED
Course Name: Neural Networks Fundamentals using TensorFlow as Example
Paper explanation
Course Name: Deep Learning with TensorFlow - bespoke
Translated by
论文讲解
I really appreciated the crystal clear answers of Chris to our questions.
Léo Dubus
Course Name: Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple
Translated by
I really appreciated the crystal clear answers of Chris to our questions.
Léo Dubus
| Code | Name | Venue | Duration | Course Date | PHP | Course Price [Remote / Classroom] |
|---|---|---|---|---|---|---|
| Neuralnettf | Neural Networks Fundamentals using TensorFlow as Example | Swansea- Princess House | 28 hours | Mon, 2018-05-07 09:30 | £5200 / £5800 | |
| tensorflowserving | TensorFlow Serving | Swansea- Princess House | 7 hours | Mon, 2018-05-07 09:30 | £1300 / £1450 | |
| embeddingprojector | Embedding Projector: Visualizing your Training Data | Swansea- Princess House | 14 hours | Thu, 2018-05-10 09:30 | £2200 / £2500 | |
| dlfornlp | Deep Learning for NLP (Natural Language Processing) | Swansea- Princess House | 28 hours | Tue, 2018-05-15 09:30 | £5200 / £5800 | |
| dlv | Deep Learning for Vision | Swansea- Princess House | 21 hours | Wed, 2018-05-16 09:30 | £3900 / £4350 | |
| undnn | Understanding Deep Neural Networks | Swansea- Princess House | 35 hours | Mon, 2018-05-21 09:30 | £6500 / £7250 | |
| tfir | TensorFlow for Image Recognition | Swansea- Princess House | 28 hours | Tue, 2018-05-22 09:30 | £5200 / £5800 | |
| tpuprogramming | TPU Programming: Building Neural Network Applications on Tensor Processing Units | Swansea- Princess House | 7 hours | Mon, 2018-06-04 09:30 | £1300 / £1450 | |
| mlbankingpython_ | Machine Learning for Banking (with Python) | Swansea- Princess House | 21 hours | Tue, 2018-06-05 09:30 | £3900 / £4350 | |
| tf101 | Deep Learning with TensorFlow | Swansea- Princess House | 21 hours | Wed, 2018-06-20 09:30 | £3900 / £4350 | |
| tensorflowserving | TensorFlow Serving | Swansea- Princess House | 7 hours | Mon, 2018-06-25 09:30 | £1300 / £1450 | |
| datamodeling | Pattern Recognition | Swansea- Princess House | 35 hours | Mon, 2018-06-25 09:30 | £6500 / £7250 | |
| tsflw2v | Natural Language Processing with TensorFlow | Swansea- Princess House | 35 hours | Mon, 2018-06-25 09:30 | £6500 / £7250 | |
| Neuralnettf | Neural Networks Fundamentals using TensorFlow as Example | Swansea- Princess House | 28 hours | Tue, 2018-07-03 09:30 | £5200 / £5800 | |
| embeddingprojector | Embedding Projector: Visualizing your Training Data | Swansea- Princess House | 14 hours | Wed, 2018-07-04 09:30 | £2200 / £2500 | |
| dlv | Deep Learning for Vision | Swansea- Princess House | 21 hours | Mon, 2018-07-09 09:30 | £3900 / £4350 | |
| dlfornlp | Deep Learning for NLP (Natural Language Processing) | Swansea- Princess House | 28 hours | Tue, 2018-07-10 09:30 | £5200 / £5800 | |
| undnn | Understanding Deep Neural Networks | Swansea- Princess House | 35 hours | Mon, 2018-07-16 09:30 | £6500 / £7250 | |
| tfir | TensorFlow for Image Recognition | Swansea- Princess House | 28 hours | Mon, 2018-07-16 09:30 | £5200 / £5800 | |
| tpuprogramming | TPU Programming: Building Neural Network Applications on Tensor Processing Units | Swansea- Princess House | 7 hours | Mon, 2018-07-23 09:30 | £1300 / £1450 | |
| mlbankingpython_ | Machine Learning for Banking (with Python) | Swansea- Princess House | 21 hours | Mon, 2018-08-06 09:30 | £3900 / £4350 | |
| tensorflowserving | TensorFlow Serving | Swansea- Princess House | 7 hours | Fri, 2018-08-17 09:30 | £1300 / £1450 | |
| tf101 | Deep Learning with TensorFlow | Swansea- Princess House | 21 hours | Mon, 2018-08-20 09:30 | £3900 / £4350 | |
| datamodeling | Pattern Recognition | Swansea- Princess House | 35 hours | Mon, 2018-08-20 09:30 | £6500 / £7250 | |
| tsflw2v | Natural Language Processing with TensorFlow | Swansea- Princess House | 35 hours | Mon, 2018-08-20 09:30 | £6500 / £7250 | |
| embeddingprojector | Embedding Projector: Visualizing your Training Data | Swansea- Princess House | 14 hours | Thu, 2018-08-23 09:30 | £2200 / £2500 | |
| dlv | Deep Learning for Vision | Swansea- Princess House | 21 hours | Wed, 2018-08-29 09:30 | £3900 / £4350 | |
| Neuralnettf | Neural Networks Fundamentals using TensorFlow as Example | Swansea- Princess House | 28 hours | Mon, 2018-09-03 09:30 | £5200 / £5800 | |
| dlfornlp | Deep Learning for NLP (Natural Language Processing) | Swansea- Princess House | 28 hours | Tue, 2018-09-04 09:30 | £5200 / £5800 | |
| tpuprogramming | TPU Programming: Building Neural Network Applications on Tensor Processing Units | Swansea- Princess House | 7 hours | Wed, 2018-09-12 09:30 | £1300 / £1450 | |
| undnn | Understanding Deep Neural Networks | Swansea- Princess House | 35 hours | Mon, 2018-09-17 09:30 | £6500 / £7250 | |
| tfir | TensorFlow for Image Recognition | Swansea- Princess House | 28 hours | Tue, 2018-09-18 09:30 | £5200 / £5800 | |
| datamodeling | Pattern Recognition | Swansea- Princess House | 35 hours | Mon, 2018-10-15 09:30 | £6500 / £7250 | |
| tsflw2v | Natural Language Processing with TensorFlow | Swansea- Princess House | 35 hours | Mon, 2018-10-15 09:30 | £6500 / £7250 | |
| tensorflowserving | TensorFlow Serving | Swansea- Princess House | 7 hours | Tue, 2018-10-16 09:30 | £1300 / £1450 | |
| mlbankingpython_ | Machine Learning for Banking (with Python) | Swansea- Princess House | 21 hours | Tue, 2018-10-16 09:30 | £3900 / £4350 | |
| tf101 | Deep Learning with TensorFlow | Swansea- Princess House | 21 hours | Wed, 2018-10-17 09:30 | £3900 / £4350 | |
| embeddingprojector | Embedding Projector: Visualizing your Training Data | Swansea- Princess House | 14 hours | Wed, 2018-10-17 09:30 | £2200 / £2500 | |
| dlv | Deep Learning for Vision | Swansea- Princess House | 21 hours | Mon, 2018-10-22 09:30 | £3900 / £4350 | |
| Neuralnettf | Neural Networks Fundamentals using TensorFlow as Example | Swansea- Princess House | 28 hours | Tue, 2018-10-30 09:30 | £5200 / £5800 | |
| dlfornlp | Deep Learning for NLP (Natural Language Processing) | Swansea- Princess House | 28 hours | Tue, 2018-10-30 09:30 | £5200 / £5800 | |
| tpuprogramming | TPU Programming: Building Neural Network Applications on Tensor Processing Units | Swansea- Princess House | 7 hours | Tue, 2018-11-06 09:30 | £1300 / £1450 | |
| undnn | Understanding Deep Neural Networks | Swansea- Princess House | 35 hours | Mon, 2018-11-12 09:30 | £6500 / £7250 |
| Code | Name | Duration | Outline |
|---|---|---|---|
| tf101 | Deep Learning with TensorFlow | 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. AudienceThis course is intended for engineers seeking to use TensorFlow for their Deep Learning projects After completing this course, delegates will:
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| tfir | TensorFlow for Image Recognition | 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:
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| tsflw2v | Natural Language Processing with TensorFlow | 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:
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| dlv | Deep Learning for Vision | 21 hours |
AudienceThis 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. |
| Neuralnettf | Neural Networks Fundamentals using TensorFlow as Example | 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 choosing the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow. |
| deepmclrg | Machine Learning & Deep Learning with Python and R | 14 hours | |
| datamodeling | Pattern Recognition | 35 hours |
This course provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. The course is interactive and includes plenty of hands-on exercises, instructor feedback, and testing of knowledge and skills acquired. Audience
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| tpuprogramming | TPU Programming: Building Neural Network Applications on Tensor Processing Units | 7 hours |
The Tensor Processing Unit (TPU) is the architecture which Google has used internally for several years, and is just now becoming available for use by the general public. It includes several optimizations specifically for use in neural networks, including streamlined matrix multiplication, and 8-bit integers instead of 16-bit in order to return appropriate levels of precision. In this instructor-led, live training, 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:
Audience
Format of the course
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| embeddingprojector | Embedding Projector: Visualizing your Training Data | 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:
Audience
Format of the course
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| tensorflowserving | TensorFlow Serving | 7 hours |
TensorFlow Serving is a system for serving machine learning (ML) models to production. In this instructor-led, live training, 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:
Audience
Format of the course
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| mlbankingpython_ | Machine Learning for Banking (with Python) | 21 hours |
Machine Learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications. In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects. Audience
Format of the course
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| undnn | Understanding Deep Neural Networks | 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:
Not all the topics would be covered in a public classroom with 35 hours duration due to the vastness of the subject. The Duration of the complete course will be around 70 hours and not 35 hours. |
| dlfornlp | Deep Learning for NLP (Natural Language Processing) | 28 hours |
Deep Learning for NLP allows a machine to learn simple to complex language processing. Among the tasks currently possible are language translation and caption generation for photos. DL (Deep Learning) is a subset of ML (Machine Learning). Python is a popular programming language that contains libraries for Deep Learning for NLP. In this instructor-led, live training, participants will learn to use Python libraries for NLP (Natural Language Processing) 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:
Audience
Format of the course
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