TensorFlow Training in Oxford

TensorFlow Training in Oxford

Local, instructor-led live TensorFlow training courses demonstrate through interactive discussion and 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 as "onsite live training" or "remote live training". Onsite live training can be carried out locally on customer premises in Oxford or in NobleProg corporate training centres in Oxford. Remote live training is carried out by way of an interactive, remote desktop. NobleProg -- Your Local Training Provider

Oxford
Learn TensorFlow in our training center in Oxford. The top-quality Oxford Business Park Centre is located in a modern building in Oxford's premier business district, just inside the ring road and a 40 minute drive from London. The park offers a thriving environment for businesses of all sizes, working in a range of sectors including engineering, electronics, telecommunications and government agencies. Over 45 local and international companies and about 4,000 people are based here and a number have chosen this as the location for their... Read more

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TensorFlow Course Events - Oxford

CodeNameVenueDurationCourse DateCourse Price [Remote / Classroom]
appaiApplied AI from ScratchOxford28 hoursMon, 2018-11-05 09:30£4400 / £5500
undnnUnderstanding Deep Neural NetworksOxford35 hoursMon, 2018-11-05 09:30£6500 / £7875
embeddingprojectorEmbedding Projector: Visualizing Your Training DataOxford14 hoursTue, 2018-11-06 09:30£2200 / £2750
tensorflowservingTensorFlow ServingOxford7 hoursMon, 2018-11-12 09:30£1300 / £1575
NeuralnettfNeural Networks Fundamentals using TensorFlow as ExampleOxford28 hoursTue, 2018-11-13 09:30£5200 / £6300
dlvDeep Learning for VisionOxford21 hoursWed, 2018-12-05 09:30£3900 / £4725
tfirTensorFlow for Image RecognitionOxford28 hoursMon, 2018-12-10 09:30£5200 / £6300
tsflw2vNatural Language Processing with TensorFlowOxford35 hoursMon, 2018-12-10 09:30£6500 / £7875
tpuprogrammingTPU Programming: Building Neural Network Applications on Tensor Processing UnitsOxford7 hoursTue, 2018-12-18 09:30£1300 / £1575
dlfornlpDeep Learning for NLP (Natural Language Processing)Oxford28 hoursTue, 2018-12-18 09:30£5200 / £6300
tf101Deep Learning with TensorFlowOxford21 hoursWed, 2018-12-26 09:30£3900 / £4725
embeddingprojectorEmbedding Projector: Visualizing Your Training DataOxford14 hoursWed, 2018-12-26 09:30£2200 / £2750
undnnUnderstanding Deep Neural NetworksOxford35 hoursMon, 2018-12-31 09:30£6500 / £7875
tensorflowservingTensorFlow ServingOxford7 hoursWed, 2019-01-09 09:30£1300 / £1575
NeuralnettfNeural Networks Fundamentals using TensorFlow as ExampleOxford28 hoursMon, 2019-01-14 09:30£5200 / £6300
appaiApplied AI from ScratchOxford28 hoursTue, 2019-01-15 09:30£4400 / £5500
dlvDeep Learning for VisionOxford21 hoursTue, 2019-01-29 09:30£3900 / £4725
tsflw2vNatural Language Processing with TensorFlowOxford35 hoursMon, 2019-02-11 09:30£6500 / £7875
dlfornlpDeep Learning for NLP (Natural Language Processing)Oxford28 hoursMon, 2019-02-11 09:30£5200 / £6300
tfirTensorFlow for Image RecognitionOxford28 hoursTue, 2019-02-12 09:30£5200 / £6300
tpuprogrammingTPU Programming: Building Neural Network Applications on Tensor Processing UnitsOxford7 hoursFri, 2019-02-15 09:30£1300 / £1575
tf101Deep Learning with TensorFlowOxford21 hoursTue, 2019-02-19 09:30£3900 / £4725
embeddingprojectorEmbedding Projector: Visualizing Your Training DataOxford14 hoursWed, 2019-02-20 09:30£2200 / £2750
undnnUnderstanding Deep Neural NetworksOxford35 hoursMon, 2019-02-25 09:30£6500 / £7875
tensorflowservingTensorFlow ServingOxford7 hoursFri, 2019-03-08 09:30£1300 / £1575
NeuralnettfNeural Networks Fundamentals using TensorFlow as ExampleOxford28 hoursTue, 2019-03-12 09:30£5200 / £6300
dlvDeep Learning for VisionOxford21 hoursMon, 2019-03-25 09:30£3900 / £4725
appaiApplied AI from ScratchOxford28 hoursMon, 2019-04-08 09:30£4400 / £5500
tfirTensorFlow for Image RecognitionOxford28 hoursMon, 2019-04-08 09:30£5200 / £6300
dlfornlpDeep Learning for NLP (Natural Language Processing)Oxford28 hoursMon, 2019-04-08 09:30£5200 / £6300

TensorFlow Course Outlines in Oxford

CodeNameDurationOverview
tf101Deep Learning with TensorFlow21 hoursTensorFlow 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
tfirTensorFlow for Image Recognition28 hoursThis 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
tsflw2vNatural Language Processing with TensorFlow35 hoursTensorFlow™ 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
dlvDeep Learning for Vision21 hoursAudience

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.
NeuralnettfNeural Networks Fundamentals using TensorFlow as Example28 hoursThis 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.
tpuprogrammingTPU Programming: Building Neural Network Applications on Tensor Processing Units7 hoursThe 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:

- 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

Audience

- Developers
- Researchers
- Engineers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
embeddingprojectorEmbedding Projector: Visualizing Your Training Data14 hoursEmbedding 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
tensorflowservingTensorFlow Serving7 hoursTensorFlow 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:

- 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

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
undnnUnderstanding Deep Neural Networks35 hoursThis 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

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.
dlfornlpDeep Learning for NLP (Natural Language Processing)28 hoursDeep 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:

- 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

Audience

- Programmers with interest in linguistics
- Programmers who seek an understanding of NLP (Natural Language Processing)

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
appaiApplied AI from Scratch28 hoursThis 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.
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Course Discounts

Course Venue Course Date Course Price [Remote / Classroom]
Advanced Statistics using SPSS Predictive Analytics Software Birmingham Mon, 2018-10-22 09:30 £5148 / £6448
Building Augmented Reality Applications with Vuforia and Unity Glasgow Wed, 2018-10-24 09:30 £2178 / £2878
Impact Evaluation – Quantitative Analysis London, Hatton Garden Wed, 2018-10-24 09:30 £2574 / £3324
Statistics with SPSS Predictive Analytics Software Cambridge Thu, 2018-11-01 09:30 £2574 / £3024
Programming in Scala Cambridge Thu, 2018-11-01 09:30 £2178 / £2628
CakePHP: Rapid Web Application Development Birmingham Tue, 2018-11-06 09:30 £4356 / £5656
AWS: A Hands-on Introduction to Cloud Computing Brighton Wed, 2018-11-07 09:30 £1287 / £1487
JMeter Fundamentals and JMeter Advanced Edinburgh Training and Conference Venue Mon, 2018-12-03 09:30 £2178 / £2578
HAProxy Administration London, Hatton Garden Mon, 2018-12-03 09:30 £2178 / £2928
Social Media Marketing Oxford Wed, 2018-12-12 09:30 N/A / £1364
Test Automation with Selenium Manchester, King Street Wed, 2018-12-12 09:30 £3267 / £4242

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