TensorFlow Training in Liverpool

TensorFlow Training in Liverpool

TensorFlow is an open source software library for deep learning.

Liverpool

Horton House Exchange Flags
Liverpool, MSY L2 3PF
United Kingdom
Merseyside GB
Liverpool
This business centre is situated in Exchange Flags, a beautifully restored landmark property at the heart of Liverpool's rejuvenated business district. This is...Read more

Client Testimonials

Neural Networks Fundamentals using TensorFlow as Example

Topic selection. Style of training. Practice orientation

Commerzbank AG

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

Neural Networks Fundamentals using TensorFlow as Example

I liked the opportunities to ask questions and get more in depth explanations of the theory.

Sharon Ruane - INTEL R&D IRELAND LIMITED

Neural Networks Fundamentals using TensorFlow as Example

Topic selection. Style of training. Practice orientation

Commerzbank AG

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

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

Neural Networks Fundamentals using TensorFlow as Example

Knowledgeable trainer

Sridhar Voorakkara - INTEL R&D IRELAND LIMITED

TensorFlow for Image Recognition

Very updated approach or api (tensorflow, kera, tflearn) to do machine learning

Paul Lee - Hong Kong Productivity Council

TensorFlow Course Events - Liverpool

Code Name Venue Duration Course Date PHP Course Price [Remote / Classroom]
datamodeling Pattern Recognition Liverpool 35 hours Mon, 2018-02-05 09:30 £6500 / £8250
tfir TensorFlow for Image Recognition Liverpool 28 hours Tue, 2018-02-06 09:30 £4400 / £5800
tpuprogramming TPU Programming: Building Neural Network Applications on Tensor Processing Units Liverpool 7 hours Wed, 2018-02-07 09:30 £1100 / £1450
tensorflowserving TensorFlow Serving Liverpool 7 hours Fri, 2018-02-09 09:30 £1100 / £1450
dlfornlp Deep Learning for NLP (Natural Language Processing) Liverpool 28 hours Mon, 2018-02-19 09:30 £4400 / £5800
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example Liverpool 28 hours Mon, 2018-02-26 09:30 £5200 / £6600
tsflw2v Natural Language Processing with TensorFlow Liverpool 35 hours Mon, 2018-02-26 09:30 £6500 / £8250
embeddingprojector Embedding Projector: Visualizing your Training Data Liverpool 14 hours Mon, 2018-02-26 09:30 £2200 / £2900
mlbankingr Machine Learning for Banking (with R) Liverpool 28 hours Tue, 2018-03-06 09:30 £4400 / £5800
tf101 Deep Learning with TensorFlow Liverpool 21 hours Wed, 2018-03-14 09:30 £3300 / £4350
mlbankingpython_ Machine Learning for Banking (with Python) Liverpool 21 hours Wed, 2018-03-14 09:30 £3300 / £4350
dlv Deep Learning for Vision Liverpool 21 hours Wed, 2018-03-21 09:30 £3900 / £4950
datamodeling Pattern Recognition Liverpool 35 hours Mon, 2018-04-02 09:30 £6500 / £8250
tpuprogramming TPU Programming: Building Neural Network Applications on Tensor Processing Units Liverpool 7 hours Mon, 2018-04-02 09:30 £1100 / £1450
tfir TensorFlow for Image Recognition Liverpool 28 hours Tue, 2018-04-03 09:30 £4400 / £5800
undnn Understanding Deep Neural Networks Liverpool 35 hours Mon, 2018-04-23 09:30 £5500 / £7250
tsflw2v Natural Language Processing with TensorFlow Liverpool 35 hours Mon, 2018-04-23 09:30 £6500 / £8250
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example Liverpool 28 hours Tue, 2018-04-24 09:30 £5200 / £6600
dlfornlp Deep Learning for NLP (Natural Language Processing) Liverpool 28 hours Tue, 2018-04-24 09:30 £4400 / £5800
embeddingprojector Embedding Projector: Visualizing your Training Data Liverpool 14 hours Thu, 2018-04-26 09:30 £2200 / £2900
tf101 Deep Learning with TensorFlow Liverpool 21 hours Wed, 2018-05-09 09:30 £3300 / £4350
tensorflowserving TensorFlow Serving Liverpool 7 hours Thu, 2018-05-10 09:30 £1100 / £1450
mlbankingr Machine Learning for Banking (with R) Liverpool 28 hours Mon, 2018-05-14 09:30 £4400 / £5800
dlv Deep Learning for Vision Liverpool 21 hours Mon, 2018-05-14 09:30 £3900 / £4950
tpuprogramming TPU Programming: Building Neural Network Applications on Tensor Processing Units Liverpool 7 hours Mon, 2018-05-21 09:30 £1100 / £1450
tfir TensorFlow for Image Recognition Liverpool 28 hours Tue, 2018-05-29 09:30 £4400 / £5800
datamodeling Pattern Recognition Liverpool 35 hours Mon, 2018-06-04 09:30 £6500 / £8250
mlbankingpython_ Machine Learning for Banking (with Python) Liverpool 21 hours Mon, 2018-06-18 09:30 £3300 / £4350
tsflw2v Natural Language Processing with TensorFlow Liverpool 35 hours Mon, 2018-06-18 09:30 £6500 / £8250
embeddingprojector Embedding Projector: Visualizing your Training Data Liverpool 14 hours Mon, 2018-06-18 09:30 £2200 / £2900
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example Liverpool 28 hours Mon, 2018-06-25 09:30 £5200 / £6600
undnn Understanding Deep Neural Networks Liverpool 35 hours Mon, 2018-06-25 09:30 £5500 / £7250
tensorflowserving TensorFlow Serving Liverpool 7 hours Thu, 2018-06-28 09:30 £1100 / £1450
dlv Deep Learning for Vision Liverpool 21 hours Wed, 2018-07-04 09:30 £3900 / £4950
mlbankingr Machine Learning for Banking (with R) Liverpool 28 hours Mon, 2018-07-09 09:30 £4400 / £5800
tpuprogramming TPU Programming: Building Neural Network Applications on Tensor Processing Units Liverpool 7 hours Tue, 2018-07-10 09:30 £1100 / £1450
tf101 Deep Learning with TensorFlow Liverpool 21 hours Wed, 2018-07-11 09:30 £3300 / £4350
dlfornlp Deep Learning for NLP (Natural Language Processing) Liverpool 28 hours Tue, 2018-07-17 09:30 £4400 / £5800
tfir TensorFlow for Image Recognition Liverpool 28 hours Tue, 2018-07-24 09:30 £4400 / £5800
datamodeling Pattern Recognition Liverpool 35 hours Mon, 2018-07-30 09:30 £6500 / £8250

Course Outlines

Code Name Duration Outline
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:

  • 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
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.

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
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:

  • 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
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:

  • 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
dlv Deep Learning for Vision 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.

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
    Data analysts
    PhD students, researchers and practitioners

 

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:

  • 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
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:

  • 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
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:

  • 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
mlbankingr Machine Learning for Banking (with R) 28 hours

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. R will be used as the programming language.

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 live projects.

Audience

  • Developers
  • Data scientists
  • Banking professionals with a technical background

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice
mlbankingpython_ Machine Learning for Banking (with Python) 21 hours

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. Python will be used as the programming language.

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

  • Developers
  • Data scientists

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice
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:

  • 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.

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