Neural Networks Training in Wales

Neural Networks Training in Wales

A Neural Network (NN), or Artificial Neural Network (ANN), is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI.

NobleProg onsite live Neural Network training courses demonstrate through hands-on practice how to construct Neural Networks using a number of mostly open-source toolkits and libraries as well as how to utilize the power of advanced hardware (GPUs) and optimization techniques involving distributed computing and big data. Our Neural Network courses are based on popular programming languages such as Python, Java, R language, and powerful libraries, including TensorFlow, Torch, Caffe, Theano and more. Our Neural Network courses cover both theory and implementation using a number of neural network implementations such as Deep Neural Networks (DNN), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN).

Neural Network training is available in various formats, including onsite live training and live instructor-led training using an interactive, remote desktop setup. Local Neural Network training can be carried out live on customer premises or in NobleProg local training centers.

Cardiff

Radisson Blu Hotel
Radisson Blu Hotel, Meridian Gate - Bute Terrace
Cardiff, GLA CF10 2FL
United Kingdom
Glamorgan GB
Cardiff
The Radisson Blu Hotel in Cardiff city centre is the perfect hub for your Welsh adventure Close to several public transportation options, our hotel in Cardiff...Read more

Client Testimonials

Neural Networks Course Events - Wales

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, 2018-03-12 09:30 £5200 / £5800
neuralnet Introduction to the use of neural networks Cardiff 7 hours Mon, 2018-03-12 09:30 £1300 / £1600
snorkel Snorkel: Rapidly process training data Swansea- Princess House 7 hours Mon, 2018-03-12 09:30 £1100 / £1250
dlforfinancewithpython Deep Learning for Finance (with Python) Swansea- Princess House 28 hours Tue, 2018-03-13 09:30 £4400 / £5000
dlfornlp Deep Learning for NLP (Natural Language Processing) Swansea- Princess House 28 hours Tue, 2018-03-13 09:30 £4400 / £5000
OpenNN OpenNN: Implementing neural networks Cardiff 14 hours Tue, 2018-03-13 09:30 £2600 / £3200
mlintro Introduction to Machine Learning Swansea- Princess House 7 hours Tue, 2018-03-13 09:30 £1300 / £1450
annmldt Artificial Neural Networks, Machine Learning, Deep Thinking Cardiff 21 hours Wed, 2018-03-14 09:30 £3900 / £4800
aiint Artificial Intelligence Overview Cardiff 7 hours Thu, 2018-03-15 09:30 £1300 / £1600
encogadv Encog: Advanced Machine Learning Swansea- Princess House 14 hours Mon, 2018-03-19 09:30 £2200 / £2500
dlfornlp Deep Learning for NLP (Natural Language Processing) Cardiff 28 hours Mon, 2018-03-19 09:30 £4400 / £5600
snorkel Snorkel: Rapidly process training data Cardiff 7 hours Mon, 2018-03-19 09:30 £1100 / £1400
mlbankingpython_ Machine Learning for Banking (with Python) Cardiff 21 hours Wed, 2018-03-21 09:30 £3300 / £4200
mldt Machine Learning and Deep Learning Swansea- Princess House 21 hours Wed, 2018-03-21 09:30 £3900 / £4350
matlabdl Matlab for Deep Learning Cardiff 14 hours Thu, 2018-03-22 09:30 £2200 / £2800
appliedml Applied Machine Learning Swansea- Princess House 14 hours Thu, 2018-03-22 09:30 £2600 / £2900
tpuprogramming TPU Programming: Building Neural Network Applications on Tensor Processing Units Cardiff 7 hours Fri, 2018-03-23 09:30 £1100 / £1400
mlintro Introduction to Machine Learning Cardiff 7 hours Mon, 2018-03-26 09:30 £1300 / £1600
mlbankingpython_ Machine Learning for Banking (with Python) Swansea- Princess House 21 hours Mon, 2018-03-26 09:30 £3300 / £3750
aiauto Artificial Intelligence in Automotive Cardiff 14 hours Mon, 2018-03-26 09:30 £2600 / £3200
undnn Understanding Deep Neural Networks Cardiff 35 hours Mon, 2018-03-26 09:30 £5500 / £7000
bspkannmldt Artificial Neural Networks, Machine Learning and Deep Thinking Cardiff 21 hours Tue, 2018-03-27 09:30 £3300 / £4200
aiint Artificial Intelligence Overview Swansea- Princess House 7 hours Tue, 2018-03-27 09:30 £1300 / £1450
encogintro Encog: Introduction to Machine Learning Cardiff 14 hours Wed, 2018-03-28 09:30 £2200 / £2800
rneuralnet Neural Network in R Swansea- Princess House 14 hours Thu, 2018-03-29 09:30 £2600 / £2900
OpenNN OpenNN: Implementing neural networks Swansea- Princess House 14 hours Thu, 2018-03-29 09:30 £2600 / £2900
facebooknmt Facebook NMT: Setting up a Neural Machine Translation System Swansea- Princess House 7 hours Fri, 2018-03-30 09:30 £1100 / £1250
datamodeling Pattern Recognition Cardiff 35 hours Mon, 2018-04-02 09:30 £6500 / £8000
MicrosoftCognitiveToolkit Microsoft Cognitive Toolkit 2.x Cardiff 21 hours Mon, 2018-04-02 09:30 N/A / £4200
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example Cardiff 28 hours Tue, 2018-04-03 09:30 £5200 / £6400
MicrosoftCognitiveToolkit Microsoft Cognitive Toolkit 2.x Swansea- Princess House 21 hours Tue, 2018-04-03 09:30 N/A / £3750
dlforbankingwithpython Deep Learning for Banking (with Python) Cardiff 28 hours Tue, 2018-04-03 09:30 £4400 / £5600
MLFWR1 Machine Learning Fundamentals with R Swansea- Princess House 14 hours Thu, 2018-04-05 09:30 £2600 / £2900
aiintrozero From Zero to AI Cardiff 35 hours Mon, 2018-04-09 09:30 £6500 / £8000
deeplearning1 Introduction to Deep Learning Cardiff 21 hours Mon, 2018-04-09 09:30 £3900 / £4800
aiauto Artificial Intelligence in Automotive Swansea- Princess House 14 hours Tue, 2018-04-10 09:30 £2600 / £2900
neuralnet Introduction to the use of neural networks Swansea- Princess House 7 hours Wed, 2018-04-11 09:30 £1300 / £1450
Fairseq Fairseq: Setting up a CNN-based machine translation system Cardiff 7 hours Mon, 2018-04-16 09:30 £1100 / £1400
appliedml Applied Machine Learning Cardiff 14 hours Mon, 2018-04-16 09:30 £2600 / £3200
Torch Torch: Getting started with Machine and Deep Learning Swansea- Princess House 21 hours Mon, 2018-04-16 09:30 N/A / £4350
encogintro Encog: Introduction to Machine Learning Swansea- Princess House 14 hours Mon, 2018-04-16 09:30 £2200 / £2500
tpuprogramming TPU Programming: Building Neural Network Applications on Tensor Processing Units Swansea- Princess House 7 hours Mon, 2018-04-16 09:30 £1100 / £1250
rneuralnet Neural Network in R Cardiff 14 hours Tue, 2018-04-17 09:30 £2600 / £3200
dlforfinancewithpython Deep Learning for Finance (with Python) Cardiff 28 hours Tue, 2018-04-17 09:30 £4400 / £5600
opennmt OpenNMT: Setting up a Neural Machine Translation System Swansea- Princess House 7 hours Thu, 2018-04-19 09:30 £1100 / £1250
opennmt OpenNMT: Setting up a Neural Machine Translation System Cardiff 7 hours Fri, 2018-04-20 09:30 £1100 / £1400
facebooknmt Facebook NMT: Setting up a Neural Machine Translation System Cardiff 7 hours Fri, 2018-04-20 09:30 £1100 / £1400
deeplearning1 Introduction to Deep Learning Swansea- Princess House 21 hours Mon, 2018-04-23 09:30 £3900 / £4350
bspkannmldt Artificial Neural Networks, Machine Learning and Deep Thinking Swansea- Princess House 21 hours Tue, 2018-04-24 09:30 £3300 / £3750
Torch Torch: Getting started with Machine and Deep Learning Cardiff 21 hours Tue, 2018-04-24 09:30 N/A / £4800
d2dbdpa From Data to Decision with Big Data and Predictive Analytics Cardiff 21 hours Tue, 2018-04-24 09:30 £3900 / £4800
mldt Machine Learning and Deep Learning Cardiff 21 hours Tue, 2018-04-24 09:30 £3900 / £4800
Fairsec Fairsec: Setting up a CNN-based machine translation system Cardiff 7 hours Wed, 2018-04-25 09:30 £1100 / £1400
Fairsec Fairsec: Setting up a CNN-based machine translation system Swansea- Princess House 7 hours Wed, 2018-04-25 09:30 £1100 / £1250
MLFWR1 Machine Learning Fundamentals with R Cardiff 14 hours Wed, 2018-04-25 09:30 £2600 / £3200
Fairseq Fairseq: Setting up a CNN-based machine translation system Swansea- Princess House 7 hours Fri, 2018-04-27 09:30 £1100 / £1250
datamodeling Pattern Recognition Swansea- Princess House 35 hours Mon, 2018-04-30 09:30 £6500 / £7250
annmldt Artificial Neural Networks, Machine Learning, Deep Thinking Swansea- Princess House 21 hours Mon, 2018-04-30 09:30 £3900 / £4350
aiintrozero From Zero to AI Swansea- Princess House 35 hours Mon, 2018-04-30 09:30 £6500 / £7250
d2dbdpa From Data to Decision with Big Data and Predictive Analytics Swansea- Princess House 21 hours Mon, 2018-04-30 09:30 £3900 / £4350
dlforbankingwithpython Deep Learning for Banking (with Python) Swansea- Princess House 28 hours Mon, 2018-04-30 09:30 £4400 / £5000
matlabdl Matlab for Deep Learning Swansea- Princess House 14 hours Tue, 2018-05-01 09:30 £2200 / £2500
OpenNN OpenNN: Implementing neural networks Cardiff 14 hours Wed, 2018-05-02 09:30 £2600 / £3200
aiint Artificial Intelligence Overview Cardiff 7 hours Thu, 2018-05-03 09:30 £1300 / £1600
snorkel Snorkel: Rapidly process training data Swansea- Princess House 7 hours Thu, 2018-05-03 09:30 £1100 / £1250
neuralnet Introduction to the use of neural networks Cardiff 7 hours Thu, 2018-05-03 09:30 £1300 / £1600
mlintro Introduction to Machine Learning Swansea- Princess House 7 hours Fri, 2018-05-04 09:30 £1300 / £1450
encogadv Encog: Advanced Machine Learning Cardiff 14 hours Mon, 2018-05-07 09:30 £2200 / £2800
annmldt Artificial Neural Networks, Machine Learning, Deep Thinking Cardiff 21 hours Mon, 2018-05-07 09:30 £3900 / £4800
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example Swansea- Princess House 28 hours Mon, 2018-05-07 09:30 £5200 / £5800
snorkel Snorkel: Rapidly process training data Cardiff 7 hours Tue, 2018-05-08 09:30 £1100 / £1400
tpuprogramming TPU Programming: Building Neural Network Applications on Tensor Processing Units Cardiff 7 hours Fri, 2018-05-11 09:30 £1100 / £1400
mldt Machine Learning and Deep Learning Swansea- Princess House 21 hours Mon, 2018-05-14 09:30 £3900 / £4350
aiauto Artificial Intelligence in Automotive Cardiff 14 hours Tue, 2018-05-15 09:30 £2600 / £3200
dlfornlp Deep Learning for NLP (Natural Language Processing) Swansea- Princess House 28 hours Tue, 2018-05-15 09:30 £4400 / £5000
appliedml Applied Machine Learning Swansea- Princess House 14 hours Tue, 2018-05-15 09:30 £2600 / £2900
mlintro Introduction to Machine Learning Cardiff 7 hours Wed, 2018-05-16 09:30 £1300 / £1600
encogadv Encog: Advanced Machine Learning Swansea- Princess House 14 hours Wed, 2018-05-16 09:30 £2200 / £2500
aiint Artificial Intelligence Overview Swansea- Princess House 7 hours Thu, 2018-05-17 09:30 £1300 / £1450
encogintro Encog: Introduction to Machine Learning Cardiff 14 hours Thu, 2018-05-17 09:30 £2200 / £2800
facebooknmt Facebook NMT: Setting up a Neural Machine Translation System Swansea- Princess House 7 hours Fri, 2018-05-18 09:30 £1100 / £1250
undnn Understanding Deep Neural Networks Swansea- Princess House 35 hours Mon, 2018-05-21 09:30 £5500 / £6250
OpenNN OpenNN: Implementing neural networks Swansea- Princess House 14 hours Mon, 2018-05-21 09:30 £2600 / £2900
dlfornlp Deep Learning for NLP (Natural Language Processing) Cardiff 28 hours Mon, 2018-05-21 09:30 £4400 / £5600
rneuralnet Neural Network in R Swansea- Princess House 14 hours Mon, 2018-05-21 09:30 £2600 / £2900
matlabdl Matlab for Deep Learning Cardiff 14 hours Tue, 2018-05-22 09:30 £2200 / £2800
bspkannmldt Artificial Neural Networks, Machine Learning and Deep Thinking Cardiff 21 hours Tue, 2018-05-22 09:30 £3300 / £4200
MicrosoftCognitiveToolkit Microsoft Cognitive Toolkit 2.x Cardiff 21 hours Wed, 2018-05-23 09:30 N/A / £4200
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example Cardiff 28 hours Tue, 2018-05-29 09:30 £5200 / £6400
MicrosoftCognitiveToolkit Microsoft Cognitive Toolkit 2.x Swansea- Princess House 21 hours Tue, 2018-05-29 09:30 N/A / £3750
MLFWR1 Machine Learning Fundamentals with R Swansea- Princess House 14 hours Wed, 2018-05-30 09:30 £2600 / £2900
aiauto Artificial Intelligence in Automotive Swansea- Princess House 14 hours Wed, 2018-05-30 09:30 £2600 / £2900
deeplearning1 Introduction to Deep Learning Cardiff 21 hours Wed, 2018-05-30 09:30 £3900 / £4800
neuralnet Introduction to the use of neural networks Swansea- Princess House 7 hours Fri, 2018-06-01 09:30 £1300 / £1450
tpuprogramming TPU Programming: Building Neural Network Applications on Tensor Processing Units Swansea- Princess House 7 hours Mon, 2018-06-04 09:30 £1100 / £1250
Fairseq Fairseq: Setting up a CNN-based machine translation system Cardiff 7 hours Mon, 2018-06-04 09:30 £1100 / £1400
datamodeling Pattern Recognition Cardiff 35 hours Mon, 2018-06-04 09:30 £6500 / £8000
aiintrozero From Zero to AI Cardiff 35 hours Mon, 2018-06-04 09:30 £6500 / £8000
mlbankingpython_ Machine Learning for Banking (with Python) Swansea- Princess House 21 hours Tue, 2018-06-05 09:30 £3300 / £3750
encogintro Encog: Introduction to Machine Learning Swansea- Princess House 14 hours Tue, 2018-06-05 09:30 £2200 / £2500

Course Outlines

Code Name Duration Outline
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.

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
mldt Machine Learning and Deep Learning 21 hours

This course covers AI (emphasizing Machine Learning and Deep Learning)

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.

deepmclrg Machine Learning & Deep Learning with Python and R 14 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:

  • 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
aiint Artificial Intelligence Overview 7 hours

This course has been created for managers, solutions architects, innovation officers, CTOs, software architects and everyone who is interested overview of applied artificial intelligence and the nearest forecast for its development.

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

 

dlforbankingwithpython Deep Learning for Banking (with Python) 28 hours

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability.

In this instructor-led, live training, participants will learn how to implement deep learning models for banking using Python as they step through the creation of a deep learning credit risk model.

By the end of this training, participants will be able to:

  • Understand the fundamental concepts of deep learning
  • Learn the applications and uses of deep learning in banking
  • Use Python, Keras, and TensorFlow to create deep learning models for banking
  • Build their own deep learning credit risk model using Python

Audience

  • Developers
  • Data scientists

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice
neuralnet Introduction to the use of neural networks 7 hours

The training is aimed at people who want to learn the basics of neural networks and their applications.

Torch Torch: Getting started with Machine and Deep Learning 21 hours

Torch is an open source machine learning library and a scientific computing framework based on the Lua programming language. It provides a development environment for numerics, machine learning, and computer vision, with a particular emphasis on deep learning and convolutional nets. It is one of the fastest and most flexible frameworks for Machine and Deep Learning and is used by companies such as Facebook, Google, Twitter, NVIDIA, AMD, Intel, and many others.

In this course we cover the principles of Torch, its unique features, and how it can be applied in real-world applications. We step through numerous hands-on exercises all throughout, demonstrating and practicing the concepts learned.

By the end of the course, participants will have a thorough understanding of Torch's underlying features and capabilities as well as its role and contribution within the AI space compared to other frameworks and libraries. Participants will have also received the necessary practice to implement Torch in their own projects.

Audience
    Software developers and programmers wishing to enable Machine and Deep Learning within their applications

Format of the course
    Overview of Machine and Deep Learning
    In-class coding and integration exercises
    Test questions sprinkled along the way to check understanding

dlforfinancewithpython Deep Learning for Finance (with Python) 28 hours

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability.

In this instructor-led, live training, participants will learn how to implement deep learning models for finance using Python as they step through the creation of a deep learning stock price prediction model.

By the end of this training, participants will be able to:

  • Understand the fundamental concepts of deep learning
  • Learn the applications and uses of deep learning in finance
  • Use Python, Keras, and TensorFlow to create deep learning models for finance
  • Build their own deep learning stock price prediction model using Python

Audience

  • Developers
  • Data scientists

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice
rneuralnet Neural Network in R 14 hours

This course is an introduction to applying neural networks in real world problems using R-project software.

OpenNN OpenNN: Implementing neural networks 14 hours

OpenNN is an open-source class library written in C++  which implements neural networks, for use in machine learning.

In this course we go over the principles of neural networks and use OpenNN to implement a sample application.

Audience
    Software developers and programmers wishing to create Deep Learning applications.

Format of the course
    Lecture and discussion coupled with hands-on exercises.

drlpython Deep Reinforcement Learning with Python 21 hours

Deep Reinforcement Learning refers to the ability of an "artificial agents" to learn by trial-and-error and rewards-and-punishments. An artificial agent aims to emulate a human's ability to obtain and construct knowledge on its own, directly from raw inputs such as vision. To realize reinforcement learning, deep learning and neural networks are used. Reinforcement learning is different from machine learning and does not rely on supervised and unsupervised learning approaches.

In this instructor-led, live training, participants will learn the fundamentals of Deep Reinforcement Learning as they step through the creation of a Deep Learning Agent.

By the end of this training, participants will be able to:

  • Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning
  • Apply advanced Reinforcement Learning algorithms to solve real-world problems
  • Build a Deep Learning Agent

Audience

  • Developers
  • Data Scientists

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice
d2dbdpa From Data to Decision with Big Data and Predictive Analytics 21 hours

Audience

If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you.

It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing.

It is not aimed at people configuring the solution, those people will benefit from the big picture though.

Delivery Mode

During the course delegates will be presented with working examples of mostly open source technologies.

Short lectures will be followed by presentation and simple exercises by the participants

Content and Software used

All software used is updated each time the course is run so we check the newest versions possible.

It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with machine learning.

opennmt OpenNMT: Setting up a Neural Machine Translation System 7 hours

OpenNMT is a full-featured, open-source (MIT) neural machine translation system that utilizes the Torch mathematical toolkit.

In this training participants will learn how to set up and use OpenNMT to carry out translation of various sample data sets. The course starts with an overview of neural networks as they apply to machine translation. Participants will carry out live exercises throughout the course to demonstrate their understanding of the concepts learned and get feedback from the instructor. By the end of this training, participants will have the knowledge and practice needed to implement a live OpenNMT solution.

Source and target language samples will be pre-arranged per the audience's requirements.

Audience

  • Localization specialists with a technical background
  • Global content managers
  • Localization engineers
  • Software developers in charge of implementing global content solutions

Format of the course

  • Part lecture, part discussion, heavy hands-on practice
mlintro Introduction to Machine Learning 7 hours

This training course is for people that would like to apply basic Machine Learning techniques in practical applications.

Audience

Data scientists and statisticians that have some familiarity with machine learning and know how to program R. The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give a practical introduction to machine learning to participants interested in applying the methods at work

Sector specific examples are used to make the training relevant to the audience.

Fairsec Fairsec: Setting up a CNN-based machine translation system 7 hours

Fairseq is an open-source sequence-to-sequence learning toolkit created by Facebok for use in Neural Machine Translation (NMT).

In this training participants will learn how to use Fairseq to carry out translation of sample content. By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution. Source and target language content samples can be prepared according to audience's requirements.

Audience

  • Localization specialists with a technical background
  • Global content managers
  • Localization engineers
  • Software developers in charge of implementing global content solutions

Format of the course
    Part lecture, part discussion, heavy hands-on practice

appliedml Applied Machine Learning 14 hours

This training course is for people that would like to apply Machine Learning in practical applications.

Audience

This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization.

The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work.

Sector specific examples are used to make the training relevant to the audience.

Fairseq Fairseq: Setting up a CNN-based machine translation system 7 hours

Fairseq is an open-source sequence-to-sequence learning toolkit created by Facebok for use in Neural Machine Translation (NMT).

In this training participants will learn how to use Fairseq to carry out translation of sample content.

By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution.

Audience

  • Localization specialists with a technical background
  • Global content managers
  • Localization engineers
  • Software developers in charge of implementing global content solutions

Format of the course
    Part lecture, part discussion, heavy hands-on practice

Note

  • If you wish to use specific source and target language content, please contact us to arrange.
MLFWR1 Machine Learning Fundamentals with R 14 hours

The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the R programming platform and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.

Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.

facebooknmt Facebook NMT: Setting up a Neural Machine Translation System 7 hours

Fairseq is an open-source sequence-to-sequence learning toolkit created by Facebok for use in Neural Machine Translation (NMT).

In this training participants will learn how to use Fairseq to carry out translation of sample content.

By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution.

Audience

  • Localization specialists with a technical background
  • Global content managers
  • Localization engineers
  • Software developers in charge of implementing global content solutions

Format of the course

  • Part lecture, part discussion, heavy hands-on practice

Note

  • If you wish to use specific source and target language content, please contact us to arrange.
annmldt Artificial Neural Networks, Machine Learning, Deep Thinking 21 hours
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
deeplearning1 Introduction to Deep Learning 21 hours This course is general overview for Deep Learning without going too deep into any specific methods. It is suitable for people who want to start using Deep learning to enhance their accuracy of prediction.
MicrosoftCognitiveToolkit Microsoft Cognitive Toolkit 2.x 21 hours

Microsoft Cognitive Toolkit 2.x (previously CNTK) is an open-source, commercial-grade toolkit that trains deep learning algorithms to learn like the human brain. According to Microsoft, CNTK can be 5-10x faster than TensorFlow on recurrent networks, and 2 to 3 times faster than TensorFlow for image-related tasks.

In this instructor-led, live training, participants will learn how to use Microsoft Cognitive Toolkit to create, train and evaluate deep learning algorithms for use in commercial-grade AI applications involving multiple types of data such data, speech, text, and images.

By the end of this training, participants will be able to:

  • Access CNTK as a library from within a Python, C#, or C++ program
  • Use CNTK as a standalone machine learning tool through its own model description language (BrainScript)
  • Use the CNTK model evaluation functionality from a Java program
  • Combine feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs)
  • Scale computation capacity on CPUs, GPUs and multiple machines
  • Access massive datasets using existing programming languages and algorithms

Audience

  • Developers
  • Data scientists

Format of the course

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

Note

  • If you wish to customize any part of this training, including the programming language of choice, please contact us to arrange.
bspkannmldt Artificial Neural Networks, Machine Learning and Deep Thinking 21 hours
snorkel Snorkel: Rapidly process training data 7 hours

Snorkel is a system for rapidly creating, modeling, and managing training data. It focuses on accelerating the development of structured or "dark" data extraction applications for domains in which large labeled training sets are not available or easy to obtain.

In this instructor-led, live training, participants will learn techniques for extracting value from unstructured data such as text, tables, figures, and images through modeling of training data with Snorkel.

By the end of this training, participants will be able to:

  • Programmatically create training sets to enable the labeling of massive training sets
  • Train high-quality end models by first modeling noisy training sets
  • Use Snorkel to implement weak supervision techniques and apply data programming to weakly-supervised machine learning systems

Audience

  • Developers
  • Data scientists

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice
cntk Using Computer Network ToolKit (CNTK) 28 hours

Computer Network ToolKit (CNTK) is Microsoft's Open Source, Multi-machine, Multi-GPU, Highly efficent RNN training machine learning framework for speech, text, and images.

Audience

This course is directed at engineers and architects aiming to utilize CNTK in their projects.

encogadv Encog: Advanced Machine Learning 14 hours

Encog is an open-source machine learning framework for Java and .Net.

In this instructor-led, live training, participants will learn advanced machine learning techniques for building accurate neural network predictive models.

By the end of this training, participants will be able to:

  • Implement different neural networks optimization techniques to resolve underfitting and overfitting
  • Understand and choose from a number of neural network architectures
  • Implement supervised feed forward and feedback networks

Audience

  • Developers
  • Analysts
  • Data scientists

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice
aiintrozero From Zero to AI 35 hours

This course is created for people who have no previous experience in probability and statistics.

encogintro Encog: Introduction to Machine Learning 14 hours

Encog is an open-source machine learning framework for Java and .Net.

In this instructor-led, live training, participants will learn how to create various neural network components using ENCOG. Real-world case studies will be discussed and machine language based solutions to these problems will be explored.

By the end of this training, participants will be able to:

  • Prepare data for neural networks using the normalization process
  • Implement feed forward networks and propagation training methodologies
  • Implement classification and regression tasks
  • Model and train neural networks using Encog's GUI based workbench
  • Integrate neural network support into real-world applications

Audience

  • Developers
  • Analysts
  • Data scientists

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice
aiauto Artificial Intelligence in Automotive 14 hours

This course covers AI (emphasizing Machine Learning and Deep Learning) in Automotive Industry. It helps to determine which technology can be (potentially) used in multiple situation in a car: from simple automation, image recognition to autonomous decision making.

matlabdl Matlab for Deep Learning 14 hours

In this instructor-led, live training, participants will learn how to use Matlab to design, build, and visualize a convolutional neural network for image recognition.

By the end of this training, participants will be able to:

  • Build a deep learning model
  • Automate data labeling
  • Work with models from Caffe and TensorFlow-Keras
  • Train data using multiple GPUs, the cloud, or clusters

Audience

  • Developers
  • Engineers
  • Domain experts

Format of the course

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