Deep Learning Training in Wales

Deep Learning Training in Wales

DL (Deep Learning) is a subset of ML (Machine Learning)

NobleProg onsite live Deep Learning training courses demonstrate through hands-on practice the fundamentals and applications of Deep Learning and cover subjects such as deep machine learning, deep structured learning, and hierarchical learning.

Deep Learning training is available in various formats, including onsite live training and live instructor-led training using an interactive, remote desktop setup. Local Deep Learning 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

Deep Learning Course Events - Wales

Code Name Venue Duration Course Date PHP Course Price [Remote / Classroom]
Fairseq Fairseq: Setting up a CNN-based machine translation system Swansea- Princess House 7 hours Thu, 2018-03-08 09:30 £1100 / £1250
tensorflowserving TensorFlow Serving Swansea- Princess House 7 hours Mon, 2018-03-12 09:30 £1100 / £1250
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example Swansea- Princess House 28 hours Mon, 2018-03-12 09:30 £5200 / £5800
dlforfinancewithpython Deep Learning for Finance (with Python) Swansea- Princess House 28 hours Tue, 2018-03-13 09:30 £4400 / £5000
dlfinancewithr Deep Learning for Finance (with R) 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
dlfornlp Deep Learning for NLP (Natural Language Processing) Swansea- Princess House 28 hours Tue, 2018-03-13 09:30 £4400 / £5000
caffe Deep Learning for Vision with Caffe Cardiff 21 hours Wed, 2018-03-14 09:30 £3300 / £4200
embeddingprojector Embedding Projector: Visualizing your Training Data Cardiff 14 hours Thu, 2018-03-15 09:30 £2200 / £2800
openface OpenFace: Creating Facial Recognition Systems Cardiff 14 hours Mon, 2018-03-19 09:30 £2200 / £2800
dlfornlp Deep Learning for NLP (Natural Language Processing) Cardiff 28 hours Mon, 2018-03-19 09:30 £4400 / £5600
dlforbankingwithr Deep Learning for Banking (with R) Swansea- Princess House 28 hours Mon, 2018-03-19 09:30 £4400 / £5000
pythonadvml Python for Advanced Machine Learning Cardiff 21 hours Tue, 2018-03-20 09:30 £3300 / £4200
pythonadvml Python for Advanced Machine Learning Swansea- Princess House 21 hours Tue, 2018-03-20 09:30 £3300 / £3750
w2vdl4j NLP with Deeplearning4j Cardiff 14 hours Tue, 2018-03-20 09:30 £2600 / £3200
mldt Machine Learning and Deep Learning Swansea- Princess House 21 hours Wed, 2018-03-21 09:30 £3900 / £4350
mlbankingpython_ Machine Learning for Banking (with Python) Cardiff 21 hours Wed, 2018-03-21 09:30 £3300 / £4200
matlabdl Matlab for Deep Learning Cardiff 14 hours Thu, 2018-03-22 09:30 £2200 / £2800
tpuprogramming TPU Programming: Building Neural Network Applications on Tensor Processing Units Cardiff 7 hours Fri, 2018-03-23 09:30 £1100 / £1400
undnn Understanding Deep Neural Networks Cardiff 35 hours Mon, 2018-03-26 09:30 £5500 / £7000
tfir TensorFlow for Image Recognition Swansea- Princess House 28 hours Mon, 2018-03-26 09:30 £4400 / £5000
dlv Deep Learning for Vision Swansea- Princess House 21 hours Mon, 2018-03-26 09:30 £3900 / £4350
mlbankingpython_ Machine Learning for Banking (with Python) Swansea- Princess House 21 hours Mon, 2018-03-26 09:30 £3300 / £3750
openface OpenFace: Creating Facial Recognition Systems Swansea- Princess House 14 hours Tue, 2018-03-27 09:30 £2200 / £2500
bspkannmldt Artificial Neural Networks, Machine Learning and Deep Thinking Cardiff 21 hours Tue, 2018-03-27 09:30 £3300 / £4200
dsstne Amazon DSSTNE: Build a recommendation system Swansea- Princess House 7 hours Thu, 2018-03-29 09:30 £1100 / £1250
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
tsflw2v Natural Language Processing with TensorFlow Cardiff 35 hours Mon, 2018-04-02 09:30 £6500 / £8000
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
w2vdl4j NLP with Deeplearning4j Swansea- Princess House 14 hours Tue, 2018-04-03 09:30 £2600 / £2900
dlforbankingwithpython Deep Learning for Banking (with Python) Cardiff 28 hours Tue, 2018-04-03 09:30 £4400 / £5600
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
dlforbankingwithr Deep Learning for Banking (with R) Cardiff 28 hours Tue, 2018-04-03 09:30 £4400 / £5600
deeplearning1 Introduction to Deep Learning Cardiff 21 hours Mon, 2018-04-09 09:30 £3900 / £4800
t2t T2T: Creating Sequence to Sequence models for generalized learning Swansea- Princess House 7 hours Mon, 2018-04-09 09:30 £1100 / £1250
radvml Advanced Machine Learning with R Cardiff 21 hours Wed, 2018-04-11 09:30 £3900 / £4800
Torch Torch: Getting started with Machine and Deep Learning Swansea- Princess House 21 hours Mon, 2018-04-16 09:30 N/A / £4350
radvml Advanced Machine Learning with R Swansea- Princess House 21 hours Mon, 2018-04-16 09:30 £3900 / £4350
tensorflowserving TensorFlow Serving Cardiff 7 hours Mon, 2018-04-16 09:30 £1100 / £1400
Fairseq Fairseq: Setting up a CNN-based machine translation system Cardiff 7 hours Mon, 2018-04-16 09:30 £1100 / £1400
tpuprogramming TPU Programming: Building Neural Network Applications on Tensor Processing Units Swansea- Princess House 7 hours Mon, 2018-04-16 09:30 £1100 / £1250
dl4j Mastering Deeplearning4j Swansea- Princess House 21 hours Tue, 2018-04-17 09:30 £3300 / £3750
dlforfinancewithpython Deep Learning for Finance (with Python) Cardiff 28 hours Tue, 2018-04-17 09:30 £4400 / £5600
dl4j Mastering Deeplearning4j Cardiff 21 hours Wed, 2018-04-18 09:30 £3300 / £4200
facebooknmt Facebook NMT: Setting up a Neural Machine Translation System Cardiff 7 hours Fri, 2018-04-20 09:30 £1100 / £1400
dladv Advanced Deep Learning Swansea- Princess House 28 hours Mon, 2018-04-23 09:30 £5200 / £5800
deeplearning1 Introduction to Deep Learning Swansea- Princess House 21 hours Mon, 2018-04-23 09:30 £3900 / £4350
dl4jir DeepLearning4J for Image Recognition Cardiff 21 hours Mon, 2018-04-23 09:30 £3300 / £4200
tfir TensorFlow for Image Recognition Cardiff 28 hours Mon, 2018-04-23 09:30 £4400 / £5600
dlv Deep Learning for Vision Cardiff 21 hours Mon, 2018-04-23 09:30 £3900 / £4800
Torch Torch: Getting started with Machine and Deep Learning Cardiff 21 hours Tue, 2018-04-24 09:30 N/A / £4800
mldt Machine Learning and Deep Learning Cardiff 21 hours Tue, 2018-04-24 09:30 £3900 / £4800
caffe Deep Learning for Vision with Caffe Swansea- Princess House 21 hours Tue, 2018-04-24 09:30 £3300 / £3750
bspkannmldt Artificial Neural Networks, Machine Learning and Deep Thinking Swansea- Princess House 21 hours Tue, 2018-04-24 09:30 £3300 / £3750
t2t T2T: Creating Sequence to Sequence models for generalized learning Cardiff 7 hours Thu, 2018-04-26 09:30 £1100 / £1400
Fairseq Fairseq: Setting up a CNN-based machine translation system Swansea- Princess House 7 hours Fri, 2018-04-27 09:30 £1100 / £1250
dladv Advanced Deep Learning Cardiff 28 hours Mon, 2018-04-30 09:30 £5200 / £6400
dsstne Amazon DSSTNE: Build a recommendation system Cardiff 7 hours Mon, 2018-04-30 09:30 £1100 / £1400
tsflw2v Natural Language Processing with TensorFlow Swansea- Princess House 35 hours Mon, 2018-04-30 09:30 £6500 / £7250
dlforbankingwithpython Deep Learning for Banking (with Python) Swansea- Princess House 28 hours Mon, 2018-04-30 09:30 £4400 / £5000
tf101 Deep Learning with TensorFlow Swansea- Princess House 21 hours Mon, 2018-04-30 09:30 £3300 / £3750
dl4jir DeepLearning4J for Image Recognition Swansea- Princess House 21 hours Mon, 2018-04-30 09:30 £3300 / £3750
datamodeling Pattern Recognition Swansea- Princess House 35 hours Mon, 2018-04-30 09:30 £6500 / £7250
matlabdl Matlab for Deep Learning Swansea- Princess House 14 hours Tue, 2018-05-01 09:30 £2200 / £2500
tf101 Deep Learning with TensorFlow Cardiff 21 hours Wed, 2018-05-02 09:30 £3300 / £4200
OpenNN OpenNN: Implementing neural networks Cardiff 14 hours Wed, 2018-05-02 09:30 £2600 / £3200
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 £1100 / £1250
w2vdl4j NLP with Deeplearning4j Cardiff 14 hours Wed, 2018-05-09 09:30 £2600 / £3200
caffe Deep Learning for Vision with Caffe Cardiff 21 hours Wed, 2018-05-09 09:30 £3300 / £4200
openface OpenFace: Creating Facial Recognition Systems Cardiff 14 hours Thu, 2018-05-10 09:30 £2200 / £2800
embeddingprojector Embedding Projector: Visualizing your Training Data Cardiff 14 hours Thu, 2018-05-10 09:30 £2200 / £2800
embeddingprojector Embedding Projector: Visualizing your Training Data Swansea- Princess House 14 hours Thu, 2018-05-10 09:30 £2200 / £2500
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
pythonadvml Python for Advanced Machine Learning Cardiff 21 hours Mon, 2018-05-14 09:30 £3300 / £4200
pythonadvml Python for Advanced Machine Learning Swansea- Princess House 21 hours Mon, 2018-05-14 09:30 £3300 / £3750
dlfinancewithr Deep Learning for Finance (with R) Swansea- Princess House 28 hours Tue, 2018-05-15 09:30 £4400 / £5000
dlfornlp Deep Learning for NLP (Natural Language Processing) Swansea- Princess House 28 hours Tue, 2018-05-15 09:30 £4400 / £5000
openface OpenFace: Creating Facial Recognition Systems Swansea- Princess House 14 hours Wed, 2018-05-16 09:30 £2200 / £2500
dlv Deep Learning for Vision Swansea- Princess House 21 hours Wed, 2018-05-16 09:30 £3900 / £4350
dsstne Amazon DSSTNE: Build a recommendation system Swansea- Princess House 7 hours Thu, 2018-05-17 09:30 £1100 / £1250
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
dlfornlp Deep Learning for NLP (Natural Language Processing) Cardiff 28 hours Mon, 2018-05-21 09:30 £4400 / £5600
OpenNN OpenNN: Implementing neural networks Swansea- Princess House 14 hours Mon, 2018-05-21 09:30 £2600 / £2900
dlforbankingwithr Deep Learning for Banking (with R) Swansea- Princess House 28 hours Mon, 2018-05-21 09:30 £4400 / £5000
dlfinancewithr Deep Learning for Finance (with R) Cardiff 28 hours Tue, 2018-05-22 09:30 £4400 / £5600
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
tfir TensorFlow for Image Recognition Swansea- Princess House 28 hours Tue, 2018-05-22 09:30 £4400 / £5000
MicrosoftCognitiveToolkit Microsoft Cognitive Toolkit 2.x Cardiff 21 hours Wed, 2018-05-23 09:30 N/A / £4200
w2vdl4j NLP with Deeplearning4j Swansea- Princess House 14 hours Thu, 2018-05-24 09:30 £2600 / £2900
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
deeplearning1 Introduction to Deep Learning Cardiff 21 hours Wed, 2018-05-30 09:30 £3900 / £4800
t2t T2T: Creating Sequence to Sequence models for generalized learning Swansea- Princess House 7 hours Thu, 2018-05-31 09:30 £1100 / £1250

Course Outlines

Code Name Duration Outline
mldt Machine Learning and Deep Learning 21 hours

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

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

 

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.

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.
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

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
bspkannmldt Artificial Neural Networks, Machine Learning and Deep Thinking 21 hours
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.

dlfinancewithr Deep Learning for Finance (with R) 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. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.

In this instructor-led, live training, participants will learn how to implement deep learning models for finance using R 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 R to create deep learning models for finance
  • Build their own deep learning stock price prediction model using R

Audience

  • Developers
  • Data scientists

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice
dladv Advanced Deep Learning 28 hours
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.
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
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
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.
dlforbankingwithr Deep Learning for Banking (with R) 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. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.

In this instructor-led, live training, participants will learn how to implement deep learning models for banking using R 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 R to create deep learning models for banking
  • Build their own deep learning credit risk model using R

Audience

  • Developers
  • Data scientists

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice
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
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
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
dl4j Mastering Deeplearning4j 21 hours

Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments on distributed GPUs and CPUs.

 

Audience

This course is directed at engineers and developers seeking to utilize Deeplearning4j in their projects.

 

After this course delegates will be able to:

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.
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
singa Mastering Apache SINGA 21 hours

SINGA is a general distributed deep learning platform for training big deep learning models over large datasets. It is designed with an intuitive programming model based on the layer abstraction. A variety of popular deep learning models are supported, namely feed-forward models including convolutional neural networks (CNN), energy models like restricted Boltzmann machine (RBM), and recurrent neural networks (RNN). Many built-in layers are provided for users. SINGA architecture is sufficiently flexible to run synchronous, asynchronous and hybrid training frameworks. SINGA also supports different neural net partitioning schemes to parallelize the training of large models, namely partitioning on batch dimension, feature dimension or hybrid partitioning.

Audience

This course is directed at researchers, engineers and developers seeking to utilize Apache SINGA as a deep learning framework.

After completing this course, delegates will:

  • understand SINGA’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

 

dsstne Amazon DSSTNE: Build a recommendation system 7 hours

Amazon DSSTNE is an open-source library for training and deploying recommendation models. It allows models with weight matrices that are too large for a single GPU to be trained on a single host.

In this instructor-led, live training, participants will learn how to use DSSTNE to build a recommendation application.

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

  • Train a recommendation model with sparse datasets as input
  • Scale training and prediction models over multiple GPUs
  • Spread out computation and storage in a model-parallel fashion
  • Generate Amazon-like personalized product recommendations
  • Deploy a production-ready application that can scale at heavy workloads

Audience

  • Developers
  • Data scientists

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice
caffe Deep Learning for Vision with Caffe 21 hours

Caffe is a deep learning framework made with expression, speed, and modularity in mind.

This course explores the application of Caffe as a Deep learning framework for image recognition using MNIST as an example

Audience

This course is suitable for Deep Learning researchers and engineers interested in utilizing Caffe as a framework.

After completing this course, delegates will be able to:

  • understand Caffe’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, implementing layers and logging
t2t T2T: Creating Sequence to Sequence models for generalized learning 7 hours

Tensor2Tensor (T2T) is a modular, extensible library for training AI models in different tasks, using different types of training data, for example: image recognition, translation, parsing, image captioning, and speech recognition. It is maintained by the Google Brain team.

In this instructor-led, live training, participants will learn how to prepare a deep-learning model to resolve multiple tasks.

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

  • Install tensor2tensor, select a data set, and train and evaluate an AI model
  • Customize a development environment using the tools and components included in Tensor2Tensor
  • Create and use a single model to concurrently learn a number of tasks from multiple domains
  • Use the model to learn from tasks with a large amount of training data and apply that knowledge to tasks where data is limited
  • Obtain satisfactory processing results using a single GPU

Audience

  • Developers
  • Data scientists

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice
dl4jir DeepLearning4J for Image Recognition 21 hours

Deeplearning4j is an Open-Source Deep-Learning Software for Java and Scala on Hadoop and Spark.

Audience

This course is meant for engineers and developers seeking to utilize DeepLearning4J in their image recognition projects.

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
w2vdl4j NLP with Deeplearning4j 14 hours

Deeplearning4j is an open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments on distributed GPUs and CPUs.

Word2Vec is a method of computing vector representations of words introduced by a team of researchers at Google led by Tomas Mikolov.

Audience

This course is directed at researchers, engineers and developers seeking to utilize Deeplearning4J to construct Word2Vec models.

openface OpenFace: Creating Facial Recognition Systems 14 hours

OpenFace is Python and Torch based open-source, real-time facial recognition software based on Google’s FaceNet research.

In this instructor-led, live training, participants will learn how to use OpenFace's components to create and deploy a sample facial recognition application.

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

  • Work with OpenFace's components, including dlib, OpenVC, Torch, and nn4 to implement face detection, alignment, and transformation.
  • Apply OpenFace to real-world applications such as surveillance, identity verification, virtual reality, gaming, and identifying repeat customers, etc.

Audience

  • Developers
  • Data scientists

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice
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
pythonadvml Python for Advanced Machine Learning 21 hours

In this instructor-led, live training, participants will learn the most relevant and cutting-edge machine learning techniques in Python as they build a series of demo applications involving image, music, text, and financial data.

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

  • Implement machine learning algorithms and techniques for solving complex problems
  • Apply deep learning and semi-supervised learning to applications involving image, music, text, and financial data
  • Push Python algorithms to their maximum potential
  • Use libraries and packages such as NumPy and Theano

Audience

  • Developers
  • Analysts
  • Data scientists

Format of the course

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

radvml Advanced Machine Learning with R 21 hours

In this instructor-led, live training, participants will learn advanced techniques for Machine Learning with R as they step through the creation of a real-world application.

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

  • Use techniques as hyper-parameter tuning and deep learning
  • Understand and implement unsupervised learning techniques
  • Put a model into production for use in a larger application

Audience

  • Developers
  • Analysts
  • Data scientists

Format of the course

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

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
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