Deep Learning Training in Sheffield

Deep Learning Training in Sheffield

Local, instructor-led live Deep Learning (DL) 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 as "onsite live training" or "remote live training". Onsite live Deep Learning training can be carried out locally on customer premises in Sheffield or in NobleProg corporate training centres in Sheffield. Remote live training is carried out by way of an interactive, remote desktop. NobleProg -- Your Local Training Provider

Chimney House Sheffield
Learn Deep Learning in our training center in Sheffield. The Chimney House is a creative and innovative space in the centre of Sheffield – the biggest feedback we receive from companies that use us is that they noticeably observe higher levels of performance and interaction from teams and individuals. The building is formed on the ethos of the truest form of recycling – 85% of furnitures, fittings and décor has been created from reclaim and salvage and matched with a ultra modern outlook. We host meetings, trainings, brainstorming sessions,... Read more

Testimonials

★★★★★
★★★★★

Deep Learning (DL) Subcategories

Deep Learning Course Events - Sheffield

CodeNameVenueDurationCourse DateCourse Price [Remote / Classroom]
embeddingprojectorEmbedding Projector: Visualizing Your Training DataChimney House Sheffield14 hoursMon, 2019-01-07 09:30£2200 / £2800
mldtMachine Learning and Deep LearningChimney House Sheffield21 hoursMon, 2019-01-07 09:30£3900 / £4800
deeplearning1Introduction to Deep LearningChimney House Sheffield21 hoursMon, 2019-01-07 09:30£3900 / £4800
intrdplrngrsneuingIntroduction Deep Learning & Neural Networks for EngineersChimney House Sheffield21 hoursTue, 2019-01-08 09:30£3300 / £4200
appaiApplied AI from ScratchChimney House Sheffield28 hoursTue, 2019-01-08 09:30£4400 / £5600
dlfortelecomwithpythonDeep Learning for Telecom (with Python)Chimney House Sheffield28 hoursTue, 2019-01-08 09:30£4400 / £5600
tf101Deep Learning with TensorFlowChimney House Sheffield21 hoursWed, 2019-01-09 09:30£3900 / £4800
bspkannmldtArtificial Neural Networks, Machine Learning and Deep ThinkingChimney House Sheffield21 hoursWed, 2019-01-09 09:30£3900 / £4800
drlpythonDeep Reinforcement Learning with PythonChimney House Sheffield21 hoursWed, 2019-01-09 09:30£3900 / £4800
dl4jMastering Deeplearning4jChimney House Sheffield21 hoursMon, 2019-01-14 09:30£3900 / £4800
openfaceOpenFace: Creating Facial Recognition SystemsChimney House Sheffield14 hoursTue, 2019-01-15 09:30£2600 / £3200
paddlepaddlePaddlePaddleChimney House Sheffield21 hoursWed, 2019-01-16 09:30N/A / £4200
pythonadvmlPython for Advanced Machine LearningChimney House Sheffield21 hoursWed, 2019-01-16 09:30£3900 / £4800
nue_lbgNeural computing – Data scienceChimney House Sheffield14 hoursThu, 2019-01-17 09:30£2600 / £3200
dlforfinancewithpythonDeep Learning for Finance (with Python)Chimney House Sheffield28 hoursMon, 2019-01-21 09:30£5200 / £6400
torchTorch: Getting started with Machine and Deep LearningChimney House Sheffield21 hoursMon, 2019-01-21 09:30N/A / £4800
dlaitedmDeep Learning AI Techniques for Executives, Developers and ManagersChimney House Sheffield21 hoursMon, 2019-01-21 09:30£3900 / £4800
facebooknmtFacebook NMT: Setting up a Neural Machine Translation SystemChimney House Sheffield7 hoursFri, 2019-02-01 09:30£1100 / £1400
dl4jirDeepLearning4J for Image RecognitionChimney House Sheffield21 hoursTue, 2019-02-19 09:30£3900 / £4800
mlbankingpython_Machine Learning for Banking (with Python)Chimney House Sheffield21 hoursTue, 2019-02-19 09:30£3900 / £4800
dlvDeep Learning for VisionChimney House Sheffield21 hoursMon, 2019-03-25 09:30£3900 / £4800
opennnOpenNN: Implementing Neural NetworksChimney House Sheffield14 hoursWed, 2019-03-27 09:30£2600 / £3200
tensorflowservingTensorFlow ServingChimney House Sheffield7 hoursFri, 2019-05-31 09:30£1300 / £1600
dlfinancewithrDeep Learning for Finance (with R)Chimney House Sheffield28 hoursMon, 2019-06-03 09:30£5200 / £6400
mlentreMachine Learning Concepts for Entrepreneurs and ManagersChimney House Sheffield21 hoursMon, 2019-06-03 09:30£3900 / £4800
annmldtArtificial Neural Networks, Machine Learning, Deep ThinkingChimney House Sheffield21 hoursWed, 2019-06-05 09:30£3900 / £4800
tpuprogrammingTPU Programming: Building Neural Network Applications on Tensor Processing UnitsChimney House Sheffield7 hoursFri, 2019-06-14 09:30£1300 / £1600
t2tT2T: Creating Sequence to Sequence Models for Generalized LearningChimney House Sheffield7 hoursFri, 2019-06-14 09:30£1100 / £1400
caffeDeep Learning for Vision with CaffeChimney House Sheffield21 hoursMon, 2019-06-17 09:30£3900 / £4800
singaMastering Apache SINGAChimney House Sheffield21 hoursWed, 2019-06-19 09:30N/A / £4200

DL (Deep Learning) Course Outlines in Sheffield

CodeNameDurationOverview
annmldtArtificial Neural Networks, Machine Learning, Deep Thinking21 hoursArtificial Neural Network 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. Deep Learning is a subset of ML.
undnnUnderstanding Deep Neural Networks35 hoursThis course begins with giving you conceptual knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications).

Part-1(40%) of this training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.

Part-2(20%) of this training introduces Theano - a python library that makes writing deep learning models easy.

Part-3(40%) of the training would be extensively based on Tensorflow - 2nd Generation API of Google's open source software library for Deep Learning. The examples and handson would all be made in TensorFlow.

Audience

This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects

After completing this course, delegates will:

-

have a good understanding on deep neural networks(DNN), CNN and RNN

-

understand TensorFlow’s structure and deployment mechanisms

-

be able to carry out installation / production environment / architecture tasks and configuration

-

be able to assess code quality, perform debugging, monitoring

-

be able to implement advanced production like training models, building graphs and logging

Not all the topics would be covered in a public classroom with 35 hours duration due to the vastness of the subject.

The Duration of the complete course will be around 70 hours and not 35 hours.
t2tT2T: Creating Sequence to Sequence Models for Generalized Learning7 hoursTensor2Tensor (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
embeddingprojectorEmbedding Projector: Visualizing Your Training Data14 hoursEmbedding Projector is an open-source web application for visualizing the data used to train machine learning systems. Created by Google, it is part of TensorFlow.

This instructor-led, live training introduces the concepts behind Embedding Projector and walks participants through the setup of a demo project.

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

- Explore how data is being interpreted by machine learning models
- Navigate through 3D and 2D views of data to understand how a machine learning algorithm interprets it
- Understand the concepts behind Embeddings and their role in representing mathematical vectors for images, words and numerals.
- Explore the properties of a specific embedding to understand the behavior of a model
- Apply Embedding Project to real-world use cases such building a song recommendation system for music lovers

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
openfaceOpenFace: Creating Facial Recognition Systems14 hoursOpenFace 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
pythonadvmlPython for Advanced Machine Learning21 hoursIn 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
radvmlAdvanced Machine Learning with R21 hoursIn 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
tensorflowservingTensorFlow Serving7 hoursTensorFlow Serving is a system for serving machine learning (ML) models to production.

In this instructor-led, live training, participants will learn how to configure and use TensorFlow Serving to deploy and manage ML models in a production environment.

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

- Train, export and serve various TensorFlow models
- Test and deploy algorithms using a single architecture and set of APIs
- Extend TensorFlow Serving to serve other types of models beyond TensorFlow models

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
matlabdlMatlab for Deep Learning14 hoursIn 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
mlbankingpython_Machine Learning for Banking (with Python)21 hoursMachine Learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications.

In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry.

Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
dlfornlpDeep Learning for NLP (Natural Language Processing)28 hoursDeep Learning for NLP allows a machine to learn simple to complex language processing. Among the tasks currently possible are language translation and caption generation for photos. DL (Deep Learning) is a subset of ML (Machine Learning). Python is a popular programming language that contains libraries for Deep Learning for NLP.

In this instructor-led, live training, participants will learn to use Python libraries for NLP (Natural Language Processing) as they create an application that processes a set of pictures and generates captions.

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

- Design and code DL for NLP using Python libraries
- Create Python code that reads a substantially huge collection of pictures and generates keywords
- Create Python Code that generates captions from the detected keywords

Audience

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

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
PaddlePaddlePaddlePaddle21 hoursPaddlePaddle (PArallel Distributed Deep LEarning) is a scalable deep learning platform developed by Baidu.

In this instructor-led, live training, participants will learn how to use PaddlePaddle to enable deep learning in their product and service applications.

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

- Set up and configure PaddlePaddle
- Set up a Convolutional Neural Network (CNN) for image recognition and object detection
- Set up a Recurrent Neural Network (RNN) for sentiment analysis
- Set up deep learning on recommendation systems to help users find answers
- Predict click-through rates (CTR), classify large-scale image sets, perform optical character recognition(OCR), rank searches, detect computer viruses, and implement a recommendation system.

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
dlfinancewithrDeep Learning for Finance (with R)28 hoursMachine 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
dlforbankingwithpythonDeep Learning for Banking (with Python)28 hoursMachine 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
dlforbankingwithrDeep Learning for Banking (with R)28 hoursMachine 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
dlforfinancewithpythonDeep Learning for Finance (with Python)28 hoursMachine 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
drlpythonDeep Reinforcement Learning with Python21 hoursDeep Reinforcement Learning refers to the ability of an "artificial agent" 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
DLAITEDMDeep Learning AI Techniques for Executives, Developers and Managers21 hoursIntroduction:

Deep learning is becoming a principal component of future product design that wants to incorporate artificial intelligence at the heart of their models. Within the next 5 to 10 years, deep learning development tools, libraries, and languages will become standard components of every software development toolkit. So far Google, Sales Force, Facebook, Amazon have been successfully using deep learning AI to boost their business. Applications ranged from automatic machine translation, image analytics, video analytics, motion analytics, generating targeted advertisement and many more.

This coursework is aimed for those organizations who want to incorporate Deep Learning as very important part of their product or service strategy. Below is the outline of the deep learning course which we can customize for different levels of employees/stakeholders in an organization.

Target Audience:

( Depending on target audience, course materials will be customized)

Executives

A general overview of AI and how it fits into corporate strategy, with breakout sessions on strategic planning, technology roadmaps, and resource allocation to ensure maximum value.

Project Managers

How to plan out an AI project, including data gathering and evaluation, data cleanup and verification, development of a proof-of-concept model, integration into business processes, and delivery across the organization.

Developers

In-depth technical trainings, with focus on neural networks and deep learning, image and video analytics (CNNs), sound and text analytics (NLP), and bringing AI into existing applications.

Salespersons

A general overview of AI and how it can satisfy customer needs, value propositions for various products and services, and how to allay fears and promote the benefits of AI.
Nue_LBGNeural computing – Data science14 hoursThis classroom based training session will contain presentations and computer based examples and case study exercises to undertake with relevant neural and deep network libraries
dlformedicineDeep Learning for Medicine14 hoursMachine 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 attempts to mimic the workings of the human brain in making decisions. It is trained with data in order to automatically provide solutions to problems. Deep Learning provides vast opportunities for the medical industry which is sitting on a data goldmine.

In this instructor-led, live training, participants will take part in a series of discussions, exercises and case-study analysis to understand the fundamentals of Deep Learning. The most important Deep Learning tools and techniques will be evaluated and exercises will be carried out to prepare participants for carrying out their own evaluation and implementation of Deep Learning solutions within their organizations.

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

- Understand the fundamentals of Deep Learning
- Learn Deep Learning techniques and their applications in the industry
- Examine issues in medicine which can be solved by Deep Learning technologies
- Explore Deep Learning case studies in medicine
- Formulate a strategy for adopting the latest technologies in Deep Learning for solving problems in medicine

Audience

- Managers
- Medical professionals in leadership roles

Format of the course

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

Note

- To request a customized training for this course, please contact us to arrange.
dlfortelecomwithpythonDeep Learning for Telecom (with Python)28 hoursMachine 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 telecom 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 telecom
- Use Python, Keras, and TensorFlow to create deep learning models for telecom
- Build their own deep learning customer churn prediction model using Python

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
dsstneAmazon DSSTNE: Build a Recommendation System7 hoursAmazon 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
MicrosoftCognitiveToolkitMicrosoft Cognitive Toolkit 2.x21 hoursMicrosoft 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 as 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.
deeplearning1Introduction to Deep Learning21 hoursThis 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.
dl4jirDeepLearning4J for Image Recognition21 hoursDeeplearning4j 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.
bspkannmldtArtificial Neural Networks, Machine Learning and Deep Thinking21 hoursArtificial Neural Network 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. Deep Learning is a subset of ML.
dladvAdvanced Deep Learning28 hoursMachine 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.
tf101Deep Learning with TensorFlow21 hoursTensorFlow is a 2nd Generation API of Google's open source software library for Deep Learning. The system is designed to facilitate research in machine learning, and to make it quick and easy to transition from research prototype to production system.

Audience

This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects

After completing this course, delegates will:

- understand TensorFlow’s structure and deployment mechanisms
- be able to carry out installation / production environment / architecture tasks and configuration
- be able to assess code quality, perform debugging, monitoring
- be able to implement advanced production like training models, building graphs and logging
tfirTensorFlow for Image Recognition28 hoursThis course explores, with specific examples, the application of Tensor Flow to the purposes of image recognition

Audience

This course is intended for engineers seeking to utilize TensorFlow for the purposes of Image Recognition

After completing this course, delegates will be able to:

- understand TensorFlow’s structure and deployment mechanisms
- carry out installation / production environment / architecture tasks and configuration
- assess code quality, perform debugging, monitoring
- implement advanced production like training models, building graphs and logging
tsflw2vNatural Language Processing with TensorFlow35 hoursTensorFlow™ is an open source software library for numerical computation using data flow graphs.

SyntaxNet is a neural-network Natural Language Processing framework for TensorFlow.

Word2Vec is used for learning vector representations of words, called "word embeddings". Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (Chapter 3.1 and 3.2 in Mikolov et al.).

Used in tandem, SyntaxNet and Word2Vec allows users to generate Learned Embedding models from Natural Language input.

Audience

This course is targeted at Developers and engineers who intend to work with SyntaxNet and Word2Vec models in their TensorFlow graphs.

After completing this course, delegates will:

- understand TensorFlow’s structure and deployment mechanisms
- be able to carry out installation / production environment / architecture tasks and configuration
- be able to assess code quality, perform debugging, monitoring
- be able to implement advanced production like training models, embedding terms, building graphs and logging
Deep Learning (DL) training courses in Sheffield, Weekend Deep Learning courses in Sheffield, Evening DL (Deep Learning) training in Sheffield, Deep Learning instructor-led in Sheffield, Deep Learning (DL) instructor in Sheffield, Weekend Deep Learning training in Sheffield, Deep Learning classes in Sheffield, Evening Deep Learning (DL) courses in Sheffield, DL (Deep Learning) trainer in Sheffield, Deep Learning one on one training in Sheffield, DL (Deep Learning) private courses in Sheffield, DL (Deep Learning) coaching in Sheffield, DL (Deep Learning) instructor-led in Sheffield, Deep Learning on-site in Sheffield, Deep Learning (DL) boot camp in Sheffield

Course Discounts

CourseVenueCourse DateCourse Price [Remote / Classroom]
RPA: Implementing Robotic Process AutomationLondon, Hatton GardenTue, 2019-01-29 09:30£3861 / £4986
JIRA for AdministratorsLondon, Hatton GardenTue, 2019-01-29 09:30£1100 / £1475
AngularJS BasicsEdinburgh Training and Conference VenueWed, 2019-01-30 09:30£2200 / £2600
QMS Auditor / Lead Auditor (ISO 9001)CardiffWed, 2019-03-13 09:30£3300 / £4200
Natural Language Processing - AI/RoboticsLondon, Hatton GardenMon, 2019-04-01 09:30£3900 / £5025
Understanding Modern Information Communication TechnologyCardiffMon, 2019-05-06 09:30£1100 / £1400

Course Discounts Newsletter

We respect the privacy of your email address. We will not pass on or sell your address to others.
You can always change your preferences or unsubscribe completely.

Some of our clients

is growing fast!

We are looking to expand our presence in the UK!

As a Business Development Manager you will:

  • expand business in the UK
  • recruit local talent (sales, agents, trainers, consultants)
  • recruit local trainers and consultants

We offer:

  • Artificial Intelligence and Big Data systems to support your local operation
  • high-tech automation
  • continuously upgraded course catalogue and content
  • good fun in international team

If you are interested in running a high-tech, high-quality training and consulting business.

Apply now!