Online or onsite, instructor-led live Neural Network training courses demonstrate through interactive discussion and 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 as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Neural Networks trainings in Birmingham can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Birmingham
Maple House, 150 Corporation Street , Birmingham, united kingdom, B4 6TB
Directions
From Moor Street Station
Exiting via the main ticket hall, travel up the escalator to the Pallasades Shopping centre. At the top turn left and follow the shops first to the left and then to the right leading to the exit from the Pallasades and on to Corporation Street. Walking down the ramp past McDonalds continue up Corporation Street for 200m passing Gap Clothes, House of Fraser and We...
Directions
From Moor Street Station
Exiting via the main ticket hall, travel up the escalator to the Pallasades Shopping centre. At the top turn left and follow the shops first to the left and then to the right leading to the exit from the Pallasades and on to Corporation Street. Walking down the ramp past McDonalds continue up Corporation Street for 200m passing Gap Clothes, House of Fraser and Wetherspoons pub. Continue across Old Square and on to Corporation street again, the entrance to Maple House is adjacent to Tesco Express.
From Snow Hill Station
Exit the station and cross on to Colmore Row. Continue heading through the walkway under the tall glass building directly opposite the station. Carry on through the shops for around 300m and then bear left in to The Minories Shopping Centre. Upon exiting The Minories walk across the pedestrian crossing, past the Tony Hancock statue in Old Square, across the next crossing and directly across the road to your right is Maple House, adjacent to Tesco Express.
From Moor Street Station
Exit the station and turn right along Moor Street Parkway. Continue for around 300m and then turn left up the hill on to The Priory. At the top of the hill go over the crossing toward Tesco Express, follow the footpath round into Corporation Street and Maple House is directly on your right adjacent to Tesco Express.
By Road - From The M1 / M6
Exit at Junction 19. Take the M6 toward Coventry (North) / Birmingham. At junction 3a exit toward Birmingham/M42/M5, merge onto the M6 motorway. Exit the M6 at junction 6, exit toward A38 (M) / Birmingham (Cen). Merge onto A5127 / Aston Expressway. Take the exit toward Lancaster Circus Queensway, City Centre. At Lancaster Circus Queensway, take the 1st exit on to James Watt Queensway. Take the first right at Newton Street, turn left in to Dalton Street. Maple House located on Corporation Street adjacent to Tesco Express.
From The M5 / M6 Southbound
at Junction 1 of the M5. Exit the M6 at Junction 6, exit towards the A38 and following instructions as above.
Parking
The nearest NCP car parks are:
NCP - Birmingham Londonderry House on Dalton Street
This instructor-led, live training in Birmingham (online or onsite) is aimed at advanced-level professionals who wish to explore state-of-the-art XAI techniques for deep learning models, with a focus on building interpretable AI systems.By the end of this training, participants will be able to:
Understand the challenges of explainability in deep learning.
Implement advanced XAI techniques for neural networks.
Interpret decisions made by deep learning models.
Evaluate the trade-offs between performance and transparency.
This is a 4 day course introducing AI and it's application using the Python programming language. There is an option to have an additional day to undertake an AI project on completion of this course.
This instructor-led, live training in Birmingham (online or onsite) is aimed at developers and data scientists who wish to 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.
This course has been created for managers, solutions architects, innovation officers, CTOs, software architects and anyone who is interested in an overview of applied artificial intelligence and the nearest forecast for its development.
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.
Artificial 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.
This is a 4 day course introducing AI and it's application. There is an option to have an additional day to undertake an AI project on completion of this course.
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.
Artificial 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.
This instructor-led, live training in Birmingham (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while making the code easy to debug.By the end of this training, participants will be able to:
Set up the necessary development environment to start developing neural network models.
Define and implement neural network models using a comprehensible source code.
Execute examples and modify existing algorithms to optimize deep learning training models while leveraging GPUs for high performance.
This instructor-led, live training in Birmingham (online or onsite) 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.By the end of this training, participants will be able to:
Apply core statistical methods to pattern recognition.
Use key models like neural networks and kernel methods for data analysis.
Implement advanced techniques for complex problem-solving.
Improve prediction accuracy by combining different models.
This instructor-led, live training in (online or onsite) is aimed at data scientists who wish to use GANs and variational autoencoders to generate new, synthetic instances of images, videos, and audio.By the end of this training, participants will be able to:
Build a GAN using machine learning libraries in Python.
Type: Theoretical training with applications decided in advance with the students on Lasagne or Keras according to the educational group Teaching method: presentation, discussions and case studies Artificial intelligence, after having disrupted many scientific fields, has begun to revolutionize a large number of economic sectors (industry, medicine, communication, etc.). However, its presentation in the mainstream media is often a fantasy, very far from what the domains of Machine Learning or Deep Learning really are. The purpose of this training is to provide engineers who already have mastery of IT tools (including a basic software programming basis) with an introduction to Deep Learning as well as to its different areas of specialization and therefore to the main network architectures. existing today. If the mathematical basics are covered during the course, a BAC+2 level of mathematics is recommended for greater comfort. It is absolutely possible to ignore the mathematical axis and retain only a “system” vision, but this approach will enormously limit your understanding of the subject.
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
This instructor-led, live training in Birmingham (online or onsite) is aimed at engineers who wish to learn about the applicability of artificial intelligence to mechatronic systems.By the end of this training, participants will be able to:
Gain an overview of artificial intelligence, machine learning, and computational intelligence.
Understand the concepts of neural networks and different learning methods.
Choose artificial intelligence approaches effectively for real-life problems.
Implement AI applications in mechatronic engineering.
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 to choose the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow.
This classroom based training session will contain presentations and computer based examples and case study exercises to undertake with relevant neural and deep network libraries
PaddlePaddle (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
This instructor-led, live training in Birmingham (online or onsite) is aimed at data scientists who wish to use Python to build recommender systems.By the end of this training, participants will be able to:
Create recommender systems at scale.
Apply collaborative filtering to build recommender systems.
Use Apache Spark to compute recommender systems on clusters.
Build a framework to test recommendation algorithms with Python.
In this instructor-led, live training in Birmingham, 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.
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
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Testimonials (7)
Hunter is fabulous, very engaging, extremely knowledgeable and personable. Very well done.
Rick Johnson - Laramie County Community College
Course - Artificial Intelligence (AI) Overview
The trainer was a professional in the subject field and related theory with application excellently
Fahad Malalla - Tatweer Petroleum
Course - Applied AI from Scratch in Python
Very flexible.
Frank Ueltzhöffer
Course - Artificial Neural Networks, Machine Learning and Deep Thinking
I liked the new insights in deep machine learning.
Josip Arneric
Course - Neural Network in R
I really appreciated the crystal clear answers of Chris to our questions.
Léo Dubus
Course - Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple
Ann created a great environment to ask questions and learn. We had a lot of fun and also learned a lot at the same time.
Gudrun Bickelq
Course - Introduction to the use of neural networks
It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.
Jonathan Blease
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
Provisonal Upcoming Courses (Contact Us For More Information)
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