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. Seoul onsite live Neural Networks trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Seoul - Gran Seoul
Jongno-gu, Tower 1, Gran Seoul, 33 Jong-ro, Spaces Gran Seoul, 7F, Seoul, korea, 01359
Gran Seoul is located on the 7th floor of 24-storey Gran Seoul Tower. The location is just a stone's throw away from Gwanghwa...
Gran Seoul is located on the 7th floor of 24-storey Gran Seoul Tower. The location is just a stone's throw away from Gwanghwamun, the largest gate of Gyeongbukgung Palace which is one of the most popular tourist attractions and national landmark in the heart of Seoul. Spaces Gran Seoul can be conveniently accessed by public transportation with Kwanghwamoon subway station located just 500 meters away and only a few minutes’ walk to Jonggak subway station. Also located nearby and within 15 minutes ride is Seoul Station where five lines of bullet train, subway, and airport train are started.
The Gran Seoul Tower is a grade-A commercial building combining office space, world class hotel, department store, major companies, finance enterprises, government offices and banks. Surrounded by historical and cultural facilities symbolizing Seoul’s pride, Gran Seoul is an ideal workspace for all businesses from start-ups to large international companies looking for established business location in the heart of Seoul's CBD.
Seoul Center 1
Jung-gu, 26, Eulji-ro 5-gil, 27/F, . West Center, Center1 Building, Seoul, korea, 04539
Center 1 is conveniently located at the core of Seoul's central business district (CBD). Center 1 is one of the most advanced...
Center 1 is conveniently located at the core of Seoul's central business district (CBD). Center 1 is one of the most advanced green building in Korea. Offers smartly landscaped park for rest and for a showcase of media technology as a front yard. It also features top class amenities such as prestigious retail shops and wide range of sophisticated restaurants (Italian, Japanese, Thai, Chinese, Korean, Indian, and Mexican) and breezy cafes. Business, dining, and shopping are under one roof at Center 1. Many financial institutions, investment banks, and insurance companies are located in close proximity, including the headquarter for the Bank of Korea. Seoul's political, public administration, media, cultural and economic venues are all within walking distance of Center 1. Also hotels, department stores and other amenities are within walking distance.
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 Seoul (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 Seoul (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 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.
Artificial intelligence has revolutionized a large number of economic sectors (industry, medicine, communication, etc.) after having upset many scientific fields. Nevertheless, his presentation in the major media is often a fantasy, far removed from what really are the fields of Machine Learning or Deep Learning. The aim of this course is to provide engineers who already have a master's degree in computer tools (including a software programming base) an introduction to Deep Learning as well as to its various fields of specialization and therefore to the main existing network architectures today. If the mathematical bases are recalled during the course, a level of mathematics of type BAC + 2 is recommended for more comfort. It is absolutely possible to ignore the mathematical axis in order to maintain only a "system" vision, but this approach will greatly 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 Seoul (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 Seoul (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 Seoul, 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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