Τα τοπικά μαθήματα κατάρτισης σε Neural Network, καθοδηγούμενα από εκπαιδευτικούς, επιδεικνύουν μέσω της διαδραστικής συζήτησης και της πρακτικής άσκησης πώς να κατασκευάσουν τα Νευρικά Δίκτυα με τη χρήση πολλών εργαλείων και βιβλιοθηκών κυρίως με ανοιχτού κώδικα, καθώς και με τον τρόπο χρήσης της δύναμης του προηγμένου υλικού (GPU) τεχνικές βελτιστοποίησης που περιλαμβάνουν κατανεμημένους υπολογιστές και μεγάλα δεδομένα. Τα μαθήματα του Νευρικού Δικτύου βασίζονται σε δημοφιλείς γλώσσες προγραμματισμού όπως Python, Java, γλώσσα R και ισχυρές βιβλιοθήκες, όπως TensorFlow, Torch, Caffe, Theano και πολλά άλλα. Τα μαθήματα του Νευρικού Δικτύου καλύπτουν τόσο τη θεωρία όσο και την εφαρμογή με τη χρήση πολλών υλοποιήσεων νευρωνικού δικτύου, όπως Νευρωνικά Δίκτυα (DNN), Συνεργατικά Νευρωνικά Δίκτυα (CNN) και Επαναλαμβανόμενα Νευρωνικά Δίκτυα (RNN). Η εκπαίδευση του Νευρικού Δικτύου είναι διαθέσιμη ως "onsite live training" ή "remote live training". Η επιτόπια κατάρτιση σε πραγματικό χρόνο μπορεί να πραγματοποιηθεί σε τοπικό επίπεδο στις εγκαταστάσεις του πελάτη Ελλάδα ή σε εταιρικά κέντρα κατάρτισης NobleProg στο Ελλάδα . Η απομακρυσμένη ζωντανή προπόνηση πραγματοποιείται μέσω μιας διαδραστικής, απομακρυσμένης επιφάνειας εργασίας. NobleProg - Ο τοπικός παροχέας εκπαίδευσης
Testimonials
ref material to use later was very good.
PAUL BEALES- Seagate Technology.
Course: Applied Machine Learning
What did you like the most about the training?: Gave me good practice with using R to build machine learning systems for real situations. I can use this in my work straight away. This was an excellent course. One of the best I have had.
Matthew Thomas - British Telecom
Course: Applied Machine Learning
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
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
the interactive part, tailored to our specific needs
Thomas Stocker
Course: Introduction to the use of neural networks
I really appreciated the crystal clear answers of Chris to our questions.
Léo Dubus
Course: Neural Networks Fundamentals using TensorFlow as Example
Knowledgeable trainer
Sridhar Voorakkara
Course: Neural Networks Fundamentals using TensorFlow as Example
I was amazed at the standard of this class - I would say that it was university standard.
David Relihan
Course: Neural Networks Fundamentals using TensorFlow as Example
Very good all round overview.Good background into why Tensorflow operates as it does.
Kieran Conboy
Course: Neural Networks Fundamentals using TensorFlow as Example
I liked the opportunities to ask questions and get more in depth explanations of the theory.
Sharon Ruane
Course: Neural Networks Fundamentals using TensorFlow as Example
The trainer very easily explained difficult and advanced topics.
Leszek K
Course: Artificial Intelligence Overview
new insights in deep machine learning
Josip Arneric
Course: Neural Network in R
We gained some knowledge about NN in general, and what was the most interesting for me were the new types of NN that are popular nowadays.
Tea Poklepovic
Course: Neural Network in R
Graphs in R :)))
Faculty of Economics and Business Zagreb
Course: Neural Network in R
Very flexible
Frank Ueltzhöffer
Course: Artificial Neural Networks, Machine Learning and Deep Thinking
flexibility
Werner Philipp
Course: Artificial Neural Networks, Machine Learning and Deep Thinking
Given outlook of the technology: what technology/process might become more important in the future; see, what the technology can be used for
Commerzbank AG
Course: Neural Networks Fundamentals using TensorFlow as Example
Topic selection. Style of training. Practice orientation
Commerzbank AG
Course: Neural Networks Fundamentals using TensorFlow as Example
Communication with lecturers
文欣 张
Course: Artificial Neural Networks, Machine Learning, Deep Thinking
like it all
lisa xie
Course: Artificial Neural Networks, Machine Learning, Deep Thinking
a lot of exercises that I can directly use in my work.
Alior Bank S.A.
Course: Neural Network in R
Examples on real data.
Alior Bank S.A.
Course: Neural Network in R
neuralnet, pROC in a loop.
Alior Bank S.A.
Course: Neural Network in R
Ένα ευρύ φάσμα θεμάτων που καλύπτονται και ουσιαστική γνώση των ηγετών.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
Translated by
έλλειψη
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
Translated by
Μεγάλη θεωρητική και πρακτική γνώση των διδασκόντων. Η επικοινωνιακή ικανότητα των εκπαιδευτών. Κατά τη διάρκεια του μαθήματος, μπορείτε να κάνετε ερωτήσεις και να λάβετε ικανοποιητικές απαντήσεις.
Kamil Kurek - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
Translated by
Πρακτικό μέρος, όπου υλοποιήσαμε αλγόριθμους. Αυτό επέτρεψε την καλύτερη κατανόηση του θέματος.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
Translated by
ασκήσεις και παραδείγματα που εφαρμόζονται σε αυτά
Paweł Orzechowski - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
Translated by
Παραδείγματα και θέματα που συζητήθηκαν.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
Translated by
Ουσιαστική γνώση, δέσμευση, παθιασμένος τρόπος μεταφοράς γνώσης. Πρακτικά παραδείγματα μετά από μια θεωρητική διάλεξη.
Janusz Chrobot - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
Translated by
Πρακτικές ασκήσεις που εκπόνησε ο κ. Maciej
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
Translated by
Neural Networks Course Outlines
| Code | Name | Duration | Overview |
|---|---|---|---|
| aiint | Artificial Intelligence Overview | 7 hours | 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. |
| 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 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. |
| appai | Applied AI from Scratch | 28 hours | 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. |
| Nue_LBG | Neural computing – Data science | 14 hours | 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 |
| drlpython | Deep Reinforcement Learning with Python | 21 hours | Deep 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 |
| 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. |
| 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 |
| encogintro | Encog: Introduction to Machine Learning | 14 hours | Encog is an open-source machine learning framework for Java and .Net. In this instructor-led, live training, participants will learn how to create various neural network components using ENCOG. Real-world case studies will be discussed and machine language based solutions to these problems will be explored. By the end of this training, participants will be able to: - Prepare data for neural networks using the normalization process - Implement feed forward networks and propagation training methodologies - Implement classification and regression tasks - Model and train neural networks using Encog's GUI based workbench - Integrate neural network support into real-world applications Audience - Developers - Analysts - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| encogadv | Encog: Advanced Machine Learning | 14 hours | Encog is an open-source machine learning framework for Java and .Net. In this instructor-led, live training, participants will learn advanced machine learning techniques for building accurate neural network predictive models. By the end of this training, participants will be able to: - Implement different neural networks optimization techniques to resolve underfitting and overfitting - Understand and choose from a number of neural network architectures - Implement supervised feed forward and feedback networks Audience - Developers - Analysts - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| snorkel | Snorkel: Rapidly Process Training Data | 7 hours | Snorkel is a system for rapidly creating, modeling, and managing training data. It focuses on accelerating the development of structured or "dark" data extraction applications for domains in which large labeled training sets are not available or easy to obtain. In this instructor-led, live training, participants will learn techniques for extracting value from unstructured data such as text, tables, figures, and images through modeling of training data with Snorkel. By the end of this training, participants will be able to: - Programmatically create training sets to enable the labeling of massive training sets - Train high-quality end models by first modeling noisy training sets - Use Snorkel to implement weak supervision techniques and apply data programming to weakly-supervised machine learning systems Audience - Developers - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| PaddlePaddle | PaddlePaddle | 21 hours | 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 |
| 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 |
| neuralnet | Introduction to the use of neural networks | 7 hours | The training is aimed at people who want to learn the basics of neural networks and their applications. |
| intrdplrngrsneuing | Introduction Deep Learning & Neural Networks for Engineers | 21 hours | 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. |
| OpenNN | OpenNN: Implementing Neural Networks | 14 hours | Το OpenNN είναι μια βιβλιοθήκη κατηγορίας ανοιχτού κώδικα γραμμένη σε C ++ που υλοποιεί νευρωνικά δίκτυα, για χρήση στη μηχανική μάθηση. Σε αυτό το μάθημα θα ξεπεράσουμε τις αρχές των νευρωνικών δικτύων και θα χρησιμοποιήσουμε το OpenNN για την υλοποίηση μιας δειγματοληπτικής εφαρμογής. Κοινό Προγραμματιστές λογισμικού και προγραμματιστές που επιθυμούν να δημιουργήσουν εφαρμογές Deep Learning. Μορφή του μαθήματος Διάλεξη και συζήτηση σε συνδυασμό με πρακτικές ασκήσεις. |
| 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 |
| 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 to choose the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow. |
| aiauto | Artificial Intelligence in Automotive | 14 hours | This course covers AI (emphasizing Machine Learning and Deep Learning) in Automotive Industry. It helps to determine which technology can be (potentially) used in multiple situation in a car: from simple automation, image recognition to autonomous decision making. |
| aiintrozero | From Zero to AI | 35 hours | This course is created for people who have no previous experience in probability and statistics. |
| bspkannmldt | Artificial Neural Networks, Machine Learning and Deep Thinking | 21 hours | 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. |
| annmldt | Artificial Neural Networks, Machine Learning, Deep Thinking | 21 hours | 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. |
| appliedml | Applied Machine Learning | 14 hours | This training course is for people that would like to apply Machine Learning in practical applications. Audience This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work. Sector specific examples are used to make the training relevant to the audience. |
| rneuralnet | Neural Network in R | 14 hours | This course is an introduction to applying neural networks in real world problems using R-project software. |
| appaipy | Applied AI from Scratch in Python | 28 hours | 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. |
Upcoming Neural Networks Courses
| Course | Course Date | Course Price [Remote / Classroom] |
|---|---|---|
| Artificial Intelligence in Automotive - Θεσσαλονίκη (Thessaloniki) | Mon, 2018-12-31 09:30 | 3500EUR / 4100EUR |
| Artificial Intelligence in Automotive - Nicosia, Jacovides Tower | Tue, 2019-01-29 09:30 | 3500EUR / 4100EUR |
| Artificial Intelligence in Automotive - Πάτρα (Patras) | Thu, 2019-02-14 09:30 | 3500EUR / 4100EUR |
| Artificial Intelligence in Automotive - Θεσσαλονίκη (Thessaloniki) | Tue, 2019-02-19 09:30 | 3500EUR / 4100EUR |
| Artificial Intelligence in Automotive - Αθήνα (Athens), Acropolis | Thu, 2019-02-21 09:30 | 3500EUR / 4100EUR |
Other regions
Other countries
Consulting
Course Discounts
| Course | Venue | Course Date | Course Price [Remote / Classroom] |
|---|---|---|---|
| Drupal and Solr | Αθήνα (Athens), Acropolis | Tue, 2018-12-18 09:30 | 2700EUR / 3300EUR |
| Cognitive Computing: An Introduction for Business Managers | Πάτρα (Patras) | Thu, 2019-02-07 09:30 | 1575EUR / 1975EUR |
| NVIDIA GPU Programming - Extended | Αθήνα (Athens), Acropolis | Mon, 2019-02-11 09:30 | N/A / 5525EUR |
| Deep Learning for Finance (with R) | Αθήνα (Athens), Acropolis | Mon, 2019-02-18 09:30 | 6300EUR / 7300EUR |
| Neural computing – Data science | Αθήνα (Athens), Acropolis | Mon, 2019-06-03 09:30 | 3150EUR / 3750EUR |
Some of our clients
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is growing fast!
We are looking to expand our presence in Greece!
As a Business Development Manager you will:
- expand business in Greece
- 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!