Τα τοπικά μαθήματα κατάρτισης Artificial Intelligence (AI), καθοδηγούμενα από εκπαιδευτές, καταδεικνύουν μέσω πρακτικών πρακτικών πώς να εφαρμόσουν λύσεις AI για την επίλυση πραγματικών προβλημάτων. Η εκπαίδευση AI 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.
Ήταν πολύ διαδραστικό και πιο χαλαρό και άτυπο από το αναμενόμενο. Καλύψαμε πολλά θέματα την εποχή εκείνη και ο εκπαιδευτής ήταν πάντα δεκτικός να μιλάει πιο λεπτομερώς ή γενικότερα για τα θέματα και τον τρόπο με τον οποίο συσχετίζονταν. Αισθάνομαι ότι η κατάρτιση μου έδωσε τα εργαλεία για να συνεχίσω να μαθαίνω, σε αντίθεση με το γεγονός ότι είναι μια μοναδική σύνοδος όπου η μάθηση σταματάει μόλις τελειώσετε κάτι που είναι πολύ σημαντικό δεδομένης της κλίμακας και της πολυπλοκότητας του θέματος.
Jonathan Blease
Course: Artificial Neural Networks, Machine Learning, Deep Thinking
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Η Ann δημιούργησε ένα εξαιρετικό περιβάλλον για να κάνει ερωτήσεις και να μάθει. Είχαμε πολύ διασκέδαση και μάθαμε πολλά ταυτόχρονα.
Gudrun Bickelq
Course: Introduction to the use of neural networks
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το διαδραστικό μέρος, προσαρμοσμένο στις συγκεκριμένες ανάγκες μας
Thomas Stocker
Course: Introduction to the use of neural networks
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Μου άρεσαν οι ασκήσεις
Office for National Statistics
Course: Natural Language Processing with Python
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Η πρακτική προσέγγιση
Kevin De Cuyper
Course: Computer Vision with OpenCV
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Ο εκπαιδευτής ήταν τόσο γνώστης και περιείχε τομείς στους οποίους με ενδιέφερε
Mohamed Salama
Course: Data Mining & Machine Learning with R
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Το θέμα είναι πολύ ενδιαφέρον
Wojciech Baranowski
Course: Introduction to Deep Learning
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Οι εκπαιδευτές θεωρητικές γνώσεις και προθυμία να λύσουν τα προβλήματα με τους συμμετέχοντες μετά την εκπαίδευση
Grzegorz Mianowski
Course: Introduction to Deep Learning
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Θέμα. Πολύ ενδιαφέρον!
Piotr
Course: Introduction to Deep Learning
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Οι ασκήσεις μετά από κάθε θέμα ήταν πραγματικά χρήσιμες, παρά το γεγονός ότι ήταν πολύ περίπλοκες στο τέλος. Γενικά, το υλικό που παρουσιάστηκε ήταν πολύ ενδιαφέρον και ενδιαφέροντα! Οι ασκήσεις με αναγνώριση εικόνας ήταν εξαιρετικές.
Dolby Poland Sp. z o.o.
Course: Introduction to Deep Learning
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Νομίζω ότι εάν η εκπαίδευση θα γίνει στη βουλγαρία θα επιτρέψει στον εκπαιδευτή να μοιραστεί τις γνώσεις του πιο αποτελεσματικά
Radek
Course: Introduction to Deep Learning
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Η γενική επισκόπηση της βαθιάς μάθησης
Bruno Charbonnier
Course: Advanced Deep Learning
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Οι ασκήσεις είναι επαρκώς πρακτικές και δεν χρειάζονται μεγάλη γνώση στην Python για να γίνει.
Alexandre GIRARD
Course: Advanced Deep Learning
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Να κάνετε ασκήσεις με πραγματικά παραδείγματα χρησιμοποιώντας Keras. Ο Μιχάλυ κατανόησε πλήρως τις προσδοκίες μας για αυτή την εκπαίδευση.
Paul Kassis
Course: Advanced Deep Learning
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το θέμα. Φαινόταν ενδιαφέρον, αλλά έφυγα ξέροντας όχι πολύ περισσότερο από πριν.
Radoslaw Labedzki
Course: Introduction to Deep Learning
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Ενδιαφέρον θέμα
Wojciech Wilk
Course: Introduction to Deep Learning
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Εκτιμώ πραγματικά τις πεντακάθαρες απαντήσεις του Chris στις ερωτήσεις μας.
Léo Dubus
Course: Neural Networks Fundamentals using TensorFlow as Example
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Γνωστός εκπαιδευτής
Sridhar Voorakkara
Course: Neural Networks Fundamentals using TensorFlow as Example
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Ήμουν έκπληκτος με το πρότυπο αυτής της κατηγορίας - θα έλεγα ότι ήταν πανεπιστημιακό πρότυπο.
David Relihan
Course: Neural Networks Fundamentals using TensorFlow as Example
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Πολύ καλή σε όλους γύρω από την επισκόπηση. Καλό υπόβαθρο για το γιατί Tensorflow λειτουργεί όπως συμβαίνει.
Kieran Conboy
Course: Neural Networks Fundamentals using TensorFlow as Example
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Μου άρεσαν οι ευκαιρίες να θέσω ερωτήσεις και να δώσω περισσότερες σε βάθος εξηγήσεις για τη θεωρία.
Sharon Ruane
Course: Neural Networks Fundamentals using TensorFlow as Example
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Έχουμε αποκτήσει πολύ περισσότερη διορατικότητα στο θέμα. Κάποια ωραία συζήτηση έγινε με κάποια πραγματικά θέματα στην εταιρεία μας
Sebastiaan Holman
Course: Machine Learning and Deep Learning
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Η εκπαίδευση μας έδωσε το σωστό υπόβαθρο που μας επιτρέπει να επεκτείνουμε περαιτέρω, δείχνοντας πώς η θεωρία και η πρακτική συμβαδίζουν. Στην πραγματικότητα με ενδιέφερε περισσότερο το θέμα από ό, τι πριν.
Jean-Paul van Tillo
Course: Machine Learning and Deep Learning
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Κάλυψη και βάθος των θεμάτων
Anirban Basu
Course: Machine Learning and Deep Learning
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νέες γνώσεις στη βαθιά μηχανική μάθηση
Josip Arneric
Course: Neural Network in R
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Έχουμε αποκτήσει κάποιες γνώσεις σχετικά με το NN γενικά, και αυτό που ήταν το πιο ενδιαφέρον για μένα ήταν οι νέοι τύποι NN που είναι δημοφιλείς στις μέρες μας.
Tea Poklepovic
Course: Neural Network in R
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Γραφήματα σε R :)))
Faculty of Economics and Business Zagreb
Course: Neural Network in R
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Η βαθιά γνώση του εκπαιδευτή για το θέμα.
Sebastian Görg
Course: Introduction to Deep Learning
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Πολύ ενημερωμένη προσέγγιση ή api (tensorflow, kera, tflearn) για να κάνει μηχανική μάθηση
Paul Lee
Course: TensorFlow for Image Recognition
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Πολύ ευέλικτο
Frank Ueltzhöffer
Course: Artificial Neural Networks, Machine Learning and Deep Thinking
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ευκαμψία
Werner Philipp
Course: Artificial Neural Networks, Machine Learning and Deep Thinking
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Λαμβάνοντας υπόψη τις προοπτικές της τεχνολογίας: ποια τεχνολογία / διαδικασία μπορεί να γίνει πιο σημαντική στο μέλλον; δείτε τι μπορεί να χρησιμοποιηθεί η τεχνολογία
Commerzbank AG
Course: Neural Networks Fundamentals using TensorFlow as Example
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Επιλογή θέματος. Στυλ εκπαίδευσης. Πρακτικός προσανατολισμός
Commerzbank AG
Course: Neural Networks Fundamentals using TensorFlow as Example
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Σε βάθος κάλυψη των θεμάτων μηχανικής μάθησης, ιδιαίτερα των νευρωνικών δικτύων. Απομονώθηκε ένα μεγάλο μέρος του θέματος.
Sacha Nandlall
Course: Python for Advanced Machine Learning
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Αυτό είναι ένα από τα καλύτερα hands-on με ασκήσεις μαθήματα προγραμματισμού που έχω πάρει ποτέ.
Laura Kahn
Course: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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Αυτή είναι μια από τις καλύτερες online εκπαιδευτικές ασκήσεις που έχω πάρει ποτέ στην καριέρα μου για 13 χρόνια. Συνεχίστε την καλή δουλειά!
Course: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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Το στυλ εκπαίδευσης του Richard το διατήρησε ενδιαφέρον, τα παραδείγματα του πραγματικού κόσμου που χρησιμοποιήθηκαν συνέβαλαν στην οδήγηση των εννοιών στο σπίτι.
Jamie Martin-Royle - NBrown Group
Course: From Data to Decision with Big Data and Predictive Analytics
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Το περιεχόμενο, όπως το βρήκα πολύ ενδιαφέρον και νομίζω ότι θα με βοήθησε στο τελευταίο μου έτος στο Πανεπιστήμιο.
Krishan Mistry - NBrown Group
Course: From Data to Decision with Big Data and Predictive Analytics
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Ασκηθείτε
L M ERICSSON LIMITED
Course: Machine Learning
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τις εργαστηριακές ασκήσεις
Marcell Lorant - L M ERICSSON LIMITED
Course: Machine Learning
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Η φόρμα notebook Jupyter, στην οποία διατίθεται το εκπαιδευτικό υλικό
L M ERICSSON LIMITED
Course: Machine Learning
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Υπήρχαν πολλές ασκήσεις και ενδιαφέροντα θέματα.
L M ERICSSON LIMITED
Course: Machine Learning
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μερικές μεγάλες εργαστηριακές ασκήσεις αναλύθηκαν και εξηγήθηκαν από τον εκπαιδευτή σε βάθος (π.χ. covariants σε γραμμική παλινδρόμηση, ταιριάζοντας την πραγματική λειτουργία)
L M ERICSSON LIMITED
Course: Machine Learning
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Είναι απλά υπέροχο ότι όλο το υλικό, συμπεριλαμβανομένων των ασκήσεων, βρίσκεται στην ίδια σελίδα και στη συνέχεια ενημερώνεται για λίγο. Η λύση αποκαλύπτεται στο τέλος. Δροσερός! Επίσης, εκτιμώ ότι ο Krzysztof κατέβαλε επιπλέον προσπάθειες για να καταλάβει τα προβλήματά μας και μας πρότεινε πιθανές τεχνικές.
Attila Nagy - L M ERICSSON LIMITED
Course: Machine Learning
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Η εύκολη χρήση της λειτουργικότητας VideoCapture για την απόκτηση εικόνων βίντεο από κάμερα με φορητό υπολογιστή.
HP Printing and Computing Solutions, Sociedad Limitada Unipe
Course: Computer Vision with OpenCV
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Απολάμβανα τις συμβουλές που έδωσε ο εκπαιδευτής σχετικά με τον τρόπο χρήσης των εργαλείων. Αυτό είναι κάτι που δεν μπορεί να πάρει από το διαδίκτυο και είναι πολύ χρήσιμο.
HP Printing and Computing Solutions, Sociedad Limitada Unipe
Course: Computer Vision with OpenCV
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Απολάμβανα τις συμβουλές που έδωσε ο εκπαιδευτής σχετικά με τον τρόπο χρήσης των εργαλείων. Αυτό είναι κάτι που δεν μπορεί να πάρει από το διαδίκτυο και είναι πολύ χρήσιμο.
HP Printing and Computing Solutions, Sociedad Limitada Unipe
Course: Computer Vision with OpenCV
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Ήταν εύκολο να το ακολουθήσεις.
HP Printing and Computing Solutions, Sociedad Limitada Unipe
Course: Computer Vision with OpenCV
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το θέμα παρουσιάστηκε καλά και κατά τρόπο ομαλό.
Marylin Houle - Ivanhoe Cambridge
Course: Introduction to R with Time Series Analysis
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Ήμουν επωφελημένος από το πάθος να διδάσκω και να εστιάζω στο να κάνω κάτι λογικό.
Zaher Sharifi - GOSI
Course: Advanced Deep Learning
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Αυτή είναι μια από τις καλύτερες online εκπαιδευτικές ασκήσεις που έχω πάρει ποτέ στην καριέρα μου για 13 χρόνια. Συνεχίστε την καλή δουλειά!
Course: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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| 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. |
| pythontextml | Python: Machine Learning with Text | 21 hours | In this instructor-led, live training, participants will learn how to use the right machine learning and NLP (Natural Language Processing) techniques to extract value from text-based data. By the end of this training, participants will be able to: - Solve text-based data science problems with high-quality, reusable code - Apply different aspects of scikit-learn (classification, clustering, regression, dimensionality reduction) to solve problems - Build effective machine learning models using text-based data - Create a dataset and extract features from unstructured text - Visualize data with Matplotlib - Build and evaluate models to gain insight - Troubleshoot text encoding errors Audience - Developers - Data Scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| textsum | Text Summarization with Python | 14 hours | In Python Machine Learning, the Text Summarization feature is able to read the input text and produce a text summary. This capability is available from the command-line or as a Python API/Library. One exciting application is the rapid creation of executive summaries; this is particularly useful for organizations that need to review large bodies of text data before generating reports and presentations. In this instructor-led, live training, participants will learn to use Python to create a simple application that auto-generates a summary of input text. By the end of this training, participants will be able to: - Use a command-line tool that summarizes text. - Design and create Text Summarization code using Python libraries. - Evaluate three Python summarization libraries: sumy 0.7.0, pysummarization 1.0.4, readless 1.0.17 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. |
| opennlp | OpenNLP for Text Based Machine Learning | 14 hours | The Apache OpenNLP library is a machine learning based toolkit for processing natural language text. It supports the most common NLP tasks, such as language detection, tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing and coreference resolution. In this instructor-led, live training, participants will learn how to create models for processing text based data using OpenNLP. Sample training data as well customized data sets will be used as the basis for the lab exercises. By the end of this training, participants will be able to: - Install and configure OpenNLP - Download existing models as well as create their own - Train the models on various sets of sample data - Integrate OpenNLP with existing Java applications Audience - Developers - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| mlbankingpython_ | Machine Learning for Banking (with Python) | 21 hours | Machine 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 |
| mlbankingr | Machine Learning for Banking (with R) | 28 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. R 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 live projects. Audience - Developers - Data scientists - Banking professionals with a technical background Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| python_nlp | Natural Language Processing with Deep Dive in Python and NLTK | 35 hours | By the end of the training the delegates are expected to be sufficiently equipped with the essential python concepts and should be able to sufficiently use NLTK to implement most of the NLP and ML based operations. The training is aimed at giving not just an executional knowledge but also the logical and operational knowledge of the technology therein. |
| 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 |
| matlabpredanalytics | Matlab for Predictive Analytics | 21 hours | Predictive analytics is the process of using data analytics to make predictions about the future. This process uses data along with data mining, statistics, and machine learning techniques to create a predictive model for forecasting future events. In this instructor-led, live training, participants will learn how to use Matlab to build predictive models and apply them to large sample data sets to predict future events based on the data. By the end of this training, participants will be able to: - Create predictive models to analyze patterns in historical and transactional data - Use predictive modeling to identify risks and opportunities - Build mathematical models that capture important trends - Use data from devices and business systems to reduce waste, save time, or cut costs Audience - Developers - Engineers - Domain experts Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| 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 |
| mlios | Machine Learning on iOS | 14 hours | In this instructor-led, live training, participants will learn how to use the iOS Machine Learning (ML) technology stack as they step through the creation and deployment of an iOS mobile app. By the end of this training, participants will be able to: - Create a mobile app capable of image processing, text analysis and speech recognition - Access pre-trained ML models for integration into iOS apps - Create a custom ML model - Add Siri Voice support to iOS apps - Understand and use frameworks such as coreML, Vision, CoreGraphics, and GamePlayKit - Use languages and tools such as Python, Keras, Caffee, Tensorflow, sci-kit learn, libsvm, Anaconda, and Spyder Audience - Developers Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| nlg | Python for Natural Language Generation | 21 hours | Natural language generation (NLG) refers to the production of natural language text or speech by a computer. In this instructor-led, live training, participants will learn how to use Python to produce high-quality natural language text by building their own NLG system from scratch. Case studies will also be examined and the relevant concepts will be applied to live lab projects for generating content. By the end of this training, participants will be able to: - Use NLG to automatically generate content for various industries, from journalism, to real estate, to weather and sports reporting - Select and organize source content, plan sentences, and prepare a system for automatic generation of original content - Understand the NLG pipeline and apply the right techniques at each stage - Understand the architecture of a Natural Language Generation (NLG) system - Implement the most suitable algorithms and models for analysis and ordering - Pull data from publicly available data sources as well as curated databases to use as material for generated text - Replace manual and laborious writing processes with computer-generated, automated content creation Audience - Developers - Data scientists 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 |
| bigdatabicriminal | Big Data Business Intelligence for Criminal Intelligence Analysis | 35 hours | Advances in technologies and the increasing amount of information are transforming how law enforcement is conducted. The challenges that Big Data pose are nearly as daunting as Big Data's promise. Storing data efficiently is one of these challenges; effectively analyzing it is another. In this instructor-led, live training, participants will learn the mindset with which to approach Big Data technologies, assess their impact on existing processes and policies, and implement these technologies for the purpose of identifying criminal activity and preventing crime. Case studies from law enforcement organizations around the world will be examined to gain insights on their adoption approaches, challenges and results. By the end of this training, participants will be able to: - Combine Big Data technology with traditional data gathering processes to piece together a story during an investigation - Implement industrial big data storage and processing solutions for data analysis - Prepare a proposal for the adoption of the most adequate tools and processes for enabling a data-driven approach to criminal investigation Audience - Law Enforcement specialists with a technical background 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 |
| 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 |
| 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. Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| fiji | Fiji: Introduction to Scientific Image Processing | 21 hours | Fiji is an open-source image processing package that bundles ImageJ (an image processing program for scientific multidimensional images) and a number of plugins for scientific image analysis. In this instructor-led, live training, participants will learn how to use the Fiji distribution and its underlying ImageJ program to create an image analysis application. By the end of this training, participants will be able to: - Use Fiji's advanced programming features and software components to extend ImageJ - Stitch large 3d images from overlapping tiles - Automatically update a Fiji installation on startup using the integrated update system - Select from a broad selection of scripting languages to build custom image analysis solutions - Use Fiji's powerful libraries, such as ImgLib on large bioimage datasets - Deploy their application and collaborate with other scientists on similar projects Audience - Scientists - Researchers - Developers Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| rasberrypiopencv | Raspberry Pi + OpenCV: Build a Facial Recognition System | 21 hours | This instructor-led, live training introduces the software, hardware, and step-by-step process needed to build a facial recognition system from scratch. Facial Recognition is also known as Face Recognition. The hardware used in this lab includes Rasberry Pi, a camera module, servos (optional), etc. Participants are responsible for purchasing these components themselves. The software used includes OpenCV, Linux, Python, etc. By the end of this training, participants will be able to: - Install Linux, OpenCV and other software utilities and libraries on a Rasberry Pi. - Configure OpenCV to capture and detect facial images. - Understand the various options for packaging a Rasberry Pi system for use in real-world environments. - Adapt the system for a variety of use cases, including surveillance, identity verification, etc. Audience - Developers - Hardware/software technicians - Technical persons in all industries - Hobbyists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice Note - Other hardware and software options include: Arduino, OpenFace, Windows, etc. If you wish to use any of these, please contact us to arrange. |
| 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 |
| 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 |
| 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 |
| cognitivecomputing | Cognitive Computing: An Introduction for Business Managers | 7 hours | Cognitive computing refers to systems that encompass machine learning, reasoning, natural language processing, speech recognition and vision (object recognition), human–computer interaction, dialog and narrative generation, to name a few. A cognitive computing system is often comprised of multiple technologies working together to process in-memory 'hot' contextual data as well as large sets of 'cold' historical data in batch. Examples of such technologies include Kafka, Spark, Elasticsearch, Cassandra, and Hadoop. In this instructor-led, live training, participants will learn how Cognitive Computing compliments AI and Big Data and how purpose-built systems can be used to realize human-like behaviors that improve the performance of human-machine interactions in business. By the end of this training, participants will understand: - The relationship between cognitive computing and artificial intelligence (AI) - The inherently probabilistic nature of cognitive computing and how to use it as a business advantage - How to manage cognitive computing systems that behave in unexpected ways - Which companies and software systems offer the most compelling cognitive computing solutions Audience - Business managers Format of the course - Lecture, case discussions and exercises |
| dsstne | Amazon DSSTNE: Build a Recommendation System | 7 hours | 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 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 |
| dlfornlp | Deep Learning for NLP (Natural Language Processing) | 28 hours | DL (Deep Learning) is a subset of ML (Machine Learning). Python is a popular programming language that contains libraries for Deep Learning for NLP. Deep Learning for NLP (Natural Language Processing) allows a machine to learn simple to complex language processing. Among the tasks currently possible are language translation and caption generation for photos. In this instructor-led, live training, participants will learn to use Python libraries for NLP 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 Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| mlfinancepython | Machine Learning for Finance (with Python) | 21 hours | Machine 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 finance 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. By the end of this training, participants will be able to: - Understand the fundamental concepts in machine learning - Learn the applications and uses of machine learning in finance - Develop their own algorithmic trading strategy using machine learning with Python Audience - Developers - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| 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. |
| ML_LBG | Machine Learning – Data science | 21 hours | This classroom based training session will explore machine learning tools with (suggested) Python. Delegates will have computer based examples and case study exercises to undertake. |
| Course | Course Date | Course Price [Remote / Classroom] |
|---|---|---|
| Applied Machine Learning - Πάτρα (Patras) | Wed, 2019-03-06 09:30 | 3500EUR / 4100EUR |
| Applied Machine Learning - Nicosia, Jacovides Tower | Mon, 2019-03-11 09:30 | 3500EUR / 4100EUR |
| Applied Machine Learning - Θεσσαλονίκη (Thessaloniki) | Wed, 2019-03-20 09:30 | 3500EUR / 4100EUR |
| Applied Machine Learning - Αθήνα (Athens), Acropolis | Mon, 2019-04-01 09:30 | 3500EUR / 4100EUR |
| Applied Machine Learning - Πάτρα (Patras) | Mon, 2019-04-29 09:30 | 3500EUR / 4100EUR |
| Course | Venue | Course Date | Course Price [Remote / Classroom] |
|---|---|---|---|
| Introduction to Embedded Computers | Αθήνα (Athens), Acropolis | Mon, 2019-02-25 09:30 | 3150EUR / 3750EUR |
| Neural computing – Data science | Αθήνα (Athens), Acropolis | Mon, 2019-06-03 09:30 | 3150EUR / 3750EUR |
| Introduction to Ansible Automation | Θεσσαλονίκη (Thessaloniki) | Mon, 2019-06-10 09:30 | 1350EUR / 1750EUR |
| Introduction to R with Time Series Analysis | Αθήνα (Athens), Acropolis | Wed, 2019-07-31 09:30 | 4725EUR / 5525EUR |
| AI Awareness for Telecom | Πάτρα (Patras) | Thu, 2019-09-05 09:30 | 3150EUR / 3750EUR |
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