Τα τοπικά μαθήματα κατάρτισης Deep Learning (DL) που διοργανώνονται από τοπικούς εκπαιδευτές, επιδεικνύουν με πρακτική άσκηση τις βασικές αρχές και εφαρμογές της βαθιάς μάθησης και καλύπτουν θέματα όπως η βαθιά μηχανική μάθηση, η βαθιά δομημένη μάθηση και η ιεραρχική μάθηση. Η εκπαίδευση Deep Learning 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
Translated by
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
Translated by
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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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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τρόπος διεξαγωγής και παράδειγμα από τον εκπαιδευτή
ORANGE POLSKA S.A.
Course: Machine Learning and Deep Learning
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Δυνατότητα να συζητήσετε μόνοι σας τα προτεινόμενα θέματα
ORANGE POLSKA S.A.
Course: Machine Learning and Deep Learning
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Επικοινωνία με τον καθηγητή
文欣 张
Course: Artificial Neural Networks, Machine Learning, Deep Thinking
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Μου αρέσει
lisa xie
Course: Artificial Neural Networks, Machine Learning, Deep Thinking
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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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Μεγάλη και ενημερωμένη γνώση κορυφαίων και πρακτικών παραδειγμάτων εφαρμογής.
ING Bank Śląski S.A.
Course: Introduction to Deep Learning
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Πολλές ασκήσεις, πολύ καλή συνεργασία με την ομάδα.
Janusz Chrobot - ING Bank Śląski S.A.
Course: Introduction to Deep Learning
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εργασία σε συνεργάτες,
ING Bank Śląski S.A.
Course: Introduction to Deep Learning
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Ήταν προφανές ότι οι ενθουσιώδες των παρουσιαζόμενων θεμάτων ήταν οι κορυφαίοι. Χρησιμοποιούνται ενδιαφέροντα παραδείγματα κατά τη διάρκεια της άσκησης.
ING Bank Śląski S.A.
Course: Introduction to Deep Learning
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Ένα ευρύ φάσμα θεμάτων που καλύπτονται και ουσιαστική γνώση των ηγετών.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
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έλλειψη
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
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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
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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
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ασκήσεις και παραδείγματα που εφαρμόζονται σε αυτά
Paweł Orzechowski - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
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Παραδείγματα και θέματα που συζητήθηκαν.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
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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
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Πρακτικές ασκήσεις που εκπόνησε ο κ. Maciej
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
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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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| Code | Name | Duration | Overview |
|---|---|---|---|
| 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. |
| 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. |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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. |
| dlfinancewithr | Deep Learning for Finance (with R) | 28 hours | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to implement deep learning models for finance using R as they step through the creation of a deep learning stock price prediction model. By the end of this training, participants will be able to: - Understand the fundamental concepts of deep learning - Learn the applications and uses of deep learning in finance - Use R to create deep learning models for finance - Build their own deep learning stock price prediction model using R Audience - Developers - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| dlforbankingwithpython | Deep Learning for Banking (with Python) | 28 hours | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for banking using Python as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to: - Understand the fundamental concepts of deep learning - Learn the applications and uses of deep learning in banking - Use Python, Keras, and TensorFlow to create deep learning models for banking - Build their own deep learning credit risk model using Python Audience - Developers - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| dlforbankingwithr | Deep Learning for Banking (with R) | 28 hours | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to implement deep learning models for banking using R as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to: - Understand the fundamental concepts of deep learning - Learn the applications and uses of deep learning in banking - Use R to create deep learning models for banking - Build their own deep learning credit risk model using R Audience - Developers - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| dlforfinancewithpython | Deep Learning for Finance (with Python) | 28 hours | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for finance using Python as they step through the creation of a deep learning stock price prediction model. By the end of this training, participants will be able to: - Understand the fundamental concepts of deep learning - Learn the applications and uses of deep learning in finance - Use Python, Keras, and TensorFlow to create deep learning models for finance - Build their own deep learning stock price prediction model using Python Audience - Developers - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| 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 |
| DLAITEDM | Deep Learning AI Techniques for Executives, Developers and Managers | 21 hours | Introduction: Deep learning is becoming a principal component of future product design that wants to incorporate artificial intelligence at the heart of their models. Within the next 5 to 10 years, deep learning development tools, libraries, and languages will become standard components of every software development toolkit. So far Google, Sales Force, Facebook, Amazon have been successfully using deep learning AI to boost their business. Applications ranged from automatic machine translation, image analytics, video analytics, motion analytics, generating targeted advertisement and many more. This coursework is aimed for those organizations who want to incorporate Deep Learning as very important part of their product or service strategy. Below is the outline of the deep learning course which we can customize for different levels of employees/stakeholders in an organization. Target Audience: ( Depending on target audience, course materials will be customized) Executives A general overview of AI and how it fits into corporate strategy, with breakout sessions on strategic planning, technology roadmaps, and resource allocation to ensure maximum value. Project Managers How to plan out an AI project, including data gathering and evaluation, data cleanup and verification, development of a proof-of-concept model, integration into business processes, and delivery across the organization. Developers In-depth technical trainings, with focus on neural networks and deep learning, image and video analytics (CNNs), sound and text analytics (NLP), and bringing AI into existing applications. Salespersons A general overview of AI and how it can satisfy customer needs, value propositions for various products and services, and how to allay fears and promote the benefits of AI. |
| Nue_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 |
| dlformedicine | Deep Learning for Medicine | 14 hours | Machine Learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep Learning is a subfield of Machine Learning which attempts to mimic the workings of the human brain in making decisions. It is trained with data in order to automatically provide solutions to problems. Deep Learning provides vast opportunities for the medical industry which is sitting on a data goldmine. In this instructor-led, live training, participants will take part in a series of discussions, exercises and case-study analysis to understand the fundamentals of Deep Learning. The most important Deep Learning tools and techniques will be evaluated and exercises will be carried out to prepare participants for carrying out their own evaluation and implementation of Deep Learning solutions within their organizations. By the end of this training, participants will be able to: - Understand the fundamentals of Deep Learning - Learn Deep Learning techniques and their applications in the industry - Examine issues in medicine which can be solved by Deep Learning technologies - Explore Deep Learning case studies in medicine - Formulate a strategy for adopting the latest technologies in Deep Learning for solving problems in medicine Audience - Managers - Medical professionals in leadership roles Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice Note - To request a customized training for this course, please contact us to arrange. |
| dlfortelecomwithpython | Deep Learning for Telecom (with Python) | 28 hours | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for telecom using Python as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to: - Understand the fundamental concepts of deep learning - Learn the applications and uses of deep learning in telecom - Use Python, Keras, and TensorFlow to create deep learning models for telecom - Build their own deep learning customer churn prediction model using Python 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 Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| deeplearning1 | Introduction to Deep Learning | 21 hours | This course is general overview for Deep Learning without going too deep into any specific methods. It is suitable for people who want to start using Deep learning to enhance their accuracy of prediction. |
| dl4jir | DeepLearning4J for Image Recognition | 21 hours | Deeplearning4j is an Open-Source Deep-Learning Software for Java and Scala on Hadoop and Spark. Audience This course is meant for engineers and developers seeking to utilize DeepLearning4J in their image recognition projects. |
| 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. |
| dladv | Advanced Deep Learning | 28 hours | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. |
| tf101 | Deep Learning with TensorFlow | 21 hours | TensorFlow is a 2nd Generation API of Google's open source software library for Deep Learning. The system is designed to facilitate research in machine learning, and to make it quick and easy to transition from research prototype to production system. Audience This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects After completing this course, delegates will: - understand TensorFlow’s structure and deployment mechanisms - be able to carry out installation / production environment / architecture tasks and configuration - be able to assess code quality, perform debugging, monitoring - be able to implement advanced production like training models, building graphs and logging |
| tfir | TensorFlow for Image Recognition | 28 hours | This course explores, with specific examples, the application of Tensor Flow to the purposes of image recognition Audience This course is intended for engineers seeking to utilize TensorFlow for the purposes of Image Recognition After completing this course, delegates will be able to: - understand TensorFlow’s structure and deployment mechanisms - carry out installation / production environment / architecture tasks and configuration - assess code quality, perform debugging, monitoring - implement advanced production like training models, building graphs and logging |
| tsflw2v | Natural Language Processing with TensorFlow | 35 hours | TensorFlow™ is an open source software library for numerical computation using data flow graphs. SyntaxNet is a neural-network Natural Language Processing framework for TensorFlow. Word2Vec is used for learning vector representations of words, called "word embeddings". Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (Chapter 3.1 and 3.2 in Mikolov et al.). Used in tandem, SyntaxNet and Word2Vec allows users to generate Learned Embedding models from Natural Language input. Audience This course is targeted at Developers and engineers who intend to work with SyntaxNet and Word2Vec models in their TensorFlow graphs. After completing this course, delegates will: - understand TensorFlow’s structure and deployment mechanisms - be able to carry out installation / production environment / architecture tasks and configuration - be able to assess code quality, perform debugging, monitoring - be able to implement advanced production like training models, embedding terms, building graphs and logging |
| Course | Course Date | Course Price [Remote / Classroom] |
|---|---|---|
| Deep Learning AI Techniques for Executives, Developers and Managers - Πάτρα (Patras) | Wed, 2019-04-17 09:30 | 5250EUR / 6050EUR |
| Deep Learning AI Techniques for Executives, Developers and Managers - Αθήνα (Athens), Acropolis | Tue, 2019-04-30 09:30 | 5250EUR / 6050EUR |
| Deep Learning AI Techniques for Executives, Developers and Managers - Θεσσαλονίκη (Thessaloniki) | Tue, 2019-05-21 09:30 | 5250EUR / 6050EUR |
| Deep Learning AI Techniques for Executives, Developers and Managers - Nicosia, Jacovides Tower | Wed, 2019-05-22 09:30 | 5250EUR / 6050EUR |
| Deep Learning AI Techniques for Executives, Developers and Managers - Πάτρα (Patras) | Tue, 2019-06-11 09:30 | 5250EUR / 6050EUR |
| Course | Venue | Course Date | Course Price [Remote / Classroom] |
|---|---|---|---|
| Deep Learning for Finance (with R) | Αθήνα (Athens), Acropolis | Mon, 2019-02-18 09:30 | 6300EUR / 7300EUR |
| 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 |
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