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
Machine Translated
Testimonials
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
Machine Translated
Το θέμα είναι πολύ ενδιαφέρον
Wojciech Baranowski
Course: Introduction to Deep Learning
Machine Translated
Οι εκπαιδευτές θεωρητικές γνώσεις και προθυμία να λύσουν τα προβλήματα με τους συμμετέχοντες μετά την εκπαίδευση
Grzegorz Mianowski
Course: Introduction to Deep Learning
Machine Translated
Θέμα. Πολύ ενδιαφέρον!
Piotr
Course: Introduction to Deep Learning
Machine Translated
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
Machine Translated
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
Machine Translated
Η γενική επισκόπηση της βαθιάς μάθησης
Bruno Charbonnier
Course: Advanced Deep Learning
Machine Translated
Οι ασκήσεις είναι επαρκώς πρακτικές και δεν χρειάζονται μεγάλη γνώση στην Python για να γίνουν.
Alexandre GIRARD
Course: Advanced Deep Learning
Machine Translated
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
Machine Translated
Εκτιμώ πραγματικά τις πεντακάθαρες απαντήσεις του Chris στις ερωτήσεις μας.
Léo Dubus
Course: Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple
Machine Translated
Γνωστός εκπαιδευτής
Sridhar Voorakkara
Course: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Ήμουν έκπληκτος με το πρότυπο αυτής της κατηγορίας - θα έλεγα ότι ήταν πανεπιστημιακό πρότυπο.
David Relihan
Course: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Πολύ καλή γενική επισκόπηση. Go από το φόντο στο γιατί ο Tensorflow λειτουργεί όπως έχει.
Kieran Conboy
Course: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
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
Machine Translated
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
Machine Translated
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
Machine Translated
Κάλυψη και βάθος των θεμάτων
Anirban Basu
Course: Machine Learning and Deep Learning
Machine Translated
Η βαθιά γνώση του εκπαιδευτή για το θέμα.
Sebastian Görg
Course: Introduction to Deep Learning
Machine Translated
Πολύ ενημερωμένη προσέγγιση ή api (tensorflow, kera, tflearn) για να κάνει μηχανική μάθηση
Paul Lee
Course: TensorFlow for Image Recognition
Machine Translated
Πολύ ευέλικτο
Frank Ueltzhöffer
Course: Artificial Neural Networks, Machine Learning and Deep Thinking
Machine Translated
ευκαμψία
Werner Philipp
Course: Artificial Neural Networks, Machine Learning and Deep Thinking
Machine Translated
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
Machine Translated
Επιλογή θέματος. Στυλ εκπαίδευσης. Πρακτικός προσανατολισμός
Commerzbank AG
Course: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
τρόπος διεξαγωγής και παράδειγμα από τον εκπαιδευτή
ORANGE POLSKA S.A.
Course: Machine Learning and Deep Learning
Machine Translated
Δυνατότητα να συζητήσετε μόνοι σας τα προτεινόμενα θέματα
ORANGE POLSKA S.A.
Course: Machine Learning and Deep Learning
Machine Translated
Επικοινωνία με τον καθηγητή
文欣 张
Course: Artificial Neural Networks, Machine Learning, Deep Thinking
Machine Translated
Μου αρέσει
lisa xie
Course: Artificial Neural Networks, Machine Learning, Deep Thinking
Machine Translated
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
Machine Translated
Μεγάλη και ενημερωμένη γνώση κορυφαίων και πρακτικών παραδειγμάτων εφαρμογής.
ING Bank Śląski S.A.
Course: Introduction to Deep Learning
Machine Translated
Πολλές ασκήσεις, πολύ καλή συνεργασία με την ομάδα.
Janusz Chrobot - ING Bank Śląski S.A.
Course: Introduction to Deep Learning
Machine Translated
εργασία σε συνεργάτες,
ING Bank Śląski S.A.
Course: Introduction to Deep Learning
Machine Translated
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
Machine Translated
Ένα ευρύ φάσμα θεμάτων που καλύπτονται και ουσιαστική γνώση των ηγετών.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
Machine Translated
έλλειψη
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
Machine Translated
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
Machine Translated
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
Machine Translated
ασκήσεις και παραδείγματα που εφαρμόζονται σε αυτά
Paweł Orzechowski - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
Machine Translated
Παραδείγματα και θέματα που συζητήθηκαν.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
Machine Translated
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
Machine Translated
Πρακτικές ασκήσεις που εκπόνησε ο κ. Maciej
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
Machine Translated
Ήμουν επωφελημένος από το πάθος να διδάσκω και να εστιάζω στο να κάνω κάτι λογικό.
Zaher Sharifi - GOSI
Course: Advanced Deep Learning
Machine Translated
Ταυτοποίηση ανθρώπινου στοιχείου και ανίχνευση κακών σημείων
王 春柱 - 中移物联网
Course: Deep Learning for NLP (Natural Language Processing)
Machine Translated
Αποδείξει
中移物联网
Course: Deep Learning for NLP (Natural Language Processing)
Machine Translated
Σχετικά με την περιοχή του προσώπου.
中移物联网
Course: Deep Learning for NLP (Natural Language Processing)
Machine Translated
Οι ανεπίσημες ανταλλαγές που είχαμε κατά τη διάρκεια των διαλέξεων με βοήθησαν πραγματικά να εμβαθύνω την κατανόησή μου για το θέμα
Explore
Course: Deep Reinforcement Learning with Python
Machine Translated
Πολλές πρακτικές συμβουλές
Pawel Dawidowski - ABB Sp. z o.o.
Course: Deep Learning with TensorFlow
Machine Translated
Πολλές πληροφορίες σχετικά με την εφαρμογή λύσεων
Michał Smolana - ABB Sp. z o.o.
Course: Deep Learning with TensorFlow
Machine Translated
Ένα πλήθος πρακτικών συμβουλών και γνώσεων του διδάσκοντος από ένα ευρύ φάσμα θεμάτων AI / IT / SQL / IoT.
ABB Sp. z o.o.
Course: Deep Learning with TensorFlow
Machine Translated
πολλές πληροφορίες, όλες τις ερωτήσεις που αναφέρθηκαν, ενδιαφέροντα παραδείγματα
A1 Telekom Austria AG
Course: Deep Learning for Telecom (with Python)
Machine Translated
Ξεκίνησα με σχεδόν μηδενική γνώση και μέχρι το τέλος μπορώ να οικοδομήσω και να εκπαιδεύσω τα δικά μου δίκτυα.
Huawei Technologies Duesseldorf GmbH
Course: TensorFlow for Image Recognition
Machine Translated
Deep Learning Subcategories
Deep Learning (DL) Course Outlines
Course Name
Duration
Overview
Course Name
Duration
Overview
21 hours
In this instructor-led, live training in Ελλάδα, 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.
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.
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
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
28 hours
In this instructor-led, live training in Ελλάδα, 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.
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.
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.
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.
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.
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.
21 hours
Caffe is a deep learning framework made with expression, speed, and modularity in mind.
This course explores the application of Caffe as a Deep learning framework for image recognition using MNIST as an example
Audience
This course is suitable for Deep Learning researchers and engineers interested in utilizing Caffe as a framework.
After completing this course, delegates will be able to:
- understand Caffe’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, implementing layers and logging
This course explores the application of Caffe as a Deep learning framework for image recognition using MNIST as an example
Audience
This course is suitable for Deep Learning researchers and engineers interested in utilizing Caffe as a framework.
After completing this course, delegates will be able to:
- understand Caffe’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, implementing layers and logging
21 hours
Audience
This course is suitable for Deep Learning researchers and engineers interested in utilizing available tools (mostly open source) for analyzing computer images
This course provide working examples.
This course is suitable for Deep Learning researchers and engineers interested in utilizing available tools (mostly open source) for analyzing computer images
This course provide working examples.
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.
21 hours
This course covers AI (emphasizing Machine Learning and Deep Learning)
14 hours
In this instructor-led, live training, we go over the principles of neural networks and use OpenNN to implement a sample application.
Format of the course
- Lecture and discussion coupled with hands-on exercises.
Format of the course
- Lecture and discussion coupled with hands-on exercises.
7 hours
In this instructor-led, live training, participants will learn how to set up and use OpenNMT to carry out translation of various sample data sets. The course starts with an overview of neural networks as they apply to machine translation. Participants will carry out live exercises throughout the course to demonstrate their understanding of the concepts learned and get feedback from the instructor.
By the end of this training, participants will have the knowledge and practice needed to implement a live OpenNMT solution.
Source and target language samples will be pre-arranged per the audience's requirements.
Format of the Course
- Part lecture, part discussion, heavy hands-on practice
By the end of this training, participants will have the knowledge and practice needed to implement a live OpenNMT solution.
Source and target language samples will be pre-arranged per the audience's requirements.
Format of the Course
- Part lecture, part discussion, heavy hands-on practice
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.
7 hours
In this instructor-led, live training, participants will learn how to use Facebook NMT (Fairseq) to carry out translation of sample content.
By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution.
Format of the course
- Part lecture, part discussion, heavy hands-on practice
Note
- If you wish to use specific source and target language content, please contact us to arrange.
By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution.
Format of the course
- Part lecture, part discussion, heavy hands-on practice
Note
- If you wish to use specific source and target language content, please contact us to arrange.
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.
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.
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
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
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
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
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
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
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
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
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:
- Understand and implement unsupervised learning techniques
- Apply clustering and classification to make predictions based on real world data.
- Visualize data to quicly gain insights, make decisions and further refine analysis.
- Improve the performance of a machine learning model using hyper-parameter tuning.
- Put a model into production for use in a larger application.
- Apply advanced machine learning techniques to answer questions involving social network data, big data, and more.
By the end of this training, participants will be able to:
- Understand and implement unsupervised learning techniques
- Apply clustering and classification to make predictions based on real world data.
- Visualize data to quicly gain insights, make decisions and further refine analysis.
- Improve the performance of a machine learning model using hyper-parameter tuning.
- Put a model into production for use in a larger application.
- Apply advanced machine learning techniques to answer questions involving social network data, big data, and more.
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
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
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
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
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
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
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.
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.
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
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.
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.
14 hours
This instructor-led, live training in Ελλάδα (online or onsite) is aimed at developers who wish to build hardware-accelerated object detection and tracking models to analyze streaming video data.
By the end of this training, participants will be able to:
- Install and configure the necessary development environment, software and libraries to begin developing.
- Build, train, and deploy deep learning models to analyze live video feeds.
- Identify, track, segment and predict different objects within video frames.
- Optimize object detection and tracking models.
- Deploy an intelligent video analytics (IVA) application.
By the end of this training, participants will be able to:
- Install and configure the necessary development environment, software and libraries to begin developing.
- Build, train, and deploy deep learning models to analyze live video feeds.
- Identify, track, segment and predict different objects within video frames.
- Optimize object detection and tracking models.
- Deploy an intelligent video analytics (IVA) application.
14 hours
This instructor-led, live training in Ελλάδα (online or onsite) is aimed at developers and data scientists who wish to apply convolutional neural networks (CNNs) to the analysis of MRI scans.
By the end of this training, participants will be able to:
- Install and configure the necessary development environment, software and libraries to begin developing.
- Analyze MRI images using deep learning techniques such as CNNs.
- Detect potential health conditions such as heart disease through MRI scan analysis.
- Apply techniques such as image segmentation and CNN training to identify potential disease.
- Identify the genomics of a disease using radiomics.
- Build and deploy a deep learning application aimed at healthcare image analysis.
By the end of this training, participants will be able to:
- Install and configure the necessary development environment, software and libraries to begin developing.
- Analyze MRI images using deep learning techniques such as CNNs.
- Detect potential health conditions such as heart disease through MRI scan analysis.
- Apply techniques such as image segmentation and CNN training to identify potential disease.
- Identify the genomics of a disease using radiomics.
- Build and deploy a deep learning application aimed at healthcare image analysis.
14 hours
This instructor-led, live training in Ελλάδα (online or onsite) is aimed at business analysts, data scientists, and developers who wish to build and implement deep learning models to accelerate revenue growth and solve problems in the business world.
By the end of this training, participants will be able to:
- Understand the core concepts of machine learning and deep learning.
- Get insights on the future of business and industry with ML and DL.
- Define business strategies and solutions with deep learning.
- Learn how to apply data science and deep learning in solving business problems.
- Build deep learning models using Python, Pandas, TensorFlow, CNTK, Torch, Keras, etc.
By the end of this training, participants will be able to:
- Understand the core concepts of machine learning and deep learning.
- Get insights on the future of business and industry with ML and DL.
- Define business strategies and solutions with deep learning.
- Learn how to apply data science and deep learning in solving business problems.
- Build deep learning models using Python, Pandas, TensorFlow, CNTK, Torch, Keras, etc.
Last Updated:
Upcoming Deep Learning (DL) Courses
Deep Learning for Banking (with R)
14-02-2022 09:30
28 hours
Deep Learning for Banking (with Python)
15-02-2022 09:30
28 hours
Deep Learning AI Techniques for Executives, Developers and Managers
16-02-2022 09:30
21 hours
Deep Reinforcement Learning with Python
16-02-2022 09:30
21 hours
Deep Learning for Finance (with Python)
14-03-2022 09:30
28 hours
Other countries
Consulting
Online Deep Learning (DL) courses, Weekend Deep Learning (DL) courses, Evening Deep Learning training, Deep Learning boot camp, Deep Learning instructor-led, Weekend Deep Learning training, Evening Deep Learning (DL) courses, DL (Deep Learning) coaching, DL (Deep Learning) instructor, Deep Learning (DL) trainer, DL (Deep Learning) training courses, Deep Learning (DL) classes, Deep Learning (DL) on-site, DL (Deep Learning) private courses, Deep Learning (DL) one on one training
.jpg "international air transport association (iata)")
