Місцеві, інструктор під керівництвом живої глибокої навчання (DL) навчальних курсів продемонструвати через практичний практиці основи та застосування глибокого навчання і охоплюють такі предмети, як глибоке навчання машини, глибоке структуроване навчання і ієрархічне навчання.
Поглиблена підготовка навчання доступна як "на місці навчання" або "дистанційне навчання жити". На місці навчання в прямому ефірі може здійснюватися на місцевому рівні в україні або в Неbleprog корпоративні навчальні центри в україні. Дистанційне навчання в прямому ефірі здійснюється шляхом інтерактивного, віддаленого робочого стола.
Nobleпрога-ваш місцевий навчальний провайдер
Machine Translated
Відгуки
Це було дуже інтерактивним і більш розслабленим і неформальним, ніж очікувалося. Ми розглянули багато тем в той час, і тренер завжди сприйнятливі говорити більш докладно або в цілому про теми і як вони були пов'язані. Я відчуваю, що навчання дав мені інструменти, щоб продовжити навчання на відміну від його час одного сеансу, де навчання зупиняється, як тільки ви закінчили, що дуже важливо з огляду на масштаби і складність теми.
Jonathan Blease
Курси: Artificial Neural Networks, Machine Learning, Deep Thinking
Machine Translated
Тема дуже цікава.
Wojciech Baranowski
Курси: Introduction to Deep Learning
Machine Translated
Тренери теоретичні знання і готовність вирішувати проблеми з учасниками після навчання.
Grzegorz Mianowski
Курси: Introduction to Deep Learning
Machine Translated
Тему. Дуже цікаво!.
Piotr
Курси: Introduction to Deep Learning
Machine Translated
Вправи після кожної теми були дуже корисними, незважаючи на те, що були занадто складні в кінці. Загалом, представлений матеріал був дуже цікавим і за участю! Вправи з розпізнаванням зображення були великими.
Dolby Poland Sp. z o.o.
Курси: Introduction to Deep Learning
Machine Translated
Я думаю, що якщо тренінг буде зроблено польською це дозволить тренеру поділитися своїми знаннями більш ефективними.
Radek
Курси: Introduction to Deep Learning
Machine Translated
Глобальний огляд глибокого навчання.
Bruno Charbonnier
Курси: Advanced Deep Learning
Machine Translated
Вправи достатньо практичні і не потребують високих знань з Python .
Alexandre GIRARD
Курси: Advanced Deep Learning
Machine Translated
Виконання вправ на реальних прикладах за допомогою епох. Італія повністю розуміла наші очікування щодо цього навчання.
Paul Kassis
Курси: Advanced Deep Learning
Machine Translated
Я дуже ціную кришталево чіткі відповіді Кріса на наші запитання.
Léo Dubus
Курси: Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple
Machine Translated
Я взагалі користувався знаючими тренера.
Sridhar Voorakkara
Курси: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Я був вражений стандартом цього класу-я хотів би сказати, що це був університетський стандарт.
David Relihan
Курси: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Дуже хороший всебічний огляд. Go оди фону в чому Tensorflow працює , як це робить.
Kieran Conboy
Курси: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Мені сподобалися можливості задавати питання і отримати більш глибокі пояснення теорії.
Sharon Ruane
Курси: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Ми отримали набагато більше розуміння в питанні. Деякі цікаві дискусії були зроблені з деякими реальними предметами в нашій компанії.
Sebastiaan Holman
Курси: Machine Learning and Deep Learning
Machine Translated
Навчання дало правильну основу, яка дозволяє нам далі розширюватися, показуючи, як теорія та практика йдуть рука об руку. Це насправді мене більше зацікавило цю тему, ніж я раніше.
Jean-Paul van Tillo
Курси: Machine Learning and Deep Learning
Machine Translated
Мені дуже сподобався охоплення і глибину теми.
Anirban Basu
Курси: Machine Learning and Deep Learning
Machine Translated
Глибокі знання тренера про тему.
Sebastian Görg
Курси: Introduction to Deep Learning
Machine Translated
Дуже оновлений підхід чи ІСЦ (течія тензора, ера, вчитися) робити Машинне навчання.
Paul Lee
Курси: TensorFlow for Image Recognition
Machine Translated
Дуже гнучким.
Frank Ueltzhöffer
Курси: Artificial Neural Networks, Machine Learning and Deep Thinking
Machine Translated
Я взагалі користувався гнучкістю.
Werner Philipp
Курси: Artificial Neural Networks, Machine Learning and Deep Thinking
Machine Translated
Враховуючи перспективи технології: які технології/процесу можуть стати більш важливими в майбутньому; Дивіться, які технології можна використовувати для.
Commerzbank AG
Курси: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Я отримав вигоду від вибору теми. Стиль навчання. Практична орієнтація.
Commerzbank AG
Курси: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Як вони вводять і застосовуються тренером
ORANGE POLSKA S.A.
Курси: Machine Learning and Deep Learning
Machine Translated
Можливість обговорити запропоновані питання
ORANGE POLSKA S.A.
Курси: Machine Learning and Deep Learning
Machine Translated
Зв'язок з викладачем
文欣 张
Курси: Artificial Neural Networks, Machine Learning, Deep Thinking
Machine Translated
Як це
lisa xie
Курси: Artificial Neural Networks, Machine Learning, Deep Thinking
Machine Translated
Поглиблене висвітлення тем машинного навчання, зокрема нейронних мереж. Дуже багато тем.
Sacha Nandlall
Курси: Python for Advanced Machine Learning
Machine Translated
Великий і актуальний Орієнтир знань і практичних прикладів застосування.
ING Bank Śląski S.A.
Курси: Introduction to Deep Learning
Machine Translated
Багато вправ, дуже хороша співпраця з групою.
Janusz Chrobot - ING Bank Śląski S.A.
Курси: Introduction to Deep Learning
Machine Translated
Практик на коловатори,
ING Bank Śląski S.A.
Курси: Introduction to Deep Learning
Machine Translated
Було видно, що господар був пристрасним про представлених тем. Використовувалися цікаві приклади під час вправ.
ING Bank Śląski S.A.
Курси: Introduction to Deep Learning
Machine Translated
Широкий спектр питань охоплює і високі знання керівних.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Курси: Understanding Deep Neural Networks
Machine Translated
Відсутність
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Курси: Understanding Deep Neural Networks
Machine Translated
Високі теоретичні та практичні знання провідних. Провідні комунікаційні можливості. Під час курсу ви можете задати питання і отримати задовільну відповідь.
Kamil Kurek - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Курси: Understanding Deep Neural Networks
Machine Translated
Практична частина, де ми реалізували алгоритми. Це дозволило краще зрозуміти цю тему.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Курси: Understanding Deep Neural Networks
Machine Translated
Вправи і приклади
Paweł Orzechowski - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Курси: Understanding Deep Neural Networks
Machine Translated
Приклади та теми, які обговорювалися.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Курси: Understanding Deep Neural Networks
Machine Translated
Змістовні знання, заручини, захоплюючий спосіб спілкування знань. Практичні приклади після теоретичної лекції.
Janusz Chrobot - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Курси: Understanding Deep Neural Networks
Machine Translated
Практичні вправи, підготовлені паном Матьясом
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Курси: Understanding Deep Neural Networks
Machine Translated
Я отримав користь від пристрасті навчати і зосередитись на тому, щоб робити речі розумними.
Zaher Sharifi - GOSI
Курси: Advanced Deep Learning
Machine Translated
Ідентифікація людини і плати погана точка виявлення
王 春柱 - 中移物联网
Курси: Deep Learning for NLP (Natural Language Processing)
Machine Translated
Продемонструвати
中移物联网
Курси: Deep Learning for NLP (Natural Language Processing)
Machine Translated
Про зону обличчя.
中移物联网
Курси: Deep Learning for NLP (Natural Language Processing)
Machine Translated
Неформальні обміни ми мали під час лекцій дійсно допомогли мені поглибити своє розуміння предмета
Explore
Курси: Deep Reinforcement Learning with Python
Machine Translated
Багато практичних порад
Pawel Dawidowski - ABB Sp. z o.o.
Курси: Deep Learning with TensorFlow
Machine Translated
Багато інформації, пов'язаної з розгортаннями рішень
Michał Smolana - ABB Sp. z o.o.
Курси: Deep Learning with TensorFlow
Machine Translated
Безліч практичних порад і знань з широкого кола питань AI/It SQL //IOT.
ABB Sp. z o.o.
Курси: Deep Learning with TensorFlow
Machine Translated
багато інформації, всі запитані питання, цікаві приклади
A1 Telekom Austria AG
Курси: Deep Learning for Telecom (with Python)
Machine Translated
Я почав з близьких до нуля знань, і до кінця зміг створити та навчити власні мережі.
Huawei Technologies Duesseldorf GmbH
Курси: TensorFlow for Image Recognition
Machine Translated
Deep Learning Підкатегорії
Deep Learning Зміст курсу
Назва курсу
Тривалість
Огляд
Назва курсу
Тривалість
Огляд
21 годин
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 годин
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 годин
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 годин
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 годин
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 годин
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 годин
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 годин
This course covers AI (emphasizing Machine Learning and Deep Learning)
14 годин
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 годин
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 годин
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 годин
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 годин
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 годин
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 годин
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 годин
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 годин
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 годин
In this instructor-led, live training in україні, 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 годин
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 годин
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 годин
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 годин
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
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
28 годин
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
28 годин
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
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
21 годин
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
21 годин
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 годин
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 годин
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.
28 годин
In this instructor-led, live training in україні, 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.
14 годин
This instructor-led, live training in україні (online or onsite) is aimed at software engineers who wish to program in Python with OpenCV 4 for deep learning.
By the end of this training, participants will be able to:
- View, load, and classify images and videos using OpenCV 4.
- Implement deep learning in OpenCV 4 with TensorFlow and Keras.
- Run deep learning models and generate impactful reports from images and videos.
By the end of this training, participants will be able to:
- View, load, and classify images and videos using OpenCV 4.
- Implement deep learning in OpenCV 4 with TensorFlow and Keras.
- Run deep learning models and generate impactful reports from images and videos.
Last Updated:
Інші країни
Консалтинг
Online DL (Deep Learning) courses, Weekend Deep Learning courses, Evening Deep Learning (DL) training, Deep Learning boot camp, DL (Deep Learning) instructor-led, Weekend DL (Deep Learning) training, Evening Deep Learning courses, Deep Learning (DL) coaching, Deep Learning (DL) instructor, Deep Learning (DL) trainer, DL (Deep Learning) training courses, DL (Deep Learning) classes, Deep Learning (DL) on-site, Deep Learning (DL) private courses, Deep Learning (DL) one on one training
.jpg "international air transport association (iata)")