Cursurile de instruire la nivel local, instruite live Deep Learning (DL) demonstrează prin practica manuală fundamentele și aplicațiile Deep Learning și acoperă subiecte cum ar fi învățarea profundă a mașinilor, învățarea profund structurată și învățarea ierarhică Învățarea pentru formarea în profunzime este disponibilă ca "formare live la fața locului" sau "formare live la distanță" Training-ul live la fața locului poate fi efectuat la fața locului la sediul clientului România sau în centrele de formare corporativa NobleProg din România Instruirea live la distanță este realizată printr-un desktop interactiv, la distanță NobleProg Furnizorul dvs de formare locală.
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Mărturii
A fost foarte interactiv și mai relaxat și mai informal decât se aștepta. Am abordat o mulțime de subiecte în timp și antrenorul a fost întotdeauna receptiv să vorbească în detaliu sau mai general despre subiectele și despre modul în care erau legate. Mă simt că instruirea mi-a dat instrumentele necesare pentru a continua învățarea, spre deosebire de o sesiune în care învățarea se oprește odată ce ați terminat ceea ce este foarte important având în vedere amploarea și complexitatea subiectului.
Jonathan Blease
Curs: Artificial Neural Networks, Machine Learning, Deep Thinking
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Subiectul este foarte interesant.
Wojciech Baranowski
Curs: Introduction to Deep Learning
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Formatorii cunoștințele teoretice și disponibilitatea de a rezolva problemele cu participanții după formare.
Grzegorz Mianowski
Curs: Introduction to Deep Learning
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Subiect. Foarte interesant!.
Piotr
Curs: Introduction to Deep Learning
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Exercițiile după fiecare subiect au fost cu adevărat utile, în ciuda faptului că au fost prea complicate la sfârșit. În general, materialul prezentat a fost foarte interesant și implicat! Exercițiile cu recunoașterea imaginii au fost minunate.
Dolby Poland Sp. z o.o.
Curs: Introduction to Deep Learning
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Cred că dacă instruirea ar fi făcută în poloneză, ar permite instructorului să-și împărtășească cunoștințele mai eficient.
Radek
Curs: Introduction to Deep Learning
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Prezentarea globală a învățării profunde.
Bruno Charbonnier
Curs: Advanced Deep Learning
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Exercițiile sunt suficient de practice și nu au nevoie de cunoștințe ridicate în Python pentru a fi efectuate.
Alexandre GIRARD
Curs: Advanced Deep Learning
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Făcând exerciții pe exemple reale folosind Eras. Italia a înțeles total așteptările noastre despre acest antrenament.
Paul Kassis
Curs: Advanced Deep Learning
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Am apreciat cu adevărat răspunsurile clare ale lui Chris la întrebările noastre.
Léo Dubus
Curs: Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple
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În general, m-am bucurat de antrenorul informat.
Sridhar Voorakkara
Curs: Neural Networks Fundamentals using TensorFlow as Example
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Am fost uimit la standardul acestei clase - aș spune că era un standard universitar.
David Relihan
Curs: Neural Networks Fundamentals using TensorFlow as Example
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Foarte bine, o privire de ansamblu. Go - Go de fundal od în ce Tensorflow funcționează așa cum o face.
Kieran Conboy
Curs: Neural Networks Fundamentals using TensorFlow as Example
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Mi-au plăcut oportunitățile de a pune întrebări și de a explica mai detaliat teoria.
Sharon Ruane
Curs: Neural Networks Fundamentals using TensorFlow as Example
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Am obținut o mai bună înțelegere a subiectului. S-au făcut niște discuții frumoase cu niște subiecte reale din cadrul companiei noastre.
Sebastiaan Holman
Curs: Machine Learning and Deep Learning
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Instruirea a oferit fundamentul potrivit care ne permite să extindem în continuare, arătând modul în care teoria și practica merg mână în mână. De fapt m-a interesat mai mult subiectul decât eram înainte.
Jean-Paul van Tillo
Curs: Machine Learning and Deep Learning
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Mi-a plăcut foarte mult acoperirea și profunzimea subiectelor.
Anirban Basu
Curs: Machine Learning and Deep Learning
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Cunoașterea profundă a antrenorului cu privire la acest subiect.
Sebastian Görg
Curs: Introduction to Deep Learning
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Abordare foarte actualizată sau CPI (debit tensor, epoca, învățare) pentru a face învățarea în mașină.
Paul Lee
Curs: TensorFlow for Image Recognition
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Foarte flexibil.
Frank Ueltzhöffer
Curs: Artificial Neural Networks, Machine Learning and Deep Thinking
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M-am bucurat, în general, de flexibilitate.
Werner Philipp
Curs: Artificial Neural Networks, Machine Learning and Deep Thinking
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Având în vedere perspectivele tehnologiei: ce tehnologie / proces ar putea deveni mai important în viitor; vedeți ce poate fi folosit tehnologia.
Commerzbank AG
Curs: Neural Networks Fundamentals using TensorFlow as Example
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Am beneficiat de selecția subiectului. Stilul de antrenament. Practică orientare.
Commerzbank AG
Curs: Neural Networks Fundamentals using TensorFlow as Example
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modul de conducere și exemplul oferit de formator
ORANGE POLSKA S.A.
Curs: Machine Learning and Deep Learning
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Posibilitatea de a discuta despre problemele propuse
ORANGE POLSKA S.A.
Curs: Machine Learning and Deep Learning
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Comunicarea cu lectori
文欣 张
Curs: Artificial Neural Networks, Machine Learning, Deep Thinking
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Îmi place
lisa xie
Curs: Artificial Neural Networks, Machine Learning, Deep Thinking
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Acoperirea aprofundată a subiectelor de învățare a mașinilor, în special a rețelelor neuronale. Demistificat o mulțime de subiect.
Sacha Nandlall
Curs: Python for Advanced Machine Learning
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Cunoștințe mari și actuale despre exemple de aplicații practice și practice.
ING Bank Śląski S.A.
Curs: Introduction to Deep Learning
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O mulțime de exerciții, o colaborare foarte bună cu grupul.
Janusz Chrobot - ING Bank Śląski S.A.
Curs: Introduction to Deep Learning
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lucrează pe colaboratori,
ING Bank Śląski S.A.
Curs: Introduction to Deep Learning
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Era evident că entuziaștii subiecților prezentați au fost conducători. Folosite exemple interesante în timpul exercițiului.
ING Bank Śląski S.A.
Curs: Introduction to Deep Learning
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O gamă largă de subiecte acoperite și o cunoaștere substanțială a liderilor.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Curs: Understanding Deep Neural Networks
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lipsă
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Curs: Understanding Deep Neural Networks
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Mari cunoștințe teoretice și practice ale lectorilor. Comunicativitatea formatorilor. În timpul cursului, puteți pune întrebări și puteți obține răspunsuri satisfăcătoare.
Kamil Kurek - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Curs: Understanding Deep Neural Networks
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Partea practică, în care am implementat algoritmi. Acest lucru a permis o mai bună înțelegere a subiectului.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Curs: Understanding Deep Neural Networks
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exerciții și exemple implementate asupra lor
Paweł Orzechowski - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Curs: Understanding Deep Neural Networks
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Exemple și probleme discutate.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Curs: Understanding Deep Neural Networks
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Cunoștințe fundamentale, angajament, un pasionat mod de a transfera cunoștințe. Exemple practice după o prelegere teoretică.
Janusz Chrobot - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Curs: Understanding Deep Neural Networks
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Exerciții practice pregătite de domnul Maciej
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Curs: Understanding Deep Neural Networks
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Am beneficiat de pasiunea de a învăța și de a ne concentra pe a face lucrurile sensibile.
Zaher Sharifi - GOSI
Curs: Advanced Deep Learning
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Identificarea umană și circuitul de detectare a punctului de rău
王 春柱 - 中移物联网
Curs: Deep Learning for NLP (Natural Language Processing)
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Demonstra
中移物联网
Curs: Deep Learning for NLP (Natural Language Processing)
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Despre zona feței.
中移物联网
Curs: Deep Learning for NLP (Natural Language Processing)
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Schimburile informale am avut în timpul prelegeri într-adevăr ma ajutat să aprofundeze înțelegerea mea de subiect
Explore
Curs: Deep Reinforcement Learning with Python
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Multe sfaturi practice
Pawel Dawidowski - ABB Sp. z o.o.
Curs: Deep Learning with TensorFlow
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Multe informații legate de implementarea soluțiilor
Michał Smolana - ABB Sp. z o.o.
Curs: Deep Learning with TensorFlow
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O multitudine de sfaturi practice și cunoștințe de lector de la o gamă largă de probleme AI / IT / SQL / IoT.
ABB Sp. z o.o.
Curs: Deep Learning with TensorFlow
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multe informații, toate întrebările nelinistite, exemple interesante
A1 Telekom Austria AG
Curs: Deep Learning for Telecom (with Python)
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Am început cu cunoștințe aproape de zero și până la sfârșit am putut să-mi construiesc și să-mi instruiesc propriile rețele.
Huawei Technologies Duesseldorf GmbH
Curs: TensorFlow for Image Recognition
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Deep Learning (DL) Subcategories
DL (Deep Learning) Course Outlines
Numele cursului
Durata
Sinoptic
Numele cursului
Durata
Sinoptic
21 hours
Sinoptic
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
Sinoptic
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
Sinoptic
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
Sinoptic
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
Sinoptic
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
Sinoptic
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
Sinoptic
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
Sinoptic
This course covers AI (emphasizing Machine Learning and Deep Learning)
14 hours
Sinoptic
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
Sinoptic
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
Sinoptic
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
Sinoptic
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
Sinoptic
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
Sinoptic
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
Sinoptic
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
Sinoptic
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
Sinoptic
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
Sinoptic
In this instructor-led, live training in România, 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
Sinoptic
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
Sinoptic
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
Sinoptic
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
Sinoptic
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 hours
Sinoptic
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 hours
Sinoptic
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 hours
Sinoptic
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 hours
Sinoptic
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
Sinoptic
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
Sinoptic
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 hours
Sinoptic
In this instructor-led, live training in România, 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 hours
Sinoptic
This instructor-led, live training in România (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.
Upcoming Deep Learning Courses
23-08-2021 09:30
14 ore
01-09-2021 09:30
21 ore
10-09-2021 09:30
7 ore
14-09-2021 09:30
14 ore
28-09-2021 09:30
21 ore
Consultanță
Sfarsit de saptamana Deep Learning Cursuri, Seara Deep Learning Training, DL (Deep Learning) Camp, Deep Learning (DL) Cu instructor, Sfarsit de saptamana Deep Learning (DL) Training, Seara Deep Learning (DL) Cursuri, DL (Deep Learning) Coaching, Deep Learning (DL) Instructor, Deep Learning (DL) Trainer, Deep Learning Cursuri, DL (Deep Learning) Clase, Deep Learning (DL) Pe pagina, Deep Learning (DL) curs privat, Deep Learning one on one training
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