Local, instructorled live Deep Learning (DL) training courses demonstrate through handson practice the fundamentals and applications of Deep Learning and cover subjects such as deep machine learning, deep structured learning, and hierarchical learning
Deep Learning training is available as "onsite live training" or "remote live training" Onsite live training can be carried out locally on customer premises in Norge or in NobleProg corporate training centers in Norge Remote live training is carried out by way of an interactive, remote desktop
NobleProg Your Local Training Provider.
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Testimonials
Øvelsene er tilstrekkelig praktiske og trenger ikke høy kunnskap i Python .
Alexandre GIRARD
Kurs: Advanced Deep Learning
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Å gjøre øvelser på virkelige eksempler ved bruk av Keras . Mihaly forsto absolutt forventningene våre til denne treningen.
Paul Kassis
Kurs: Advanced Deep Learning
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Kunnskapsrik trener
Sridhar Voorakkara
Kurs: Neural Networks Fundamentals using TensorFlow as Example
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Jeg ble overrasket over standarden i denne klassen - jeg vil si at det var universitetsstandard.
David Relihan
Kurs: Neural Networks Fundamentals using TensorFlow as Example
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Veldig bra allround-oversikt. Go bakgrunnen for hvorfor Tensorflow fungerer som den gjør.
Kieran Conboy
Kurs: Neural Networks Fundamentals using TensorFlow as Example
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Jeg likte mulighetene til å stille spørsmål og få mer grundige forklaringer på teorien.
Sharon Ruane
Kurs: Neural Networks Fundamentals using TensorFlow as Example
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Vi har fått mye mer innsikt i fagstoffet. Noen fine diskusjoner ble ført med noen virkelige emner i selskapet vårt
Sebastiaan Holman
Kurs: Machine Learning and Deep Learning
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Opplæringen ga det rette grunnlaget som gjør at vi kan utvide videre, ved å vise hvordan teori og praksis går hånd i hånd. Det gjorde meg faktisk mer interessert i faget enn jeg var før.
Jean-Paul van Tillo
Kurs: Machine Learning and Deep Learning
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Dekning og dybde av emner
Anirban Basu
Kurs: Machine Learning and Deep Learning
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Trenerens dype kunnskap om emnet.
Sebastian Görg
Kurs: Introduction to Deep Learning
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Veldig oppdatert tilnærming eller api (tensorflow, kera, tflearn) for å gjøre maskinlæring
Paul Lee
Kurs: TensorFlow for Image Recognition
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Veldig fleksibel
Frank Ueltzhöffer
Kurs: Artificial Neural Networks, Machine Learning and Deep Thinking
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fleksibilitet
Werner Philipp
Kurs: Artificial Neural Networks, Machine Learning and Deep Thinking
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Gitt syn på teknologien: hvilken teknologi / prosess som kan bli viktigere i fremtiden; se, hva teknologien kan brukes til
Commerzbank AG
Kurs: Neural Networks Fundamentals using TensorFlow as Example
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Valg av emne. Treningsstil. Øv orientering
Commerzbank AG
Kurs: Neural Networks Fundamentals using TensorFlow as Example
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Inngående dekning av maskinlæringsemner, spesielt nevrale nettverk. Demystifiserte mye av emnet.
Sacha Nandlall
Kurs: Python for Advanced Machine Learning
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Jeg satte veldig pris på de krystallklare svarene til Chris på spørsmålene våre.
Léo Dubus
Kurs: Neural Networks Fundamentals using TensorFlow as Example
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måte å dirigere og eksempel gitt av trener
ORANGE POLSKA S.A.
Kurs: Machine Learning and Deep Learning
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Mulighet for å diskutere de foreslåtte problemene selv
ORANGE POLSKA S.A.
Kurs: Machine Learning and Deep Learning
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Kommunikasjon med foreleseren
文欣 张
Kurs: Artificial Neural Networks, Machine Learning, Deep Thinking
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Liker det
lisa xie
Kurs: Artificial Neural Networks, Machine Learning, Deep Thinking
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Stor og oppdatert kunnskap om ledende og praktiske anvendelseseksempler.
ING Bank Śląski S.A.
Kurs: Introduction to Deep Learning
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Mye øvelser, veldig godt samarbeid med gruppen.
Janusz Chrobot - ING Bank Śląski S.A.
Kurs: Introduction to Deep Learning
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arbeide med kolaboratorer,
ING Bank Śląski S.A.
Kurs: Introduction to Deep Learning
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Det var tydelig at ildsjelene for de presenterte emnene ledet. Brukte interessante eksempler mens du trente.
ING Bank Śląski S.A.
Kurs: Introduction to Deep Learning
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Et bredt spekter av temaer dekket og betydelig kunnskap fra lederne.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurs: Understanding Deep Neural Networks
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mangelen
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurs: Understanding Deep Neural Networks
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Stor teoretisk og praktisk kunnskap hos trenerne. Kommunikasjonsevne for trenere. I løpet av kurset kan du stille spørsmål og få tilfredsstillende svar.
Kamil Kurek - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurs: Understanding Deep Neural Networks
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Praktisk del, der vi implementerte algoritmer. Dette muliggjorde en bedre forståelse av emnet.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurs: Understanding Deep Neural Networks
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øvelser og eksempler implementert på dem
Paweł Orzechowski - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurs: Understanding Deep Neural Networks
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Eksempler og spørsmål diskutert.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurs: Understanding Deep Neural Networks
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Substant kunnskap, engasjement, en lidenskapelig måte å formidle kunnskap på. Praktiske eksempler etter et teoretisk forelesning.
Janusz Chrobot - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurs: Understanding Deep Neural Networks
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Praktiske øvelser utarbeidet av Mr. Maciej
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurs: Understanding Deep Neural Networks
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Jeg hadde godt av lidenskapen til å undervise og fokusere på å gjøre ting fornuftig.
Zaher Sharifi - GOSI
Kurs: Advanced Deep Learning
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Deep Learning (DL) Underkategorier
Deep Learning Kursplaner
Kursnavn
Varighet
Oversikt
Kursnavn
Varighet
Oversikt
14 timer
Oversikt
Dette kurset dekker AI (med vekt på Machine Learning og Deep Learning ) i Automotive . Det hjelper til med å bestemme hvilken teknologi som (potensielt) kan brukes i flere situasjoner i en bil: fra enkel automatisering, bildegjenkjenning til autonome beslutninger.
14 timer
Oversikt
TensorFlow.js is a JavaScript framework for machine learning. TensorFlow.js enables users to build and train machine learning models directly in JavaScript.
This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use TensorFlow.js to identify patterns and generate predictions through machine learning models.
By the end of this training, participants will be able to:
- Build and train machine learning models with TensorFlow.js.
- Run machine learning models in the browser or under Node.js.
- Retrain pre-existing machine learning models using custom data.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use TensorFlow.js to identify patterns and generate predictions through machine learning models.
By the end of this training, participants will be able to:
- Build and train machine learning models with TensorFlow.js.
- Run machine learning models in the browser or under Node.js.
- Retrain pre-existing machine learning models using custom data.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
14 timer
Oversikt
Denne klasseromsbaserte treningsøkten vil inneholde presentasjoner og databaserte eksempler og case study-øvelser for å gjennomføre med relevante nevrale og dype nettverksbiblioteker.
14 timer
Oversikt
OpenCV is a library of programming functions for deciphering images with computer algorithms. OpenCV 4 is the latest OpenCV release and it provides optimized modularity, updated algorithms, and more. With OpenCV 4 and Python, users will be able to view, load, and classify images and videos for advanced image recognition.
This instructor-led, live training (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.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
This instructor-led, live training (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.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
14 timer
Oversikt
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
7 timer
Oversikt
I denne instruktørledede, OpenNMT , vil deltakerne lære å sette opp og bruke OpenNMT til å utføre oversettelse av forskjellige eksempler av datasett. Kurset starter med en oversikt over nevrale nettverk slik de gjelder for maskinoversettelse. Deltakerne vil gjennomføre liveøvelser gjennom hele kurset for å demonstrere sin forståelse av konseptene som er lært og få tilbakemeldinger fra instruktøren.
Ved slutten av denne opplæringen vil deltakerne ha kunnskap og praksis som trengs for å implementere en live OpenNMT løsning.
Kildespråk og målspråkprøver blir forhåndsarrangert i henhold til publikums krav.
Kursets format
- Delforedrag, deldiskusjon, tung praktisk praksis
Ved slutten av denne opplæringen vil deltakerne ha kunnskap og praksis som trengs for å implementere en live OpenNMT løsning.
Kildespråk og målspråkprøver blir forhåndsarrangert i henhold til publikums krav.
Kursets format
- Delforedrag, deldiskusjon, tung praktisk praksis
14 timer
Oversikt
I denne instruktørledede, OpenNN , går vi over prinsippene i nevrale nettverk og bruker OpenNN til å implementere et eksempelapplikasjon.
Kursets format
- Foredrag og diskusjon kombinert med praktiske øvelser.
Kursets format
- Foredrag og diskusjon kombinert med praktiske øvelser.
21 timer
Oversikt
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
21 timer
Oversikt
In this instructor-led, live training, participants will learn the most relevant and cutting-edge machine learning techniques in Python as they build a series of demo applications involving image, music, text, and financial data.
By the end of this training, participants will be able to:
- Implement machine learning algorithms and techniques for solving complex problems.
- Apply deep learning and semi-supervised learning to applications involving image, music, text, and financial data.
- Push Python algorithms to their maximum potential.
- Use libraries and packages such as NumPy and Theano.
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
By the end of this training, participants will be able to:
- Implement machine learning algorithms and techniques for solving complex problems.
- Apply deep learning and semi-supervised learning to applications involving image, music, text, and financial data.
- Push Python algorithms to their maximum potential.
- Use libraries and packages such as NumPy and Theano.
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 timer
Oversikt
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.
Audience
- Developers
- Analysts
- Data scientists
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:
- 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.
Audience
- Developers
- Analysts
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 timer
Oversikt
Deep learning is a subfield of machine learning. It uses methods based on learning data representations and structures such as neural networks.
Keras is a high-level neural networks API for fast development and experimentation. It runs on top of TensorFlow, CNTK, or Theano.
This instructor-led, live training (online or onsite) is aimed at developers who wish to build a self-driving car (autonomous vehicle) using deep learning techniques.
By the end of this training, participants will be able to:
- Use computer vision techniques to identify lanes.
- Use Keras to build and train convolutional neural networks.
- Train a deep learning model to differentiate traffic signs.
- Simulate a fully autonomous car.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Keras is a high-level neural networks API for fast development and experimentation. It runs on top of TensorFlow, CNTK, or Theano.
This instructor-led, live training (online or onsite) is aimed at developers who wish to build a self-driving car (autonomous vehicle) using deep learning techniques.
By the end of this training, participants will be able to:
- Use computer vision techniques to identify lanes.
- Use Keras to build and train convolutional neural networks.
- Train a deep learning model to differentiate traffic signs.
- Simulate a fully autonomous car.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
21 timer
Oversikt
SINGA er en generelt distribuert plattform for dyp læring for å trene store modeller for dyp læring over store datasett. Den er designet med en intuitiv programmeringsmodell basert på lagets abstraksjon. En rekke populære dype læringsmodeller støttes, nemlig fremover-modeller inkludert konvolusjonelle nevrale nettverk (CNN), energimodeller som begrenset Boltzmann-maskin (RBM) og tilbakevendende nevrale nettverk (RNN). Mange innebygde lag er tilgjengelig for brukere. SINGA-arkitekturen er tilstrekkelig fleksibel til å kjøre synkrone, asynkrone og hybrid treningsrammer. SINGA støtter også forskjellige nevrale nettoppdelingsordninger for å parallellisere opplæringen av store modeller, nemlig partisjonering på batchdimensjon, funksjonsdimensjon eller hybrid partisjonering.
Publikum
Dette kurset er rettet mot forskere, ingeniører og utviklere som søker å bruke Apache SINGA som en dyp læringsramme.
Etter fullført kurs vil delegatene:
- forstå SINGAs struktur og distribusjonsmekanismer
- kunne utføre installasjons- / produksjonsmiljø / arkitekturoppgaver og konfigurasjon
- kunne vurdere kodekvalitet, utføre feilsøking, overvåking
- kunne implementere avansert produksjon som treningsmodeller, innebygd vilkår, bygge grafer og logging
Publikum
Dette kurset er rettet mot forskere, ingeniører og utviklere som søker å bruke Apache SINGA som en dyp læringsramme.
Etter fullført kurs vil delegatene:
- forstå SINGAs struktur og distribusjonsmekanismer
- kunne utføre installasjons- / produksjonsmiljø / arkitekturoppgaver og konfigurasjon
- kunne vurdere kodekvalitet, utføre feilsøking, overvåking
- kunne implementere avansert produksjon som treningsmodeller, innebygd vilkår, bygge grafer og logging
7 timer
Oversikt
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
21 timer
Oversikt
TensorFlow er et populært bibliotek og maskinlæringsbibliotek utviklet av Go ogle for dyp læring, numerisk beregning og storskala maskinlæring. TensorFlow 2.0, utgitt i januar 2019, er den nyeste versjonen av TensorFlow og inkluderer forbedringer i ivrig utførelse, kompatibilitet og API-konsistens.
Denne instruktørledede, liveopplæringen (på stedet eller fjernkontrollen) er rettet mot utviklere og dataforskere som ønsker å bruke Tensorflow 2.0 til å bygge prediktorer, klassifisere, generative modeller, nevrale nettverk og så videre.
Ved slutten av denne opplæringen vil deltakerne kunne:
- Installer og konfigurer TensorFlow 2.0.
- Forstå fordelene med TensorFlow 2.0 i forhold til tidligere versjoner.
- Bygg dype læringsmodeller.
- Implementere en avansert bildeklassifiserer.
- Distribuer en dyp læringsmodell til sky-, mobil- og IoT-enheter.
Kursets format
- Interaktiv forelesning og diskusjon.
- Masse øvelser og trening.
- Praktisk implementering i et live-lab-miljø.
Alternativer for tilpasning av kurset
- For å be om en tilpasset opplæring for dette kurset, vennligst kontakt oss for å avtale.
- Hvis du vil lære mer om TensorFlow , kan du gå til: https://www.tensorflow.org/
Denne instruktørledede, liveopplæringen (på stedet eller fjernkontrollen) er rettet mot utviklere og dataforskere som ønsker å bruke Tensorflow 2.0 til å bygge prediktorer, klassifisere, generative modeller, nevrale nettverk og så videre.
Ved slutten av denne opplæringen vil deltakerne kunne:
- Installer og konfigurer TensorFlow 2.0.
- Forstå fordelene med TensorFlow 2.0 i forhold til tidligere versjoner.
- Bygg dype læringsmodeller.
- Implementere en avansert bildeklassifiserer.
- Distribuer en dyp læringsmodell til sky-, mobil- og IoT-enheter.
Kursets format
- Interaktiv forelesning og diskusjon.
- Masse øvelser og trening.
- Praktisk implementering i et live-lab-miljø.
Alternativer for tilpasning av kurset
- For å be om en tilpasset opplæring for dette kurset, vennligst kontakt oss for å avtale.
- Hvis du vil lære mer om TensorFlow , kan du gå til: https://www.tensorflow.org/
21 timer
Oversikt
TensorFlow Lite is an open source deep learning framework for executing models on mobile and embedded devices with limited compute and memory resources.
This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to deploy deep learning models on embedded devices.
By the end of this training, participants will be able to:
- Install and configure Tensorflow Lite on an embedded device.
- Understand the concepts and components underlying TensorFlow Lite.
- Convert existing machine learning models to TensorFlow Lite format for execution on embedded devices.
- Work within the limitations of small devices and TensorFlow Lite, while learning how to expand their default capabilities.
- Deploy deep learning models on embedded devices running Linux to solve physical world problems such as recognizing images and voice, predicting patterns, and initiating movements and responses from robots and other embedded systems in the field.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to deploy deep learning models on embedded devices.
By the end of this training, participants will be able to:
- Install and configure Tensorflow Lite on an embedded device.
- Understand the concepts and components underlying TensorFlow Lite.
- Convert existing machine learning models to TensorFlow Lite format for execution on embedded devices.
- Work within the limitations of small devices and TensorFlow Lite, while learning how to expand their default capabilities.
- Deploy deep learning models on embedded devices running Linux to solve physical world problems such as recognizing images and voice, predicting patterns, and initiating movements and responses from robots and other embedded systems in the field.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
21 timer
Oversikt
MXNet is a flexible, open-source Deep Learning library that is popular for research prototyping and production. Together with the high-level Gluon API interface, Apache MXNet is a powerful alternative to TensorFlow and PyTorch.
This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use Apache MXNet to build and deploy a deep learning model for image recognition.
By the end of this training, participants will be able to:
- Install and configure Apache MXNet and its components.
- Understand MXNet's architecture and data structures.
- Use Apache MXNet's low-level and high-level APIs to efficiently build neural networks.
- Build a convolutional neural network for image classification.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use Apache MXNet to build and deploy a deep learning model for image recognition.
By the end of this training, participants will be able to:
- Install and configure Apache MXNet and its components.
- Understand MXNet's architecture and data structures.
- Use Apache MXNet's low-level and high-level APIs to efficiently build neural networks.
- Build a convolutional neural network for image classification.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
21 timer
Oversikt
TensorFlow Lite is an open source deep learning framework for mobile devices and embedded systems.
This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop mobile applications with deep learning capabilities.
By the end of this training, participants will be able to:
- Install and configure TensorFlow Lite.
- Understand the principles behind TensorFlow, machine learning and deep learning.
- Load TensorFlow Models onto an Android device.
- Enable deep learning and machine learning functionality such as computer vision and natural language recognition in a mobile application.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
- To learn more about TensorFlow, please visit: https://www.tensorflow.org/lite/
This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop mobile applications with deep learning capabilities.
By the end of this training, participants will be able to:
- Install and configure TensorFlow Lite.
- Understand the principles behind TensorFlow, machine learning and deep learning.
- Load TensorFlow Models onto an Android device.
- Enable deep learning and machine learning functionality such as computer vision and natural language recognition in a mobile application.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
- To learn more about TensorFlow, please visit: https://www.tensorflow.org/lite/
21 timer
Oversikt
TensorFlow Lite is an open source deep learning framework for mobile devices and embedded systems.
This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop iOS mobile applications with deep learning capabilities.
By the end of this training, participants will be able to:
- Install and configure TensorFlow Lite.
- Understand the principles behind TensorFlow and machine learning on mobile devices.
- Load TensorFlow Models onto an iOS device.
- Run an iOS application capable of detecting and classifying an object captured through the device's camera.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop iOS mobile applications with deep learning capabilities.
By the end of this training, participants will be able to:
- Install and configure TensorFlow Lite.
- Understand the principles behind TensorFlow and machine learning on mobile devices.
- Load TensorFlow Models onto an iOS device.
- Run an iOS application capable of detecting and classifying an object captured through the device's camera.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
21 timer
Oversikt
TensorFlow Lite for Microcontrollers is a port of TensorFlow Lite designed to run machine learning models on microcontrollers and other devices with limited memory.
This instructor-led, live training (online or onsite) is aimed at engineers who wish to write, load and run machine learning models on very small embedded devices.
By the end of this training, participants will be able to:
- Install TensorFlow Lite.
- Load machine learning models onto an embedded device to enable it to detect speech, classify images, etc.
- Add AI to hardware devices without relying on network connectivity.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
This instructor-led, live training (online or onsite) is aimed at engineers who wish to write, load and run machine learning models on very small embedded devices.
By the end of this training, participants will be able to:
- Install TensorFlow Lite.
- Load machine learning models onto an embedded device to enable it to detect speech, classify images, etc.
- Add AI to hardware devices without relying on network connectivity.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
7 timer
Oversikt
TensorFlow Serving er et system for servering av maskinlæring (ML) modeller til produksjon.
I denne instruktørledede TensorFlow vil deltakerne lære å konfigurere og bruke TensorFlow Serving til å distribuere og administrere ML-modeller i et produksjonsmiljø.
Ved slutten av denne opplæringen vil deltakerne kunne:
- Tren, eksporter og server forskjellige TensorFlow modeller
- Test og distribuer algoritmer ved hjelp av en enkelt arkitektur og sett med APIer
- Utvid TensorFlow Servering for å tjene andre typer modeller utover TensorFlow modeller
Publikum
- Utviklere
- Data forskere
Kursets format
- Delforelesning, deldiskusjon, øvelser og tung praktisk øvelse
I denne instruktørledede TensorFlow vil deltakerne lære å konfigurere og bruke TensorFlow Serving til å distribuere og administrere ML-modeller i et produksjonsmiljø.
Ved slutten av denne opplæringen vil deltakerne kunne:
- Tren, eksporter og server forskjellige TensorFlow modeller
- Test og distribuer algoritmer ved hjelp av en enkelt arkitektur og sett med APIer
- Utvid TensorFlow Servering for å tjene andre typer modeller utover TensorFlow modeller
Publikum
- Utviklere
- Data forskere
Kursets format
- Delforelesning, deldiskusjon, øvelser og tung praktisk øvelse
21 timer
Oversikt
TensorFlow er et 2. generasjons API for Go ogles open source programvarebibliotek for Deep Learning . Systemet er designet for å lette forskning i maskinlæring, og for å gjøre det raskt og enkelt å overføre fra forskningsprototype til produksjonssystem.
Publikum
Dette kurset er ment for ingeniører som søker å bruke TensorFlow til Deep Learning prosjektene sine
Etter fullført kurs vil delegatene:
- forstå TensorFlow struktur og distribusjonsmekanismer
- kunne utføre installasjons- / produksjonsmiljø / arkitekturoppgaver og konfigurasjon
- kunne vurdere kodekvalitet, utføre feilsøking, overvåking
- kunne implementere avansert produksjon som treningsmodeller, bygge grafer og logging
Publikum
Dette kurset er ment for ingeniører som søker å bruke TensorFlow til Deep Learning prosjektene sine
Etter fullført kurs vil delegatene:
- forstå TensorFlow struktur og distribusjonsmekanismer
- kunne utføre installasjons- / produksjonsmiljø / arkitekturoppgaver og konfigurasjon
- kunne vurdere kodekvalitet, utføre feilsøking, overvåking
- kunne implementere avansert produksjon som treningsmodeller, bygge grafer og logging
28 timer
Oversikt
Dette kurset utforsker, med spesifikke eksempler, anvendelsen av Tensor Flow til formålet med gjenkjenning
Publikum
Dette kurset er beregnet på ingeniører som søker å bruke TensorFlow til bildegjenkjenning
Etter fullført kurs vil delegatene kunne:
- forstå TensorFlow struktur og distribusjonsmekanismer
- utføre installasjons- / produksjonsmiljø / arkitekturoppgaver og konfigurasjon
- vurdere kodekvalitet, utfør feilsøking, overvåking
- implementere avansert produksjon som treningsmodeller, bygge grafer og logging
Publikum
Dette kurset er beregnet på ingeniører som søker å bruke TensorFlow til bildegjenkjenning
Etter fullført kurs vil delegatene kunne:
- forstå TensorFlow struktur og distribusjonsmekanismer
- utføre installasjons- / produksjonsmiljø / arkitekturoppgaver og konfigurasjon
- vurdere kodekvalitet, utfør feilsøking, overvåking
- implementere avansert produksjon som treningsmodeller, bygge grafer og logging
21 timer
Oversikt
TensorFlow Extended (TFX) is an end-to-end platform for deploying production ML pipelines.
This instructor-led, live training (online or onsite) is aimed at data scientists who wish to go from training a single ML model to deploying many ML models to production.
By the end of this training, participants will be able to:
- Install and configure TFX and supporting third-party tools.
- Use TFX to create and manage a complete ML production pipeline.
- Work with TFX components to carry out modeling, training, serving inference, and managing deployments.
- Deploy machine learning features to web applications, mobile applications, IoT devices and more.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
This instructor-led, live training (online or onsite) is aimed at data scientists who wish to go from training a single ML model to deploying many ML models to production.
By the end of this training, participants will be able to:
- Install and configure TFX and supporting third-party tools.
- Use TFX to create and manage a complete ML production pipeline.
- Work with TFX components to carry out modeling, training, serving inference, and managing deployments.
- Deploy machine learning features to web applications, mobile applications, IoT devices and more.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
21 timer
Oversikt
Torch er et bibliotek med åpen kildekode og et vitenskapelig datarammeverk basert på programmeringsspråket Lua . Det gir et utviklingsmiljø for numerikk, maskinlæring og datasyn, med særlig vekt på dyp læring og sammenvevingsnett. Det er et av de raskeste og mest fleksible rammene for Machine and Deep Learning og brukes av selskaper som Facebook , Go ogle, Twitter, NVIDIA, AMD, Intel og mange andre.
I denne instruktørledede liveopplæringen dekker vi prinsippene for Torch , dens unike funksjoner og hvordan den kan brukes i virkelige applikasjoner. Vi går gjennom en rekke praktiske øvelser gjennom hele tiden, demonstrerer og praktiserer de innlærte konseptene.
Mot slutten av kurset vil deltakerne ha en grundig forståelse av Torch underliggende funksjoner og evner, så vel som dens rolle og bidrag innenfor AI-rommet sammenlignet med andre rammer og biblioteker. Deltakerne vil også ha fått nødvendig praksis for å implementere Torch i sine egne prosjekter.
Kursets format
- Oversikt over maskin og Deep Learning
- Kodings- og integrasjonsøvelser i klassen
- Testspørsmål strødd underveis for å sjekke forståelse
I denne instruktørledede liveopplæringen dekker vi prinsippene for Torch , dens unike funksjoner og hvordan den kan brukes i virkelige applikasjoner. Vi går gjennom en rekke praktiske øvelser gjennom hele tiden, demonstrerer og praktiserer de innlærte konseptene.
Mot slutten av kurset vil deltakerne ha en grundig forståelse av Torch underliggende funksjoner og evner, så vel som dens rolle og bidrag innenfor AI-rommet sammenlignet med andre rammer og biblioteker. Deltakerne vil også ha fått nødvendig praksis for å implementere Torch i sine egne prosjekter.
Kursets format
- Oversikt over maskin og Deep Learning
- Kodings- og integrasjonsøvelser i klassen
- Testspørsmål strødd underveis for å sjekke forståelse
7 timer
Oversikt
The Tensor Processing Unit (TPU) is the architecture which Google has used internally for several years, and is just now becoming available for use by the general public. It includes several optimizations specifically for use in neural networks, including streamlined matrix multiplication, and 8-bit integers instead of 16-bit in order to return appropriate levels of precision.
In this instructor-led, live training, participants will learn how to take advantage of the innovations in TPU processors to maximize the performance of their own AI applications.
By the end of the training, participants will be able to:
- Train various types of neural networks on large amounts of data.
- Use TPUs to speed up the inference process by up to two orders of magnitude.
- Utilize TPUs to process intensive applications such as image search, cloud vision and photos.
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to take advantage of the innovations in TPU processors to maximize the performance of their own AI applications.
By the end of the training, participants will be able to:
- Train various types of neural networks on large amounts of data.
- Use TPUs to speed up the inference process by up to two orders of magnitude.
- Utilize TPUs to process intensive applications such as image search, cloud vision and photos.
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
35 timer
Oversikt
TensorFlow™ is an open source software library for numerical computation using data flow graphs.
SyntaxNet is a neural-network Natural Language Processing framework for TensorFlow.
Word2Vec is used for learning vector representations of words, called "word embeddings". Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (Chapter 3.1 and 3.2 in Mikolov et al.).
Used in tandem, SyntaxNet and Word2Vec allows users to generate Learned Embedding models from Natural Language input.
Audience
This course is targeted at Developers and engineers who intend to work with SyntaxNet and Word2Vec models in their TensorFlow graphs.
After completing this course, delegates will:
- understand TensorFlow’s structure and deployment mechanisms
- be able to carry out installation / production environment / architecture tasks and configuration
- be able to assess code quality, perform debugging, monitoring
- be able to implement advanced production like training models, embedding terms, building graphs and logging
SyntaxNet is a neural-network Natural Language Processing framework for TensorFlow.
Word2Vec is used for learning vector representations of words, called "word embeddings". Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (Chapter 3.1 and 3.2 in Mikolov et al.).
Used in tandem, SyntaxNet and Word2Vec allows users to generate Learned Embedding models from Natural Language input.
Audience
This course is targeted at Developers and engineers who intend to work with SyntaxNet and Word2Vec models in their TensorFlow graphs.
After completing this course, delegates will:
- understand TensorFlow’s structure and deployment mechanisms
- be able to carry out installation / production environment / architecture tasks and configuration
- be able to assess code quality, perform debugging, monitoring
- be able to implement advanced production like training models, embedding terms, building graphs and logging
35 timer
Oversikt
Dette kurset begynner med å gi deg konseptuell kunnskap i nevrale nettverk og generelt i maskinlæringsalgoritme, dyp læring (algoritmer og applikasjoner).
Del-1 (40%) av denne opplæringen er mer fokus på grunnleggende, men vil hjelpe deg å velge riktig teknologi: TensorFlow , Caffe , Theano, DeepDrive, Keras , etc.
Del-2 (20%) av denne opplæringen introduserer Theano - et pytonbibliotek som gjør det enkelt å skrive dype læringsmodeller.
Del 3 (40%) av opplæringen vil være omfattende basert på Tensorflow - 2nd Generation API of Go ogles open source programvarebibliotek for Deep Learning . Eksemplene og håndsonen ville alle være laget i TensorFlow .
Publikum
Dette kurset er ment for ingeniører som søker å bruke TensorFlow til Deep Learning prosjektene sine
Etter fullført kurs vil delegatene:
-
ha god forståelse for dype nevrale nettverk (DNN), CNN og RNN
-
forstå TensorFlow struktur og distribusjonsmekanismer
-
kunne utføre installasjons- / produksjonsmiljø / arkitekturoppgaver og konfigurasjon
-
kunne vurdere kodekvalitet, utføre feilsøking, overvåking
-
kunne implementere avansert produksjon som treningsmodeller, bygge grafer og logging
Del-1 (40%) av denne opplæringen er mer fokus på grunnleggende, men vil hjelpe deg å velge riktig teknologi: TensorFlow , Caffe , Theano, DeepDrive, Keras , etc.
Del-2 (20%) av denne opplæringen introduserer Theano - et pytonbibliotek som gjør det enkelt å skrive dype læringsmodeller.
Del 3 (40%) av opplæringen vil være omfattende basert på Tensorflow - 2nd Generation API of Go ogles open source programvarebibliotek for Deep Learning . Eksemplene og håndsonen ville alle være laget i TensorFlow .
Publikum
Dette kurset er ment for ingeniører som søker å bruke TensorFlow til Deep Learning prosjektene sine
Etter fullført kurs vil delegatene:
-
ha god forståelse for dype nevrale nettverk (DNN), CNN og RNN
-
forstå TensorFlow struktur og distribusjonsmekanismer
-
kunne utføre installasjons- / produksjonsmiljø / arkitekturoppgaver og konfigurasjon
-
kunne vurdere kodekvalitet, utføre feilsøking, overvåking
-
kunne implementere avansert produksjon som treningsmodeller, bygge grafer og logging
14 timer
Oversikt
Video analytics refers to the technology and techniques used to process a video stream. A common application would be capturing and identifying live video events through motion detection, facial recognition, crowd and vehicle counting, etc.
This instructor-led, live training (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.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
This instructor-led, live training (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.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
28 timer
Oversikt
Dette kurset vil gi deg kunnskap i nevrale nettverk og generelt i maskinlæringsalgoritme, dyp læring (algoritmer og applikasjoner).
Denne opplæringen er mer fokus på grunnleggende, men vil hjelpe deg å velge riktig teknologi: TensorFlow , Caffe , Teano, DeepDrive, Keras , etc. Eksemplene er laget i TensorFlow .
Denne opplæringen er mer fokus på grunnleggende, men vil hjelpe deg å velge riktig teknologi: TensorFlow , Caffe , Teano, DeepDrive, Keras , etc. Eksemplene er laget i TensorFlow .
21 timer
Oversikt
Dette kurset dekker AI (med vekt på Machine Learning og Deep Learning )
Rådgivning
Helg Deep Learning kurs, kveld DL (Deep Learning) trening, Deep Learning boot camp, Deep Learning instruktørledet, Helg DL (Deep Learning) trening, Kveld Deep Learning kurs, DL (Deep Learning) coaching, Deep Learning (DL) instruktør, DL (Deep Learning) trener, Deep Learning (DL) kurs, Deep Learning klasser, Deep Learning (DL) on-site, DL (Deep Learning) private kurs, Deep Learning tomannshånd trening
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