Lokala instruktörsutbildningar för levande neurala nätverk visar genom interaktiva diskussioner och handson-övningar hur man konstruerar neurala nätverk med hjälp av ett antal främst opensource-verktyg och bibliotek samt hur man utnyttjar kraften i avancerad maskinvara (GPU) och optimeringstekniker som innefattar distribuerad databehandling och stora data Våra neurala nätverkskurser bygger på populära programmeringsspråk som Python, Java, R-språk och kraftfulla bibliotek, inklusive TensorFlow, Torch, Caffe, Theano och mer Våra neurala nätverkskurser omfattar både teori och implementering med hjälp av ett antal neurala nätverk implementeringar som Deep Neural Networks (DNN), Convolutional Neural Networks (CNN) och Recurrent Neural Networks (RNN) Neural Network Training är tillgänglig som "live training" eller "remote live training" Utbildning på plats kan genomföras lokalt på kundlokaler i Sverige eller i NobleProgs företagsutbildningscenter i Sverige Fjärrutbildning sker genom en interaktiv fjärrskrivbord NobleProg Din lokala utbildningsleverantör.
Machine Translated
Vittnesmål
Det var mycket interaktivt och mer avslappnat och informellt än förväntat. Vi omfattade många ämnen på tiden och tränaren var alltid mottaglig för att prata mer i detalj eller mer allmänt om ämnena och hur de var relaterade. Jag tycker att träningen har gett mig verktygen för att fortsätta att lära sig i motsats till att det är en engångsperiod där lärandet slutar när du är klar vilket är mycket viktigt med tanke på ämnets skala och komplexitet.
Jonathan Blease
Kurs: Artificial Neural Networks, Machine Learning, Deep Thinking
Machine Translated
Ann skapade en bra miljö för att ställa frågor och lära sig. Vi hade mycket roligt och lärde oss också mycket på samma gång.
Gudrun Bickelq
Kurs: Introduction to the use of neural networks
Machine Translated
Den interaktiva delen, skräddarsydd för våra specifika behov.
Thomas Stocker
Kurs: Introduction to the use of neural networks
Machine Translated
Jag uppskattade verkligen kristallklara svaren på Chris till våra frågor.
Léo Dubus
Kurs: Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple
Machine Translated
Jag har i allmänhet haft den kunniga tränaren.
Sridhar Voorakkara
Kurs: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Jag var förvånad över standarden på den här klassen - jag skulle säga att det var universitetsstandard.
David Relihan
Kurs: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Mycket bra översikt över hela världen. Go bakom bakgrund till varför Tensorflow fungerar som det gör.
Kieran Conboy
Kurs: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Jag gillade möjligheterna att ställa frågor och få djupare förklaringar av teorin.
Sharon Ruane
Kurs: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Tränaren förklarade mycket enkelt svåra och avancerade ämnen.
Leszek K
Kurs: Artificial Intelligence Overview
Machine Translated
Jag gillade de nya insikterna i djup maskininlärning.
Josip Arneric
Kurs: Neural Network in R
Machine Translated
Vi fick lite kunskap om NN i allmänhet, och det som var mest intressant för mig var de nya typerna av NN som är populära idag.
Tea Poklepovic
Kurs: Neural Network in R
Machine Translated
Jag tyckte mest om graferna i R :))).
Faculty of Economics and Business Zagreb
Kurs: Neural Network in R
Machine Translated
Mycket flexibel.
Frank Ueltzhöffer
Kurs: Artificial Neural Networks, Machine Learning and Deep Thinking
Machine Translated
Jag har i allmänhet haft flexibilitet.
Werner Philipp
Kurs: Artificial Neural Networks, Machine Learning and Deep Thinking
Machine Translated
Med tanke på tekniken: vilken teknik / process kan bli viktigare i framtiden; se vad tekniken kan användas till.
Commerzbank AG
Kurs: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Jag fick nytta av ämnesval. Stil av träning. Övningsorientering.
Commerzbank AG
Kurs: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Kommunikation med föreläsare
文欣 张
Kurs: Artificial Neural Networks, Machine Learning, Deep Thinking
Machine Translated
Gilla det
lisa xie
Kurs: Artificial Neural Networks, Machine Learning, Deep Thinking
Machine Translated
många övningar som jag direkt kan använda i mitt arbete.
Alior Bank S.A.
Kurs: Sieci Neuronowe w R
Machine Translated
Exempel på reella data.
Alior Bank S.A.
Kurs: Sieci Neuronowe w R
Machine Translated
neuralnet, pROC i en slinga.
Alior Bank S.A.
Kurs: Sieci Neuronowe w R
Machine Translated
Ett brett spektrum av ämnen och omfattande kunskaper om ledarna.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurs: Understanding Deep Neural Networks
Machine Translated
brist
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurs: Understanding Deep Neural Networks
Machine Translated
Stor teoretisk och praktisk kunskap om lärarna. Kommunikativitet av tränare. Under kursen kan du ställa frågor och få tillfredsställande svar.
Kamil Kurek - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurs: Understanding Deep Neural Networks
Machine Translated
Praktisk del, där vi implementerade algoritmer. Detta möjliggjorde en bättre förståelse av ämnet.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurs: Understanding Deep Neural Networks
Machine Translated
övningar och exempel genomförda på dem
Paweł Orzechowski - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurs: Understanding Deep Neural Networks
Machine Translated
Exempel och problem diskuterade.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurs: Understanding Deep Neural Networks
Machine Translated
Väsentlig kunskap, engagemang, ett passionerat sätt att överföra kunskap. Praktiska exempel efter en teoretisk föreläsning.
Janusz Chrobot - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurs: Understanding Deep Neural Networks
Machine Translated
Praktiska övningar utarbetade av Mr Maciej
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurs: Understanding Deep Neural Networks
Machine Translated
De informella utbyten vi hade under föreläsningarna hjälpte mig verkligen att fördjupa min förståelse av ämnet
Explore
Kurs: Deep Reinforcement Learning with Python
Machine Translated
Tränaren var en professionell inom ämnesområdet och relaterad teori med tillämpning utmärkt
Fahad Malalla - Tatweer Petroleum
Kurs: Applied AI from Scratch in Python
Machine Translated
teman, vänlig attityd presentatören
Fujitsu Technology Solutions Sp. z o.o.
Kurs: Artificial Intelligence Overview
Machine Translated
Neural Networks underkategorier
Neural Networks läroplaner
Kurs Namn
Varaktighet
Översikt
Kurs Namn
Varaktighet
Översikt
7 timmar
Översikt
This course has been created for managers, solutions architects, innovation officers, CTOs, software architects and anyone who is interested in an overview of applied artificial intelligence and the nearest forecast for its development.
7 timmar
Översikt
The training is aimed at people who want to learn the basics of neural networks and their applications.
14 timmar
Översikt
This course is an introduction to applying neural networks in real world problems using R-project software.
14 timmar
Översikt
This training course is for people that would like to apply Machine Learning in practical applications.
Audience
This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization.
The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work.
Sector specific examples are used to make the training relevant to the audience.
Audience
This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization.
The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work.
Sector specific examples are used to make the training relevant to the audience.
21 timmar
Översikt
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 timmar
Översikt
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.
35 timmar
Översikt
This course is created for people who have no previous experience in probability and statistics.
14 timmar
Översikt
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.
28 timmar
Översikt
This course will give you knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications).
This training is more focus on fundamentals, but will help you to choose the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow.
This training is more focus on fundamentals, but will help you to choose the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow.
21 timmar
Översikt
This instructor-led, live course provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.
The course is interactive and includes plenty of hands-on exercises, instructor feedback, and testing of knowledge and skills acquired.
The course is interactive and includes plenty of hands-on exercises, instructor feedback, and testing of knowledge and skills acquired.
21 timmar
Översikt
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 timmar
Översikt
In this instructor-led, live training in Sverige, 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.
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.
21 timmar
Översikt
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 timmar
Översikt
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 timmar
Översikt
Snorkel is a system for rapidly creating, modeling, and managing training data. It focuses on accelerating the development of structured or "dark" data extraction applications for domains in which large labeled training sets are not available or easy to obtain.
In this instructor-led, live training, participants will learn techniques for extracting value from unstructured data such as text, tables, figures, and images through modeling of training data with Snorkel.
By the end of this training, participants will be able to:
- Programmatically create training sets to enable the labeling of massive training sets
- Train high-quality end models by first modeling noisy training sets
- Use Snorkel to implement weak supervision techniques and apply data programming to weakly-supervised machine learning systems
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 techniques for extracting value from unstructured data such as text, tables, figures, and images through modeling of training data with Snorkel.
By the end of this training, participants will be able to:
- Programmatically create training sets to enable the labeling of massive training sets
- Train high-quality end models by first modeling noisy training sets
- Use Snorkel to implement weak supervision techniques and apply data programming to weakly-supervised machine learning systems
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 timmar
Översikt
Encog is an open-source machine learning framework for Java and .Net.
In this instructor-led, live training, participants will learn advanced machine learning techniques for building accurate neural network predictive models.
By the end of this training, participants will be able to:
- Implement different neural networks optimization techniques to resolve underfitting and overfitting
- Understand and choose from a number of neural network architectures
- Implement supervised feed forward and feedback networks
Audience
- Developers
- Analysts
- 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 advanced machine learning techniques for building accurate neural network predictive models.
By the end of this training, participants will be able to:
- Implement different neural networks optimization techniques to resolve underfitting and overfitting
- Understand and choose from a number of neural network architectures
- Implement supervised feed forward and feedback networks
Audience
- Developers
- Analysts
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 timmar
Översikt
Encog is an open-source machine learning framework for Java and .Net.
In this instructor-led, live training, participants will learn how to create various neural network components using ENCOG. Real-world case studies will be discussed and machine language based solutions to these problems will be explored.
By the end of this training, participants will be able to:
- Prepare data for neural networks using the normalization process
- Implement feed forward networks and propagation training methodologies
- Implement classification and regression tasks
- Model and train neural networks using Encog's GUI based workbench
- Integrate neural network support into real-world applications
Audience
- Developers
- Analysts
- 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 create various neural network components using ENCOG. Real-world case studies will be discussed and machine language based solutions to these problems will be explored.
By the end of this training, participants will be able to:
- Prepare data for neural networks using the normalization process
- Implement feed forward networks and propagation training methodologies
- Implement classification and regression tasks
- Model and train neural networks using Encog's GUI based workbench
- Integrate neural network support into real-world applications
Audience
- Developers
- Analysts
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 timmar
Översikt
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
35 timmar
Översikt
This course begins with giving you conceptual knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications).
Part-1(40%) of this training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.
Part-2(20%) of this training introduces Theano - a python library that makes writing deep learning models easy.
Part-3(40%) of the training would be extensively based on Tensorflow - 2nd Generation API of Google's open source software library for Deep Learning. The examples and handson would all be made in TensorFlow.
Audience
This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects
After completing this course, delegates will:
-
have a good understanding on deep neural networks(DNN), CNN and RNN
-
understand TensorFlow’s structure and deployment mechanisms
-
be able to carry out installation / production environment / architecture tasks and configuration
-
be able to assess code quality, perform debugging, monitoring
-
be able to implement advanced production like training models, building graphs and logging
Part-1(40%) of this training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.
Part-2(20%) of this training introduces Theano - a python library that makes writing deep learning models easy.
Part-3(40%) of the training would be extensively based on Tensorflow - 2nd Generation API of Google's open source software library for Deep Learning. The examples and handson would all be made in TensorFlow.
Audience
This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects
After completing this course, delegates will:
-
have a good understanding on deep neural networks(DNN), CNN and RNN
-
understand TensorFlow’s structure and deployment mechanisms
-
be able to carry out installation / production environment / architecture tasks and configuration
-
be able to assess code quality, perform debugging, monitoring
-
be able to implement advanced production like training models, building graphs and logging
21 timmar
Översikt
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
14 timmar
Översikt
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
28 timmar
Översikt
This is a 4 day course introducing AI and it's application. There is an option to have an additional day to undertake an AI project on completion of this course.
28 timmar
Översikt
This is a 4 day course introducing AI and it's application using the Python programming language. There is an option to have an additional day to undertake an AI project on completion of this course.
21 timmar
Översikt
This instructor-led, live training in Sverige (online or onsite) is aimed at engineers who wish to learn about the applicability of artificial intelligence to mechatronic systems.
By the end of this training, participants will be able to:
- Gain an overview of artificial intelligence, machine learning, and computational intelligence.
- Understand the concepts of neural networks and different learning methods.
- Choose artificial intelligence approaches effectively for real-life problems.
- Implement AI applications in mechatronic engineering.
By the end of this training, participants will be able to:
- Gain an overview of artificial intelligence, machine learning, and computational intelligence.
- Understand the concepts of neural networks and different learning methods.
- Choose artificial intelligence approaches effectively for real-life problems.
- Implement AI applications in mechatronic engineering.
14 timmar
Översikt
This instructor-led, live training in Sverige (online or onsite) is aimed at data scientists who wish to use Python to build recommender systems.
By the end of this training, participants will be able to:
- Create recommender systems at scale.
- Apply collaborative filtering to build recommender systems.
- Use Apache Spark to compute recommender systems on clusters.
- Build a framework to test recommendation algorithms with Python.
By the end of this training, participants will be able to:
- Create recommender systems at scale.
- Apply collaborative filtering to build recommender systems.
- Use Apache Spark to compute recommender systems on clusters.
- Build a framework to test recommendation algorithms with Python.
14 timmar
Översikt
This instructor-led, live training in Sverige (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while making the code easy to debug.
By the end of this training, participants will be able to:
- Set up the necessary development environment to start developing neural network models.
- Define and implement neural network models using a comprehensible source code.
- Execute examples and modify existing algorithms to optimize deep learning training models while leveraging GPUs for high performance.
By the end of this training, participants will be able to:
- Set up the necessary development environment to start developing neural network models.
- Define and implement neural network models using a comprehensible source code.
- Execute examples and modify existing algorithms to optimize deep learning training models while leveraging GPUs for high performance.
14 timmar
Översikt
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.
Kommande Neural Networks Kurser
22-11-2021 09:30
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25-11-2021 09:30
14 timmar
01-12-2021 09:30
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06-12-2021 09:30
28 timmar
20-12-2021 09:30
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