Formations Réseaux Neurones, Neural Networks en Anglais. Un Réseau Neurones est un ensemble de neurones formels interconnectés permettant de résoudre des problèmes complexes comme la reconnaissance des formes ou le traitement du langage naturel, grâce à l'ajustement des coefficients de pondération dans une phase d'apprentissage.
Nos Clients témoignent
C'était très interactif et plus détendu et informel que prévu. Nous avons couvert de nombreux sujets dans le temps et le formateur a toujours été réceptif à parler plus en détail ou plus généralement des sujets et de leurs relations. Je pense que la formation m'a donné les outils pour continuer à apprendre, par opposition à une session unique où l'apprentissage s'arrête une fois que vous avez terminé, ce qui est très important compte tenu de l'ampleur et de la complexité du sujet.
Jonathan Blease
Formation: Artificial Neural Networks, Machine Learning, Deep Thinking
Machine Translated
Ann a créé un environnement idéal pour poser des questions et apprendre. Nous nous sommes beaucoup amusés et avons aussi beaucoup appris en même temps.
Gudrun Bickelq
Formation: Introduction to the use of neural networks
Machine Translated
La partie interactive, adaptée à nos besoins spécifiques.
Thomas Stocker
Formation: Introduction to the use of neural networks
Machine Translated
J'ai vraiment apprécié les réponses claires de Chris à nos questions.
Léo Dubus
Formation: Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple
Machine Translated
J'ai généralement apprécié le formateur compétent.
Sridhar Voorakkara
Formation: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
J'ai été stupéfait par la qualité de ce cours - je dirais que c'était la norme universitaire.
David Relihan
Formation: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Très bon aperçu général. Go historique des raisons pour lesquelles Tensorflow fonctionne comme il le fait.
Kieran Conboy
Formation: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
J'ai aimé les possibilités de poser des questions et d'obtenir des explications plus approfondies de la théorie.
Sharon Ruane
Formation: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Le formateur a très facilement expliqué des sujets difficiles et avancés.
Leszek K
Formation: Artificial Intelligence Overview
Machine Translated
J'ai aimé les nouvelles connaissances sur l'apprentissage en machine profonde.
Josip Arneric
Formation: Neural Network in R
Machine Translated
Nous avons acquis des connaissances sur NN en général et ce qui était le plus intéressant pour moi, c’était les nouveaux types de NN qui sont populaires de nos jours.
Tea Poklepovic
Formation: Neural Network in R
Machine Translated
J'ai surtout apprécié les graphiques en R :))).
Faculty of Economics and Business Zagreb
Formation: Neural Network in R
Machine Translated
Très souple.
Frank Ueltzhöffer
Formation: Artificial Neural Networks, Machine Learning and Deep Thinking
Machine Translated
J'ai généralement apprécié la flexibilité.
Werner Philipp
Formation: Artificial Neural Networks, Machine Learning and Deep Thinking
Machine Translated
Compte tenu des perspectives de la technologie: quelle technologie / processus pourrait devenir plus important dans le futur; voir à quoi la technologie peut être utilisée
Commerzbank AG
Formation: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
J'ai bénéficié de la sélection de sujets. Style de formation. Orientation pratique
Commerzbank AG
Formation: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Communication avec des conférenciers
文欣 张
Formation: Artificial Neural Networks, Machine Learning, Deep Thinking
Machine Translated
Comme ça
lisa xie
Formation: Artificial Neural Networks, Machine Learning, Deep Thinking
Machine Translated
beaucoup d'exercices que je peux utiliser directement dans mon travail.
Alior Bank S.A.
Formation: Sieci Neuronowe w R
Machine Translated
Exemples sur des données réelles
Alior Bank S.A.
Formation: Sieci Neuronowe w R
Machine Translated
neuralnet, pROC dans une boucle.
Alior Bank S.A.
Formation: Sieci Neuronowe w R
Machine Translated
Un large éventail de sujets couverts et une connaissance approfondie des dirigeants.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Formation: Understanding Deep Neural Networks
Machine Translated
manque
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Formation: Understanding Deep Neural Networks
Machine Translated
Grandes connaissances théoriques et pratiques des conférenciers. Communicativeness des formateurs. Pendant le cours, vous pouvez poser des questions et obtenir des réponses satisfaisantes.
Kamil Kurek - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Formation: Understanding Deep Neural Networks
Machine Translated
Partie pratique, où nous avons implémenté des algorithmes. Cela a permis une meilleure compréhension du sujet.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Formation: Understanding Deep Neural Networks
Machine Translated
exercices et exemples mis en oeuvre
Paweł Orzechowski - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Formation: Understanding Deep Neural Networks
Machine Translated
Exemples et problèmes discutés.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Formation: Understanding Deep Neural Networks
Machine Translated
Connaissances substantielles, engagement, une manière passionnée de transférer des connaissances. Exemples pratiques après un cours théorique.
Janusz Chrobot - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Formation: Understanding Deep Neural Networks
Machine Translated
Exercices pratiques préparés par M. Maciej
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Formation: Understanding Deep Neural Networks
Machine Translated
Les échanges informels que nous avons eus au cours des conférences m’ont vraiment aidé à approfondir ma compréhension du sujet
Explore
Formation: Deep Reinforcement Learning with Python
Machine Translated
Le formateur était un professionnel du domaine et de la théorie associée, avec une excellente application.
Fahad Malalla - Tatweer Petroleum
Formation: Applied AI from Scratch in Python
Machine Translated
thèmes, attitude amicale du présentateur
Fujitsu Technology Solutions Sp. z o.o.
Formation: Artificial Intelligence Overview
Machine Translated
Sous-catégories Réseaux Neurones
Plans de cours Réseaux Neurones
Nom du Cours
Durée
Aperçu
Nom du Cours
Durée
Aperçu
7 hours
Aperçu
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 hours
Aperçu
La formation s'adresse aux personnes qui souhaitent apprendre les bases des réseaux de neurones et de leurs applications.
14 hours
Aperçu
Ce cours est une introduction à l'application de réseaux de neurones à des problèmes du monde réel à l'aide du logiciel R-project.
14 hours
Aperçu
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 hours
Aperçu
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
Aperçu
Le réseau de neurones artificiels est un modèle de données informatique utilisé dans le développement de systèmes d' Artificial Intelligence (AI) capables d'effectuer des tâches "intelligentes". Neural Networks sont couramment utilisés dans les applications Machine Learning (ML), qui sont elles-mêmes une implémentation de l'IA. Deep Learning est un sous-ensemble de ML.
35 hours
Aperçu
This course is created for people who have no previous experience in probability and statistics.
14 hours
Aperçu
Ce cours couvre l'IA (mettant l'accent sur l' Machine Learning et l' Deep Learning ) dans l'industrie Automotive . Cela aide à déterminer quelle technologie peut (potentiellement) être utilisée dans plusieurs situations de la voiture: de l'automatisation simple à la prise de décision autonome en passant par la reconnaissance d'images.
28 hours
Aperçu
Ce cours vous donnera des connaissances sur les réseaux de neurones et plus généralement sur les algorithmes d’apprentissage automatique, d’apprentissage approfondi (algorithmes et applications).
Cette formation met davantage l'accent sur les principes fondamentaux, mais vous aidera à choisir la technologie TensorFlow : TensorFlow , Caffe , Teano, DeepDrive, Keras , etc. Les exemples sont réalisés dans TensorFlow .
Cette formation met davantage l'accent sur les principes fondamentaux, mais vous aidera à choisir la technologie TensorFlow : TensorFlow , Caffe , Teano, DeepDrive, Keras , etc. Les exemples sont réalisés dans TensorFlow .
21 hours
Aperçu
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 hours
Aperçu
Type : Formation théorique avec applications décidées en amont avec les élèves sur Lasagne ou Keras selon le groupe pédagogique
Méthode pédagogique : présentation, échanges et études de cas
L’intelligence artificielle, après avoir bouleversé de nombreux domaines scientifiques, a commencé à révolutionner un grand nombre de secteurs économiques (industrie, médecine, communication, etc.). Néanmoins, sa présentation dans les grands media relève souvent du fantasme, très éloignée de ce que sont réellement les domaines du Machine Learning ou du Deep Learning. L’objet de cette formation est d’apporter à des ingénieurs ayant déjà une maîtrise des outils informatiques (dont une base de programmation logicielle) une introduction au Deep Learning ainsi qu’à ses différents domaines de spécialisation et donc aux principales architectures de réseau existant aujourd’hui. Si les bases mathématiques sont rappelées pendant le cours, un niveau de mathématique de type BAC+2 est recommandé pour plus de confort. Il est dans l’absolu possible de faire l’impasse sur l’axe mathématique pour ne conserver qu’une vision « système », mais cette approche limitera énormément votre compréhension du sujet.
Méthode pédagogique : présentation, échanges et études de cas
L’intelligence artificielle, après avoir bouleversé de nombreux domaines scientifiques, a commencé à révolutionner un grand nombre de secteurs économiques (industrie, médecine, communication, etc.). Néanmoins, sa présentation dans les grands media relève souvent du fantasme, très éloignée de ce que sont réellement les domaines du Machine Learning ou du Deep Learning. L’objet de cette formation est d’apporter à des ingénieurs ayant déjà une maîtrise des outils informatiques (dont une base de programmation logicielle) une introduction au Deep Learning ainsi qu’à ses différents domaines de spécialisation et donc aux principales architectures de réseau existant aujourd’hui. Si les bases mathématiques sont rappelées pendant le cours, un niveau de mathématique de type BAC+2 est recommandé pour plus de confort. Il est dans l’absolu possible de faire l’impasse sur l’axe mathématique pour ne conserver qu’une vision « système », mais cette approche limitera énormément votre compréhension du sujet.
7 hours
Aperçu
In this instructor-led, live training in Belgique, 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 hours
Aperçu
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
Aperçu
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
Aperçu
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 hours
Aperçu
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 hours
Aperçu
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 hours
Aperçu
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 hours
Aperçu
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 hours
Aperçu
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 hours
Aperçu
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 hours
Aperçu
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 hours
Aperçu
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 hours
Aperçu
This instructor-led, live training in Belgique (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 hours
Aperçu
This instructor-led, live training in Belgique (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 hours
Aperçu
This instructor-led, live training in Belgique (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 hours
Aperçu
OpenNN est une bibliothèque de classes open-source écrite en C ++ qui implémente des réseaux de neurones, pour une utilisation en apprentissage automatique.
Dans ce cours, nous allons passer en revue les principes des réseaux de neurones et utiliser OpenNN pour implémenter un exemple d'application.
Public
Les développeurs de logiciels et les programmeurs souhaitant créer des applications d'apprentissage approfondi.
Format du cours
Lecture et discussion associées à des exercices pratiques.
Dans ce cours, nous allons passer en revue les principes des réseaux de neurones et utiliser OpenNN pour implémenter un exemple d'application.
Public
Les développeurs de logiciels et les programmeurs souhaitant créer des applications d'apprentissage approfondi.
Format du cours
Lecture et discussion associées à des exercices pratiques.
Prochains cours Réseaux Neurones
26-10-2021 09:30
14 heures
03-11-2021 09:30
14 heures
15-11-2021 09:30
35 heures
29-11-2021 09:30
14 heures
30-11-2021 09:30
21 heures
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Weekend Réseaux Neurones cours, Soir Réseaux Neurones formation, Réseaux Neurones stage d’entraînement, Réseaux Neurones formateur à distance, Réseaux Neurones formateur en ligne, Réseaux Neurones formateur Online, Réseaux Neurones cours en ligne, Réseaux Neurones cours à distance, Réseaux Neurones professeur à distance, Réseaux Neurones visioconférence, Réseaux Neurones stage d’entraînement intensif, Réseaux Neurones formation accélérée, Réseaux Neurones formation intensive, Formation inter Réseaux Neurones, Formation intra Réseaux Neurones, Formation intra Enteprise Réseaux Neurones, Formation inter Entreprise Réseaux Neurones, Weekend Réseaux Neurones formation, Soir Réseaux Neurones cours, Réseaux Neurones coaching, Réseaux Neurones entraînement, Réseaux Neurones préparation, Réseaux Neurones instructeur, Réseaux Neurones professeur, Réseaux Neurones formateur, Réseaux Neurones stage de formation, Réseaux Neurones cours, Réseaux Neurones sur place, Réseaux Neurones formations privées, Réseaux Neurones formation privée, Réseaux Neurones cours particulier, Réseaux Neurones cours particuliers
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