Lokale, door een instructeur geleide, live Artificial Intelligence (AI) -cursussen demonstreren door hands-on de praktijk van het implementeren van AI-oplossingen voor het oplossen van problemen in de echte wereld. AI-training is beschikbaar als 'live training op locatie' of 'live training op afstand'. Onsite live training kan lokaal worden uitgevoerd op het terrein van de klant in Nederland of in NobleProg bedrijfsopleidingscentra in Nederland . Live-training op afstand wordt uitgevoerd via een interactieve, externe desktop. NobleProg - uw lokale trainingsaanbieder.
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Getuigenissen
We hebben veel meer inzicht gekregen in het onderwerp. Er is een aardige discussie gevoerd met enkele echte onderwerpen binnen ons bedrijf.
Sebastiaan Holman
Cursus: Machine Learning and Deep Learning
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De training bood de juiste basis om verder uit te breiden, door te laten zien hoe theorie en praktijk hand in hand gaan. Ik raakte hierdoor meer geïnteresseerd in het onderwerp dan voorheen.
Jean-Paul van Tillo
Cursus: Machine Learning and Deep Learning
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Ik heb echt genoten van de dekking en diepte van onderwerpen.
Anirban Basu
Cursus: Machine Learning and Deep Learning
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Het geduld van de trainer
European Space Agency (ESA/ESTEC)
Cursus: Getting Started with Quantum Computing and Q#
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Hij was zeer informatief en nuttig.
Pratheep Ravy
Cursus: Predictive Modelling with R
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Het was heel interactief en meer ontspannen en informeel dan verwacht. We hebben veel onderwerpen behandeld in de tijd en de trainer was altijd ontvankelijk om meer in detail of meer in het algemeen te praten over de onderwerpen en hoe deze gerelateerd waren. Ik heb het gevoel dat de training me de tools heeft gegeven om verder te leren, in tegenstelling tot een eenmalige sessie waarbij het leren stopt als je klaar bent, wat erg belangrijk is gezien de schaal en complexiteit van het onderwerp.
Jonathan Blease
Cursus: Artificial Neural Networks, Machine Learning, Deep Thinking
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Ann creëerde een geweldige omgeving om vragen te stellen en te leren. We hadden veel plezier en leerden ook veel tegelijkertijd.
Gudrun Bickelq
Cursus: Introduction to the use of neural networks
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Het interactieve deel, afgestemd op onze specifieke behoeften.
Thomas Stocker
Cursus: Introduction to the use of neural networks
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Ik vond de oefeningen leuk.
Office for National Statistics
Cursus: Natural Language Processing with Python
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Ik genoot oprecht van de praktische benadering.
Kevin De Cuyper
Cursus: Computer Vision with OpenCV
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bereik van materiaal
Maciej Jonczyk
Cursus: From Data to Decision with Big Data and Predictive Analytics
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systematisering van kennis op het gebied van ML
Orange Polska
Cursus: From Data to Decision with Big Data and Predictive Analytics
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De trainer was zo deskundig en inclusief gebieden waarin ik geïnteresseerd was.
Mohamed Salama
Cursus: Data Mining & Machine Learning with R
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Het onderwerp is erg interessant.
Wojciech Baranowski
Cursus: Introduction to Deep Learning
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Train theoretische kennis en bereidheid om de problemen met de deelnemers na de training op te lossen.
Grzegorz Mianowski
Cursus: Introduction to Deep Learning
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Onderwerp. Heel interessant!.
Piotr
Cursus: Introduction to Deep Learning
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Oefeningen na elk onderwerp waren erg nuttig, ondanks dat er aan het eind te gecompliceerd was. Over het algemeen was het gepresenteerde materiaal erg interessant en betekend het! Oefeningen met beeldherkenning waren geweldig.
Dolby Poland Sp. z o.o.
Cursus: Introduction to Deep Learning
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Ik denk dat als de training in het Pools wordt gedaan, de trainer zijn kennis efficiënter zou kunnen delen.
Radek
Cursus: Introduction to Deep Learning
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Het wereldwijde overzicht van deep learning.
Bruno Charbonnier
Cursus: Advanced Deep Learning
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De oefeningen zijn voldoende praktisch en hebben geen hoge kennis van Python nodig om gedaan te worden.
Alexandre GIRARD
Cursus: Advanced Deep Learning
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Oefeningen doen op echte voorbeelden met behulp van Eras. Italië heeft onze verwachtingen over deze training volledig begrepen.
Paul Kassis
Cursus: Advanced Deep Learning
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Ik waardeerde echt de kristalheldere antwoorden van Chris op onze vragen.
Léo Dubus
Cursus: Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple
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Ik genoot over het algemeen van de deskundige trainer.
Sridhar Voorakkara
Cursus: Neural Networks Fundamentals using TensorFlow as Example
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Ik was verbaasd over de standaard van deze klas - ik zou zeggen dat het een universitaire standaard was.
David Relihan
Cursus: Neural Networks Fundamentals using TensorFlow as Example
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Zeer goed overzicht. Go achtergrond waarom Tensorflow zo werkt.
Kieran Conboy
Cursus: Neural Networks Fundamentals using TensorFlow as Example
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Ik vond de mogelijkheden om vragen te stellen en meer diepgaande verklaringen van de theorie te krijgen.
Sharon Ruane
Cursus: Neural Networks Fundamentals using TensorFlow as Example
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De trainer legde zeer gemakkelijk moeilijke en geavanceerde onderwerpen uit.
Leszek K
Cursus: Artificial Intelligence Overview
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Ik vond de nieuwe inzichten in deep machine learning leuk.
Josip Arneric
Cursus: Neural Network in R
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We hebben wat kennis opgedaan van NN in het algemeen en wat voor mij het meest interessant was, waren de nieuwe typen NN die tegenwoordig populair zijn.
Tea Poklepovic
Cursus: Neural Network in R
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Ik genoot vooral van de grafieken in R :))).
Faculty of Economics and Business Zagreb
Cursus: Neural Network in R
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De diepe kennis van de trainer over het onderwerp.
Sebastian Görg
Cursus: Introduction to Deep Learning
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Zeer geactualiseerde aanpak of CPI (tensor flow, era, learn) om machine learning te doen.
Paul Lee
Cursus: TensorFlow for Image Recognition
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Zeer flexibel.
Frank Ueltzhöffer
Cursus: Artificial Neural Networks, Machine Learning and Deep Thinking
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Ik genoot over het algemeen van de flexibiliteit.
Werner Philipp
Cursus: Artificial Neural Networks, Machine Learning and Deep Thinking
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Gezien de visie van de technologie: welke technologie / proces kan in de toekomst belangrijker worden; zie, waar de technologie voor kan worden gebruikt.
Commerzbank AG
Cursus: Neural Networks Fundamentals using TensorFlow as Example
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Ik had baat bij het selecteren van onderwerpen. Stijl van training. Oefen oriëntatie.
Commerzbank AG
Cursus: Neural Networks Fundamentals using TensorFlow as Example
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Het bevalt allemaal
蒙 李
Cursus: Machine Learning Fundamentals with Python
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manier van leidinggeven en voorbeeld gegeven door de trainer
ORANGE POLSKA S.A.
Cursus: Machine Learning and Deep Learning
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Mogelijkheid om de voorgestelde problemen zelf te bespreken
ORANGE POLSKA S.A.
Cursus: Machine Learning and Deep Learning
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Communicatie met docenten
文欣 张
Cursus: Artificial Neural Networks, Machine Learning, Deep Thinking
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Vind het leuk
lisa xie
Cursus: Artificial Neural Networks, Machine Learning, Deep Thinking
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Diepgaande aandacht voor onderwerpen van machine learning, met name neurale netwerken. Demystified veel van het onderwerp.
Sacha Nandlall
Cursus: Python for Advanced Machine Learning
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Dit is een van de beste praktische oefeningencursussen die ik ooit heb gevolgd.
Laura Kahn
Cursus: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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Dit is een van de beste online training die ik ooit heb gevolgd in mijn 13-jarige carrière. Ga zo door!.
Cursus: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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veel oefeningen die ik direct in mijn werk kan gebruiken.
Alior Bank S.A.
Cursus: Sieci Neuronowe w R
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Voorbeelden van echte gegevens.
Alior Bank S.A.
Cursus: Sieci Neuronowe w R
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Dit is een van de beste online training die ik ooit heb gevolgd in mijn 13-jarige carrière. Ga zo door!.
Cursus: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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Kunstmatige Intelligentie Cursusoverzicht
Course Name
Duration
Overview
Course Name
Duration
Overview
7 hours
Overview
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.
21 hours
Overview
This course uses a practical approach to teaching OptaPlanner. It provides participants with the tools needed to perform the basic functions of this tool.
28 hours
Overview
This four day course is aimed at teaching how genetic algorithms work; it also covers how to select model parameters of a genetic algorithm; there are many applications for genetic algorithms in this course and optimization problems are tackled with the genetic algorithms.
7 hours
Overview
This is a classroom based training session in a presentation and Q&A format
14 hours
Overview
This instructor-led, live training in Nederland (online or onsite) is aimed at technical persons who wish to set up or extend an RPA system with more intelligent capabilities.
By the end of this training, participants will be able to:
- Install and configure UiPath IPA.
- Enable robots to manage other robots.
- Apply computer vision to locate screen objects with accuracy.
- Enable robots that can detect language patterns and carry out sentiment analysis on unstructured content.
By the end of this training, participants will be able to:
- Install and configure UiPath IPA.
- Enable robots to manage other robots.
- Apply computer vision to locate screen objects with accuracy.
- Enable robots that can detect language patterns and carry out sentiment analysis on unstructured content.
14 hours
Overview
This instructor-led, live training in Nederland (online or onsite) is aimed at software testers who wish to have an AI driven software testing environment.
By the end of this training, participants will be able to:
- Automate unit test generation and parameterization with AI.
- Apply machine learning learning in a real world use-case.
- Automate the generation and maintenance of API tests with AI.
- Use machine learning methods to self-heal the execution of Selenium tests.
By the end of this training, participants will be able to:
- Automate unit test generation and parameterization with AI.
- Apply machine learning learning in a real world use-case.
- Automate the generation and maintenance of API tests with AI.
- Use machine learning methods to self-heal the execution of Selenium tests.
35 hours
Overview
This is a 5 day introduction to Data Science and Artificial Intelligence (AI).
The course is delivered with examples and exercises using Python
The course is delivered with examples and exercises using Python
7 hours
Overview
This instructor-led, live training in Nederland (online or onsite) is aimed at marketers who wish to use AI to improve improve digital marketing strategies through valuable customer insights.
By the end of this training, participants will be able to:
- Leverage AI software to improve the way brands connect to users.
- Use chatbots to optimize the user-experience.
- Increase productivity and revenue through the automation of tasks.
By the end of this training, participants will be able to:
- Leverage AI software to improve the way brands connect to users.
- Use chatbots to optimize the user-experience.
- Increase productivity and revenue through the automation of tasks.
28 hours
Overview
Microsoft Azure Cloud offerings are the most comprehensive set of services w.r.t. AI, ML, DL
From Vision, Speech to Language to Conversational bots, Azure is empowering organizations to process their data to derive insights
This instructor-led, live training (online or onsite) is aimed at AI enthusiasts who wish to use Azure to build AI scenarios in the cloud
By the end of this training, participants will be able to:
- Build Azure based AI scenarios.
- Understand the end to end functioning of API based infering
- Build conversational bots for business needs
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 more details or customized training for this course, please contact us to arrange.
From Vision, Speech to Language to Conversational bots, Azure is empowering organizations to process their data to derive insights
This instructor-led, live training (online or onsite) is aimed at AI enthusiasts who wish to use Azure to build AI scenarios in the cloud
By the end of this training, participants will be able to:
- Build Azure based AI scenarios.
- Understand the end to end functioning of API based infering
- Build conversational bots for business needs
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 more details or customized training for this course, please contact us to arrange.
14 hours
Overview
This instructor-led, live training in Nederland (online or onsite) is aimed at data scientists who wish to use IBM Cloud Pak to prepare data for use in AI solutions.
By the end of this training, participants will be able to:
- Install and configure Cloud Pak for Data.
- Unify the collection, organization and analysis of data.
- Integrate Cloud Pak for Data with a variety of services to solve common business problems.
- Implement workflows for collaborating with team members on the development of an AI solution.
By the end of this training, participants will be able to:
- Install and configure Cloud Pak for Data.
- Unify the collection, organization and analysis of data.
- Integrate Cloud Pak for Data with a variety of services to solve common business problems.
- Implement workflows for collaborating with team members on the development of an AI solution.
21 hours
Overview
This instructor-led, live training in Nederland (online or onsite) is aimed at engineers who wish to program and create robots through basic AI methods.
By the end of this training, participants will be able to:
- Implement filters (Kalman and particle) to enable the robot to locate moving objects in its environment.
- Implement search algorithms and motion planning.
- Implement PID controls to regulate a robot's movement within an environment.
- Implement SLAM algorithms to enable a robot to map out an unknown environment.
By the end of this training, participants will be able to:
- Implement filters (Kalman and particle) to enable the robot to locate moving objects in its environment.
- Implement search algorithms and motion planning.
- Implement PID controls to regulate a robot's movement within an environment.
- Implement SLAM algorithms to enable a robot to map out an unknown environment.
7 hours
Overview
This instructor-led, live training in Nederland (online or onsite) is aimed at managers and business leaders who wish to learn about the fundamentals of artificial intelligence and manage AI projects for their organization.
By the end of this training, participants will be able to understand AI at a technical level and strategize using their organization’s data and resources to successfully manage AI projects.
By the end of this training, participants will be able to understand AI at a technical level and strategize using their organization’s data and resources to successfully manage AI projects.
80 hours
Overview
In this instructor-led, live training in Nederland (online or onsite), participants will learn the different technologies, frameworks and techniques for programming different types of robots to be used in the field of nuclear technology and environmental systems.
The 4-week course is held 5 days a week. Each day is 4-hours long and consists of lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete various real-world projects applicable to their work in order to practice their acquired knowledge.
The target hardware for this course will be simulated in 3D through simulation software. The code will then be loaded onto physical hardware (Arduino or other) for final deployment testing. The ROS (Robot Operating System) open-source framework, C++ and Python will be used for programming the robots.
By the end of this training, participants will be able to:
- Understand the key concepts used in robotic technologies.
- Understand and manage the interaction between software and hardware in a robotic system.
- Understand and implement the software components that underpin robotics.
- Build and operate a simulated mechanical robot that can see, sense, process, navigate, and interact with humans through voice.
- Understand the necessary elements of artificial intelligence (machine learning, deep learning, etc.) applicable to building a smart robot.
- Implement filters (Kalman and Particle) to enable the robot to locate moving objects in its environment.
- Implement search algorithms and motion planning.
- Implement PID controls to regulate a robot's movement within an environment.
- Implement SLAM algorithms to enable a robot to map out an unknown environment.
- Test and troubleshoot a robot in realistic scenarios.
The 4-week course is held 5 days a week. Each day is 4-hours long and consists of lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete various real-world projects applicable to their work in order to practice their acquired knowledge.
The target hardware for this course will be simulated in 3D through simulation software. The code will then be loaded onto physical hardware (Arduino or other) for final deployment testing. The ROS (Robot Operating System) open-source framework, C++ and Python will be used for programming the robots.
By the end of this training, participants will be able to:
- Understand the key concepts used in robotic technologies.
- Understand and manage the interaction between software and hardware in a robotic system.
- Understand and implement the software components that underpin robotics.
- Build and operate a simulated mechanical robot that can see, sense, process, navigate, and interact with humans through voice.
- Understand the necessary elements of artificial intelligence (machine learning, deep learning, etc.) applicable to building a smart robot.
- Implement filters (Kalman and Particle) to enable the robot to locate moving objects in its environment.
- Implement search algorithms and motion planning.
- Implement PID controls to regulate a robot's movement within an environment.
- Implement SLAM algorithms to enable a robot to map out an unknown environment.
- Test and troubleshoot a robot in realistic scenarios.
120 hours
Overview
In this instructor-led, live training in Nederland (online or onsite), participants will learn the different technologies, frameworks and techniques for programming different types of robots to be used in the field of nuclear technology and environmental systems.
The 6-week course is held 5 days a week. Each day is 4-hours long and consists of lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete various real-world projects applicable to their work in order to practice their acquired knowledge.
The target hardware for this course will be simulated in 3D through simulation software. The ROS (Robot Operating System) open-source framework, C++ and Python will be used for programming the robots.
By the end of this training, participants will be able to:
- Understand the key concepts used in robotic technologies.
- Understand and manage the interaction between software and hardware in a robotic system.
- Understand and implement the software components that underpin robotics.
- Build and operate a simulated mechanical robot that can see, sense, process, navigate, and interact with humans through voice.
- Understand the necessary elements of artificial intelligence (machine learning, deep learning, etc.) applicable to building a smart robot.
- Implement filters (Kalman and Particle) to enable the robot to locate moving objects in its environment.
- Implement search algorithms and motion planning.
- Implement PID controls to regulate a robot's movement within an environment.
- Implement SLAM algorithms to enable a robot to map out an unknown environment.
- Extend a robot's ability to perform complex tasks through Deep Learning.
- Test and troubleshoot a robot in realistic scenarios.
The 6-week course is held 5 days a week. Each day is 4-hours long and consists of lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete various real-world projects applicable to their work in order to practice their acquired knowledge.
The target hardware for this course will be simulated in 3D through simulation software. The ROS (Robot Operating System) open-source framework, C++ and Python will be used for programming the robots.
By the end of this training, participants will be able to:
- Understand the key concepts used in robotic technologies.
- Understand and manage the interaction between software and hardware in a robotic system.
- Understand and implement the software components that underpin robotics.
- Build and operate a simulated mechanical robot that can see, sense, process, navigate, and interact with humans through voice.
- Understand the necessary elements of artificial intelligence (machine learning, deep learning, etc.) applicable to building a smart robot.
- Implement filters (Kalman and Particle) to enable the robot to locate moving objects in its environment.
- Implement search algorithms and motion planning.
- Implement PID controls to regulate a robot's movement within an environment.
- Implement SLAM algorithms to enable a robot to map out an unknown environment.
- Extend a robot's ability to perform complex tasks through Deep Learning.
- Test and troubleshoot a robot in realistic scenarios.
7 hours
Overview
The training is aimed at people who want to learn the basics of neural networks and their applications.
14 hours
Overview
This course is an introduction to applying neural networks in real world problems using R-project software.
14 hours
Overview
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
Overview
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
Overview
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 hours
Overview
This course is created for people who have no previous experience in probability and statistics.
14 hours
Overview
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 hours
Overview
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 hours
Overview
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
Overview
Artificial intelligence has revolutionized a large number of economic sectors (industry, medicine, communication, etc.) after having upset many scientific fields. Nevertheless, his presentation in the major media is often a fantasy, far removed from what really are the fields of Machine Learning or Deep Learning. The aim of this course is to provide engineers who already have a master's degree in computer tools (including a software programming base) an introduction to Deep Learning as well as to its various fields of specialization and therefore to the main existing network architectures today. If the mathematical bases are recalled during the course, a level of mathematics of type BAC + 2 is recommended for more comfort. It is absolutely possible to ignore the mathematical axis in order to maintain only a "system" vision, but this approach will greatly limit your understanding of the subject.
7 hours
Overview
In this instructor-led, live training in Nederland, 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
Overview
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
Overview
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
Overview
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
Overview
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
Overview
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
Last Updated:
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