Os cursos de treinamento de Inteligência Artificial (AI) ao vivo demonstram, através da prática, como implementar soluções de IA para resolver problemas do mundo real. O treinamento de IA está disponível como "treinamento ao vivo no local" ou "treinamento remoto ao vivo" Treinamento ao vivo pode ser realizado nas instalações do cliente no Brasil ou nos centros de treinamento corporativo da NobleProg no Brasil . O treinamento ao vivo remoto é realizado por meio de uma área de trabalho online e interativa NobleProg seu provedor de treinamento local.
NobleProg -- Seu Provedor de Treinamento Local
Declaração de Clientes
Ele foi muito informativo e útil.
Pratheep Ravy
Curso: Predictive Modelling with R
Machine Translated
Foi muito interativo e mais descontraído e informal do que o esperado. Nós cobrimos muitos tópicos no tempo e o treinador sempre foi receptivo a falar mais detalhadamente ou mais geralmente sobre os tópicos e como eles estavam relacionados. Eu sinto que o treinamento me deu as ferramentas para continuar aprendendo, em vez de ser uma sessão única em que o aprendizado para quando você terminar, o que é muito importante, dada a escala e a complexidade do tópico.
Jonathan Blease
Curso: Artificial Neural Networks, Machine Learning, Deep Thinking
Machine Translated
Ann criou um ótimo ambiente para fazer perguntas e aprender. Nós nos divertimos muito e também aprendemos muito ao mesmo tempo.
Gudrun Bickelq
Curso: Introduction to the use of neural networks
Machine Translated
A parte interativa, adaptada às nossas necessidades específicas.
Thomas Stocker
Curso: Introduction to the use of neural networks
Machine Translated
Eu gostei dos exercícios.
Office for National Statistics
Curso: Natural Language Processing with Python
Machine Translated
Eu realmente gostei da abordagem prática.
Kevin De Cuyper
Curso: Computer Vision with OpenCV
Machine Translated
variedade de material
Maciej Jonczyk
Curso: From Data to Decision with Big Data and Predictive Analytics
Machine Translated
sistematizando conhecimento no campo de ML
Orange Polska
Curso: From Data to Decision with Big Data and Predictive Analytics
Machine Translated
O treinador foi tão experiente e áreas incluídas eu estava interessado em.
Mohamed Salama
Curso: Data Mining & Machine Learning with R
Machine Translated
O assunto é muito interessante.
Wojciech Baranowski
Curso: Introduction to Deep Learning
Machine Translated
Formadores de conhecimentos teóricos e vontade de resolver os problemas com os participantes após o treinamento.
Grzegorz Mianowski
Curso: Introduction to Deep Learning
Machine Translated
Tema. Muito interessante!.
Piotr
Curso: Introduction to Deep Learning
Machine Translated
Exercícios após cada tópico foram realmente úteis, apesar de terem sido muito complicados no final. Em geral, o material apresentado foi muito interessante e envolvente! Exercícios com reconhecimento de imagem foram ótimos.
Dolby Poland Sp. z o.o.
Curso: Introduction to Deep Learning
Machine Translated
Eu acho que se o treinamento fosse feito em polonês, isso permitiria ao treinador compartilhar seus conhecimentos de forma mais eficiente.
Radek
Curso: Introduction to Deep Learning
Machine Translated
A visão global da aprendizagem profunda.
Bruno Charbonnier
Curso: Advanced Deep Learning
Machine Translated
Os exercícios são suficientemente práticos e não precisam de um alto conhecimento em Python para ser feito.
Alexandre GIRARD
Curso: Advanced Deep Learning
Machine Translated
Fazendo exercícios em exemplos reais usando Eras. A Itália entendeu totalmente as nossas expectativas sobre esse treinamento.
Paul Kassis
Curso: Advanced Deep Learning
Machine Translated
Eu realmente apreciei as respostas cristalinas de Chris às nossas perguntas.
Léo Dubus
Curso: Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple
Machine Translated
Eu geralmente gostava do treinador experiente.
Sridhar Voorakkara
Curso: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Fiquei espantado com o padrão desta classe - eu diria que era padrão universitário.
David Relihan
Curso: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Muito boa visão geral. Go fundo od em porque Tensorflow opera como ele faz.
Kieran Conboy
Curso: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Gostei das oportunidades de fazer perguntas e obter explicações mais aprofundadas da teoria.
Sharon Ruane
Curso: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Nós obtivemos muito mais informações sobre o assunto. Alguma discussão agradável foi feita com alguns assuntos reais dentro da nossa empresa.
Sebastiaan Holman
Curso: Machine Learning and Deep Learning
Machine Translated
O treinamento forneceu a base correta que nos permite expandir ainda mais, mostrando como a teoria e a prática andam de mãos dadas. Isso realmente me deixou mais interessado no assunto do que eu era antes.
Jean-Paul van Tillo
Curso: Machine Learning and Deep Learning
Machine Translated
Eu realmente gostei da cobertura e profundidade dos tópicos.
Anirban Basu
Curso: Machine Learning and Deep Learning
Machine Translated
O treinador explicou muito facilmente tópicos difíceis e avançados.
Leszek K
Curso: Artificial Intelligence Overview
Machine Translated
Eu gostei dos novos insights em deep machine learning.
Josip Arneric
Curso: Neural Network in R
Machine Translated
Ganhamos algum conhecimento sobre o NN em geral, e o que foi mais interessante para mim foram os novos tipos de NN que são populares hoje em dia.
Tea Poklepovic
Curso: Neural Network in R
Machine Translated
Eu gostei principalmente dos gráficos em R :))).
Faculty of Economics and Business Zagreb
Curso: Neural Network in R
Machine Translated
O profundo conhecimento do treinador sobre o tema.
Sebastian Görg
Curso: Introduction to Deep Learning
Machine Translated
Abordagem muito atualizada ou CPI (tensor flow, era, learn) para fazer aprendizado de máquina.
Paul Lee
Curso: TensorFlow for Image Recognition
Machine Translated
Muito flexível.
Frank Ueltzhöffer
Curso: Artificial Neural Networks, Machine Learning and Deep Thinking
Machine Translated
Eu geralmente gostei da flexibilidade.
Werner Philipp
Curso: Artificial Neural Networks, Machine Learning and Deep Thinking
Machine Translated
Dada perspectiva da tecnologia: qual tecnologia / processo pode se tornar mais importante no futuro; veja, para que a tecnologia pode ser usada.
Commerzbank AG
Curso: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Eu estava beneficiado com a seleção de tópicos. Estilo de treinamento Orientação prática.
Commerzbank AG
Curso: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
Todos gostam
蒙 李
Curso: Machine Learning Fundamentals with Python
Machine Translated
forma de condução e exemplo dado pelo treinador
ORANGE POLSKA S.A.
Curso: Machine Learning and Deep Learning
Machine Translated
Possibilidade de discutir os problemas propostos você mesmo
ORANGE POLSKA S.A.
Curso: Machine Learning and Deep Learning
Machine Translated
Comunicação com palestrantes
文欣 张
Curso: Artificial Neural Networks, Machine Learning, Deep Thinking
Machine Translated
Gostei
lisa xie
Curso: Artificial Neural Networks, Machine Learning, Deep Thinking
Machine Translated
Abrangente cobertura de tópicos de aprendizado de máquina, particularmente redes neurais. Desmistificou muito do tópico.
Sacha Nandlall
Curso: Python for Advanced Machine Learning
Machine Translated
Este é um dos melhores trabalhos práticos com os cursos de programação de exercícios que já fiz.
Laura Kahn
Curso: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
Machine Translated
Este é um dos treinamentos online de melhor qualidade que já fiz em minha carreira de 13 anos. Continue com o ótimo trabalho!.
Curso: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
Machine Translated
muitos exercícios que posso usar diretamente no meu trabalho.
Alior Bank S.A.
Curso: Sieci Neuronowe w R
Machine Translated
Exemplos de dados reais.
Alior Bank S.A.
Curso: Sieci Neuronowe w R
Machine Translated
neuralnet, pROC em um loop.
Alior Bank S.A.
Curso: Sieci Neuronowe w R
Machine Translated
O estilo de treinamento de Richard manteve interessante, os exemplos do mundo real usados ajudaram a levar os conceitos para casa.
Jamie Martin-Royle - NBrown Group
Curso: From Data to Decision with Big Data and Predictive Analytics
Machine Translated
O conteúdo, como eu achei muito interessante e acho que me ajudaria no meu último ano na Universidade.
Krishan Mistry - NBrown Group
Curso: From Data to Decision with Big Data and Predictive Analytics
Machine Translated
Eu realmente gostei de exercícios
L M ERICSSON LIMITED
Curso: Machine Learning
Machine Translated
os exercícios de laboratório
Marcell Lorant - L M ERICSSON LIMITED
Curso: Machine Learning
Machine Translated
Este é um dos treinamentos online de melhor qualidade que já fiz em minha carreira de 13 anos. Continue com o ótimo trabalho!.
Curso: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
Machine Translated
Nossos Clientes
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.jpg "european southern observatory (eso)")
_ireland.gif "environmental protection agency (epa) ireland")
Subcategorias AI
Programas do curso AI
Nome do Curso
Duração
Visão geral
Nome do Curso
Duração
Visão geral
7 horas
Visão geral
Este curso foi criado para administradores, solution architects, executivos de inovação, CTOs, e todos os interessados em aprender e conhecer o panorama geral da inteligência artificial aplicada aos problemas organizacionais.
21 horas
Visão geral
Este curso é um enfoque pratico a ferramenta OptaPLanner, proporcionando aos partiipantes todas as ferramentas necessárias para conseguir um conhecimento introdutório unico e funcional que os permit realizar as funções basicas nessa ferramenta.
28 horas
Visão geral
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 horas
Visão geral
This is a classroom based training session in a presentation and Q&A format
14 horas
Visão geral
This instructor-led, live training in Brasil (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 horas
Visão geral
This instructor-led, live training in Brasil (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 horas
Visão geral
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 horas
Visão geral
This instructor-led, live training in Brasil (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.
14 horas
Visão geral
This instructor-led, live training in Brasil (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 horas
Visão geral
This instructor-led, live training in Brasil (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 horas
Visão geral
This instructor-led, live training in Brasil (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 horas
Visão geral
In this instructor-led, live training in Brasil (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 horas
Visão geral
In this instructor-led, live training in Brasil (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 horas
Visão geral
O treinamento é destinado a pessoas que querem aprender o básico das redes neurais e suas aplicações.
14 horas
Visão geral
Este curso é uma introdução à aplicação de redes neurais em problemas reais usando o software R-project.
14 horas
Visão geral
Este curso de treinamento é para pessoas que gostariam de aplicar o Machine Learning de forma pratica, o objetivo do treinamento é fornecer as ferramentas essenciais para a aplicaçao pratica e cotidiana dos conhecimentos em Machine Learning.
É um curso que vai dirigido à cientístas de dados e estatísticos que tem alguma familiarização com estatísticas e como programar em R (ou Python ou outra linguagem a sua escolha). A enfase deste curso é em aspectos práticos da preparação do modelo de dadosm execução, análise post hoc e visualização.
É um curso que vai dirigido à cientístas de dados e estatísticos que tem alguma familiarização com estatísticas e como programar em R (ou Python ou outra linguagem a sua escolha). A enfase deste curso é em aspectos práticos da preparação do modelo de dadosm execução, análise post hoc e visualização.
21 horas
Visão geral
Rede Neural Artificial é um modelo computacional de dados utilizado no desenvolvimento de sistemas de Inteligência Artificial (IA) capazes de realizar tarefas "inteligentes". Redes neurais são comumente usadas em aplicações de Aprendizado de Máquina (ML), que são elas mesmas uma implementação de IA. Deep Learning é um subconjunto do ML.
21 horas
Visão geral
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 horas
Visão geral
Este curso é criado para pessoas que nao tem nenhuma experiência com probabilidade e estatística, e o objetivo é proporcionar todas as ferramentas sumamente necessárias para que os participates tenham as capacidades e conhecimentos iniciais estatísticos e probabilísticos para enfrentar os problemas organizacionais.
14 horas
Visão geral
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 horas
Visão geral
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 horas
Visão geral
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 horas
Visão geral
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 horas
Visão geral
In this instructor-led, live training in Brasil, 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 horas
Visão geral
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 horas
Visão geral
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 horas
Visão geral
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 horas
Visão geral
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 horas
Visão geral
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 horas
Visão geral
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
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