由讲师进行实时指导的人工智能本地培训课程通过动手实践演示如何实施人工智能解决方案以解决实际问题。
人工智能培训形式包括“现场实时培训”和“远程实时培训”。现场实时培训可在客户位于中国的所在场所或NobleProg位于中国的企业培训中心进行，远程实时培训可通过交互式远程桌面进行。
NobleProg -- 您的本地培训提供商
客户评论
都很喜欢
蒙 李
课程: Machine Learning Fundamentals with Python
与讲师的交流环节
文欣 张
课程: Artificial Neural Networks, Machine Learning, Deep Thinking
都喜欢
lisa xie
课程: Artificial Neural Networks, Machine Learning, Deep Thinking
我喜欢实例讲解
AUO友达光电（苏州）有限公司
课程: OptaPlanner in Practice
人间识别和电路板坏点检测
王 春柱 - 中移物联网
课程: Deep Learning for NLP (Natural Language Processing)
演示
中移物联网
课程: Deep Learning for NLP (Natural Language Processing)
About face area.
中移物联网
课程: Deep Learning for NLP (Natural Language Processing)
他的信息非常丰富，乐于助人。
Pratheep Ravy
课程: Predictive Modelling with R
Machine Translated
它非常互动，比预期更轻松和非正式。我们在当时涵盖了很多主题，培训师总是乐于接受更详细的讨论，或者更广泛地讨论主题及其相关方式。我觉得培训给了我继续学习的工具，相反，它是一次性会议，一旦你完成学习就会停止，这对于主题的规模和复杂性非常重要。
Jonathan Blease
课程: Artificial Neural Networks, Machine Learning, Deep Thinking
Machine Translated
安创造了一个提问和学习的好环境。我们有很多乐趣，同时也学到了很多东西。
Gudrun Bickelq
课程: Introduction to the use of neural networks
Machine Translated
交互式部分，根据我们的特定需求量身定制。
Thomas Stocker
课程: Introduction to the use of neural networks
Machine Translated
我确实喜欢这些练习。
Office for National Statistics
课程: Natural Language Processing with Python
Machine Translated
我真的很喜欢亲自动手的方法。
Kevin De Cuyper
课程: Computer Vision with OpenCV
Machine Translated
材料范围
Maciej Jonczyk
课程: From Data to Decision with Big Data and Predictive Analytics
Machine Translated
系统化ML领域的知识
Orange Polska
课程: From Data to Decision with Big Data and Predictive Analytics
Machine Translated
培训师知识渊博，包括我感兴趣的领域。
Mohamed Salama
课程: Data Mining & Machine Learning with R
Machine Translated
这个话题非常有趣。
Wojciech Baranowski
课程: Introduction to Deep Learning
Machine Translated
培训师的理论知识和培训后与参与者解决问题的意愿。
Grzegorz Mianowski
课程: Introduction to Deep Learning
Machine Translated
话题。很有意思！。
Piotr
课程: Introduction to Deep Learning
Machine Translated
每个主题后的练习都非常有用，尽管最后太复杂了。一般来说，所提供的材料非常有趣并涉及！图像识别练习很棒。
Dolby Poland Sp. z o.o.
课程: Introduction to Deep Learning
Machine Translated
我认为，如果培训是在波兰语中完成的，那么培训师就可以更有效地分享他的知识。
Radek
课程: Introduction to Deep Learning
Machine Translated
深度学习的全球概述。
Bruno Charbonnier
课程: Advanced Deep Learning
Machine Translated
这些练习非常实用，不需要Python的高级知识。
Alexandre GIRARD
课程: Advanced Deep Learning
Machine Translated
使用Eras对实例进行练习。意大利完全理解我们对此培训的期望。
Paul Kassis
课程: Advanced Deep Learning
Machine Translated
我真的很感激克里斯对我们问题的明确答案。
Léo Dubus
课程: Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple
Machine Translated
我一般都很喜欢知识渊博的教练。
Sridhar Voorakkara
课程: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
我对这门课程的标准感到惊讶 - 我会说它是大学标准。
David Relihan
课程: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
非常好的全面概述。 Go OD背景到原因Tensorflow工作，因为它确实。
Kieran Conboy
课程: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
我喜欢有机会提出问题并对理论进行更深入的解释。
Sharon Ruane
课程: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
我们对这个主题有了更多的了解。我们公司内部的一些真实主题进行了一些很好的讨论。
Sebastiaan Holman
课程: Machine Learning and Deep Learning
Machine Translated
通过展示理论与实践如何相辅相成，培训提供了正确的基础，使我们能够进一步扩展。它实际上让我对这个主题比以前更感兴趣。
Jean-Paul van Tillo
课程: Machine Learning and Deep Learning
Machine Translated
我非常喜欢主题的报道和深度。
Anirban Basu
课程: Machine Learning and Deep Learning
Machine Translated
培训师很容易解释困难和高级话题。
Leszek K
课程: Artificial Intelligence Overview
Machine Translated
我喜欢深度机器学习的新见解。
Josip Arneric
课程: Neural Network in R
Machine Translated
我们获得了一些关于NN的知识，对我来说最有趣的是现在流行的新型NN。
Tea Poklepovic
课程: Neural Network in R
Machine Translated
我最喜欢R :)））中的图表。
Faculty of Economics and Business Zagreb
课程: Neural Network in R
Machine Translated
关于该主题的培训师的深刻知识。
Sebastian Görg
课程: Introduction to Deep Learning
Machine Translated
非常更新的方法或CPI（张量流，时代，学习）做机器学习。
Paul Lee
课程: TensorFlow for Image Recognition
Machine Translated
鉴于技术前景：未来哪种技术/流程可能变得更加重要;看，这项技术可以用于什么。
Commerzbank AG
课程: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
我从主题选择中受益。训练风格。练习方向。
Commerzbank AG
课程: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
教练的指导和举例
ORANGE POLSKA S.A.
课程: Machine Learning and Deep Learning
Machine Translated
可以自己讨论提议的问题。
ORANGE POLSKA S.A.
课程: Machine Learning and Deep Learning
Machine Translated
深入报道机器学习主题，特别是神经网络。揭开了很多话题的神秘面纱。
Sacha Nandlall
课程: Python for Advanced Machine Learning
Machine Translated
Artificial Intelligence课程大纲
课程名称
课程时长
课程概览
课程名称
课程时长
课程概览
7小时
课程概览
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小时
课程概览
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小时
课程概览
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小时
课程概览
This is a classroom based training session in a presentation and Q&A format
14小时
课程概览
This instructor-led, live training in 中国 (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小时
课程概览
This instructor-led, live training in 中国 (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小时
课程概览
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小时
课程概览
This instructor-led, live training in 中国 (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小时
课程概览
This instructor-led, live training in 中国 (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小时
课程概览
This instructor-led, live training in 中国 (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小时
课程概览
This instructor-led, live training in 中国 (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小时
课程概览
In this instructor-led, live training in 中国 (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小时
课程概览
In this instructor-led, live training in 中国 (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小时
课程概览
The training is aimed at people who want to learn the basics of neural networks and their applications.
14小时
课程概览
This course is an introduction to applying neural networks in real world problems using R-project software.
14小时
课程概览
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小时
课程概览
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小时
课程概览
This course is created for people who have no previous experience in probability and statistics.
14小时
课程概览
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小时
课程概览
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小时
课程概览
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小时
课程概览
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.
21小时
课程概览
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小时
课程概览
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小时
课程概览
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小时
课程概览
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小时
课程概览
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小时
课程概览
在这一由讲师引导的现场培训中，参与者将学习如何使用Matlab来设计、构建、可视化用于图像识别的卷积神经网络。
在培训结束后，参与者将能够：
- 建立深度学习的模式
- 使数据分类自动化
- 使用Caffe和TensorFlow-Keras的模型
- 使用多个GPU、云或群集训练数据
受众
- 开发人员
- 工程师
- 领域专家
课程形式
- 部分讲座、部分讨论、练习和大量实操
在培训结束后，参与者将能够：
- 建立深度学习的模式
- 使数据分类自动化
- 使用Caffe和TensorFlow-Keras的模型
- 使用多个GPU、云或群集训练数据
受众
- 开发人员
- 工程师
- 领域专家
课程形式
- 部分讲座、部分讨论、练习和大量实操
35小时
课程概览
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
近期人工智能培训课程
08-10-2021 09:30
上海 - 上海中区广场
7 小时
25-10-2021 09:30
上海 - 上海中区广场
28 小时
27-10-2021 09:30
上海 - 上海中区广场
7 小时
09-11-2021 09:30
上海 - 上海中区广场
14 小时
17-11-2021 09:30
上海 - 上海中区广场
21 小时
Artificial Intelligence,培训,课程,培训课程, 企业AI (Artificial Intelligence)培训, 短期AI培训, AI课程, 人工智能周末培训, AI (Artificial Intelligence)晚上培训, 人工智能训练, 学习Artificial Intelligence, AI (Artificial Intelligence)老师, 学人工智能班, AI远程教育, 一对一AI (Artificial Intelligence)课程, 小组Artificial Intelligence课程, AI培训师, AI (Artificial Intelligence)辅导班, Artificial Intelligence教程, AI私教, Artificial Intelligence辅导, AI (Artificial Intelligence)讲师AI,培训,课程,培训课程, 企业AI培训, 短期Artificial Intelligence培训, Artificial Intelligence课程, AI周末培训, Artificial Intelligence晚上培训, AI (Artificial Intelligence)训练, 学习AI, Artificial Intelligence老师, 学Artificial Intelligence (AI)班, AI (Artificial Intelligence)远程教育, 一对一Artificial Intelligence课程, 小组Artificial Intelligence (AI)课程, Artificial Intelligence培训师, AI (Artificial Intelligence)辅导班, AI教程, AI私教, Artificial Intelligence (AI)辅导, AI (Artificial Intelligence)讲师
.jpg "european molecular biology laboratory (embl)")
.jpg "european organization for nuclear research (cern)")
.jpg "international air transport association (iata)")
