Навучальныя курсы Neural Networks

Гэта 4-дзённы курс, які ўводзіць AI і яе заяўку з дапамогай Python праграмнай мовы. У будучыні гэта можа стаць выдатным і паспяховым бізнесам.

Глыбіня Reinforcement Learning звяртаецца на магчымасць & quot; артыфікальнага агенту" навучэнне працэсу і памылкі і начар. Масавічны агент мае эмуляцыю чалавека ' зможнасці атрымаць і стварыць веды самае, несапраўдна з сурага ўводаў, як гляд. Для зразумевання падтрымкі навучэння, выкарыстоўваюцца глыбокі навучэнне і нерўныя сеткі. Перацягнуць вывучэнне аднакто ад машынаў, і не задаецца на наглядзе і непраглядзеныя падходы навучэння.У гэтым інструктарам, жывым вучэннем, удзельнікі будуць навучаць основы глыбокі Reinforcement Learning, калі яны працягнуць праз стварэнне Deep Learning Агента.Да канца гэтага прывучэння удзельнікі будуць магчыма:

Зразумець ключы канцепцыі за Глыбіня Reinforcement Learning і быць магчыма адрозненне яго ад Machine Learning Ужыць пашыраныя алгарытмы Reinforcement Learning для вырашэння праблемы рэальнага свету Пабудаваць Deep Learning Агент

Аўдыёмнасць

Распрацоўшчыкі навуковых дадзеных

Фармат курса

Часткая лекцыя, часткавыя працэсы, працэсы і цяжкія рукі на практыку

Гэта курс быў створаны для кіраўнікоў, архітэктораў рашэнняў, інновацыйных афіцый, ЦТР, архітэктораў праграмы і кожнага, хто зацікаўлены ў паглядзе прыкладнага штучнага разумення і найбліжэйшага прагнозу для яго развіцця.

The training is aimed at people who want to learn the basics of neural networks and their applications.

This course is an introduction to applying neural networks in real world problems using R-project software.

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.

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.

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.

This course is created for people who have no previous experience in probability and statistics.

Гэта курс пакрывае AI (эмфазіруючы Machine Learning і Deep Learning) у Automotive Індустрыі. Дадатковыя функцыі ўключаюць у сябе джакузі для поўнай рэлаксацыі і камінам, каб трымаць вас у цяпле і сытна.

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 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.

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.

In this instructor-led, live training in Беларусь, 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.

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.

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

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

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

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 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

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

This classroom based training session will contain presentations and computer based examples and case study exercises to undertake with relevant neural and deep network libraries

This is a 4 day course introducing AI and it's application. There is an option to have an additional day to undertake an AI project on completion of this course. 

This instructor-led, live training in Беларусь (online or onsite) is aimed at engineers who wish to learn about the applicability of artificial intelligence to mechatronic systems. By the end of this training, participants will be able to:

• Gain an overview of artificial intelligence, machine learning, and computational intelligence.
• Understand the concepts of neural networks and different learning methods.
• Choose artificial intelligence approaches effectively for real-life problems.
• Implement AI applications in mechatronic engineering.

«Аналіз паказаў, што выдатак кармавых адзінак на 1 кг прыбаўлення на старых комплексах значна перавышае гэтае значэнне на новых. Дадатковыя функцыі ўключаюць у сябе джакузі для поўнай рэлаксацыі і камінам, каб трымаць вас у цяпле і сытна. Гэта інструктар-праведзены, жывы трэнінг (онлайн або на сайце) накіраваны да дадзеных навукоўцаў, якія хочуць выкарыстоўваць Python для будаўніцтва рэкамендацыйных сістэмаў. У канцы гэтага трэніру ўдзельнікі зможаць:

Стварыць рэкамендацыйныя сістэмы на шкале. Выкарыстоўвайце калектыўны фільтр для будаўніцтва рэкамендацыйных сістэмаў. Узнагароджанне Apache Spark для распрацоўкі рэкамендацыйных сістэмаў на кластерах. Стварыце рамку для выпрабавання алгоритмаў рэкамендацыі з Python.

Формат курса

Інтэрактыўныя лекцыі і дискусіі. Многія практыкаванні і практыкаванні. Вынікі ў Live-Lab Environment.

Вынікі пошуку - Customization options

Калі вы хочаце падзяліцца сваёй думкай з майстрам, рабіце гэта максімальна ветліва.

This instructor-led, live training in Беларусь (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while making the code easy to debug. By the end of this training, participants will be able to:

• Set up the necessary development environment to start developing neural network models.
• Define and implement neural network models using a comprehensible source code.
• Execute examples and modify existing algorithms to optimize deep learning training models while leveraging GPUs for high performance.

У гэтым інструктар-праведзены, жывы трэнінг, мы ідуць над правіламі нейральных сетак і выкарыстоўваць OpenNN для ажыццяўлення прыкладання зразка. Формат курса

Узнагароджанне і ўзнагароджанне ў дадзеным выпадку.