Szkolenia Sieci Neuronowe
Lokalne, prowadzone przez instruktaże kursy szkoleniowe na żywo w sieci neuronowej pokazują poprzez interaktywną dyskusję i praktyczne ćwiczenia dotyczące budowy sieci neuronowych przy użyciu wielu narzędzi i bibliotek opartych na otwartym oprogramowaniu, jak również jak wykorzystać moc zaawansowanych urządzeń (GPU) i technik optymalizacji związanych z przetwarzaniem rozproszonym i duże dane Nasze kursy neuronowe oparte są na popularnych językach programowania, takich jak język Python, Java, R i potężne biblioteki, w tym TensorFlow, Latarka, Caffe, Theano i inne Nasze kursy neuronowe obejmują zarówno teorię, jak i implementację, wykorzystując szereg implementacji sieci neuronowych, takich jak Deep Neural Networks (DNN), Convolutional Neural Networks (CNN) i Recurrent Neural Networks (RNN) Szkolenie z sieci neuronowej jest dostępne jako "szkolenie na miejscu" lub "szkolenie na żywo" Szkolenie na żywo w siedzibie klienta może odbywać się lokalnie w siedzibie klienta w Polsce lub w centrach szkoleniowych korporacji NobleProg w Polsce Zdalne szkolenie na żywo odbywa się za pomocą interaktywnego, zdalnego pulpitu NobleProg Twój lokalny dostawca szkoleń.
Opinie uczestników
zakres materialu
Maciej Jonczyk
Nazwa kursu: From Data to Decision with Big Data and Predictive Analytics
usystematyzowanie wiedzy z dziedziny ML
Orange Polska
Nazwa kursu: From Data to Decision with Big Data and Predictive Analytics
Trener bardzo zrozumiale wytłumaczył trudne i zaawansowane tematy.
Leszek K
Nazwa kursu: Artificial Intelligence Overview
dużo ćwiczeń, które bezpośrednio mogę wykorzystać w mojej pracy
Alior Bank S.A.
Nazwa kursu: Sieci Neuronowe w R
Przykłady na danych rzeczywistych
Alior Bank S.A.
Nazwa kursu: Sieci Neuronowe w R
neuralnet, pROC w petli
Alior Bank S.A.
Nazwa kursu: Sieci Neuronowe w R
ref materiał do wykorzystania później był bardzo dobry.
PAUL BEALES- Seagate Technology.
Nazwa kursu: Applied Machine Learning
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Co najbardziej podobało Ci się w treningu ?: Dałem mi dobrą praktykę z użyciem R do budowania systemów uczenia maszynowego w rzeczywistych sytuacjach. Mogę użyć tego w mojej pracy od razu. What d
Nazwa kursu: Applied Machine Learning
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To było bardzo interaktywne i bardziej zrelaksowane i nieformalne niż się spodziewano. Omówiliśmy wiele tematów w tym czasie, a trener zawsze był otwarty na bardziej szczegółowe omówienie tematów i ich powiązanie. Uważam, że szkolenie dało mi narzędzia do kontynuacji nauki, a nie jednorazową sesję, w której nauka kończy się po ukończeniu, co jest bardzo ważne ze względu na skalę i złożoność tematu.
Jonathan Blease
Nazwa kursu: Artificial Neural Networks, Machine Learning, Deep Thinking
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Ann stworzyła świetne środowisko do zadawania pytań i uczenia się. Mieliśmy dużo zabawy, a także wiele się nauczyliśmy w tym samym czasie. Gudrun
Gudrun Bickelq
Nazwa kursu: Introduction to the use of neural networks
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Ann stworzyła świetne środowisko do zadawania pytań i uczenia się. Mieliśmy dużo zabawy, a także wiele się nauczyliśmy w tym samym czasie. Gudrun
Gudrun Bickelq
Nazwa kursu: Introduction to the use of neural networks
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część interaktywna, dostosowana do naszych konkretnych potrzeb
Thomas Stocker
Nazwa kursu: Introduction to the use of neural networks
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ref materiał do wykorzystania później był bardzo dobry
PAUL BEALES
Nazwa kursu: Applied Machine Learning
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Temat jest bardzo interesujący
Wojciech Baranowski
Nazwa kursu: Introduction to Deep Learning
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Trenerzy wiedzą teoretyczną i chęć rozwiązania problemów z uczestnikami po szkoleniu Gr
Grzegorz Mianowski
Nazwa kursu: Introduction to Deep Learning
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Temat. Bardzo interesujące!
Piotr
Nazwa kursu: Introduction to Deep Learning
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Ćwiczenia po każdym temacie były naprawdę pomocne, mimo że na końcu były zbyt skomplikowane. Ogólnie rzecz biorąc, prezentowany materiał był bardzo ciekawy i angażujący! Ćwiczenia z rozpoznawaniem obrazu były świetne. - Dolby Po
Dolby Poland Sp. z o.o.
Nazwa kursu: Introduction to Deep Learning
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Myślę, że gdyby szkolenie odbyło się w języku polskim, pozwoliłoby trenerowi na dzielenie się wiedzą
Radek
Nazwa kursu: Introduction to Deep Learning
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temat. wydawało się to interesujące, ale opuściłem wiedząc niewiele więcej niż wcześniej. Rados
Radoslaw Labedzki
Nazwa kursu: Introduction to Deep Learning
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Interesujący temat
Wojciech Wilk
Nazwa kursu: Introduction to Deep Learning
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Naprawdę doceniam krystalicznie czyste odpowiedzi Chrisa na nasze pytania.
Léo Dubus
Nazwa kursu: Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple
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Doświadczony trener
Sridhar Voorakkara
Nazwa kursu: Neural Networks Fundamentals using TensorFlow as Example
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Byłem zdumiony standardem tej klasy, powiedziałbym, że był to standard uniwersytecki.
David Relihan
Nazwa kursu: Neural Networks Fundamentals using TensorFlow as Example
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Bardzo dobry przegląd ogólny. Dobre tło, dlaczego Tensorflow działa tak, jak działa. K
Kieran Conboy
Nazwa kursu: Neural Networks Fundamentals using TensorFlow as Example
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Spodobały mi się możliwości zadawania pytań i dokładniejszego wyjaśnienia teorii. Sha
Sharon Ruane
Nazwa kursu: Neural Networks Fundamentals using TensorFlow as Example
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nowe spostrzeżenia w głębokim uczeniu maszynowym
Josip Arneric
Nazwa kursu: Neural Network in R
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Zdobyliśmy trochę wiedzy na temat NN w ogóle, a najbardziej interesujące dla mnie były nowe typy NN, które są obecnie popularne. Tea
Tea Poklepovic
Nazwa kursu: Neural Network in R
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Wykresy w R :)))
Faculty of Economics and Business Zagreb
Nazwa kursu: Neural Network in R
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Głęboka wiedza trenera na ten temat.
Sebastian Görg
Nazwa kursu: Introduction to Deep Learning
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Bardzo elastyczny
Frank Ueltzhöffer
Nazwa kursu: Artificial Neural Networks, Machine Learning and Deep Thinking
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elastyczność
Werner Philipp
Nazwa kursu: Artificial Neural Networks, Machine Learning and Deep Thinking
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Perspektywa technologii: jaka technologia / proces może stać się ważniejsza w przyszłości; zobacz, do czego można wykorzystać tę technologię Commer
Commerzbank AG
Nazwa kursu: Neural Networks Fundamentals using TensorFlow as Example
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Wybór tematu. Styl szkolenia. Poćwicz orientację
Commerzbank AG
Nazwa kursu: Neural Networks Fundamentals using TensorFlow as Example
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Objaśnienie papieru
Nazwa kursu: Deep Learning with TensorFlow - bespoke
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Komunikacja z wykładowcami
文欣 张
Nazwa kursu: Artificial Neural Networks, Machine Learning, Deep Thinking
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Podoba się
lisa xie
Nazwa kursu: Artificial Neural Networks, Machine Learning, Deep Thinking
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Styl treningowy Richarda był interesujący, przykłady z prawdziwego świata pomogły wprowadzić koncepcje do domu ...
Jamie Martin-Royle - NBrown Group
Nazwa kursu: From Data to Decision with Big Data and Predictive Analytics
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Treść, ponieważ uważam ją za bardzo interesującą i myślę, że pomogłaby mi w ostatnim roku studiów na uniwersytecie.
Krishan Mistry - NBrown Group
Nazwa kursu: From Data to Decision with Big Data and Predictive Analytics
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Plany Kursów
| Kod | Nazwa | Czas trwania | Charakterystyka kursu |
|---|---|---|---|
| aiint | Artificial Intelligence Overview | 7 godz. | Kurs ten został stworzony dla menadżerów, architektów, analityków biznesowych i systemowych, menedżerów oprogramowania oraz wszystkich zainteresowanych przeglądem stosowania sztucznej inteligencji i prognozą dla jej rozwoju. |
| Neuralnettf | Neural Networks Fundamentals using TensorFlow as Example | 28 godz. | 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 choosing the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow. |
| dlforbankingwithpython | Deep Learning for Banking (with Python) | 28 godz. | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for banking using Python as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to: Understand the fundamental concepts of deep learning Learn the applications and uses of deep learning in banking Use Python, Keras, and TensorFlow to create deep learning models for banking Build their own deep learning credit risk model using Python Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| neuralnet | Introduction to the use of neural networks | 7 godz. | Szkolenie skierowane jest do osób, które chcą zapoznać się z podstawami sieci neuronowych oraz ich zastosowań. |
| datamodeling | Pattern Recognition | 35 godz. | This 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. Audience Data analysts PhD students, researchers and practitioners |
| dlforfinancewithpython | Deep Learning for Finance (with Python) | 28 godz. | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for finance using Python as they step through the creation of a deep learning stock price prediction model. By the end of this training, participants will be able to: Understand the fundamental concepts of deep learning Learn the applications and uses of deep learning in finance Use Python, Keras, and TensorFlow to create deep learning models for finance Build their own deep learning stock price prediction model using Python Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| rneuralnet | Sieci Neuronowe w R | 14 godz. | Szkolenie jest wprowadzeniem do wdrożenia sieci neuronowych w życiu codziennym wykorzystując oprogramowanie R-project. |
| Torch | Torch: Getting started with Machine and Deep Learning | 21 godz. | Torch is an open source machine learning library and a scientific computing framework based on the Lua programming language. It provides a development environment for numerics, machine learning, and computer vision, with a particular emphasis on deep learning and convolutional nets. It is one of the fastest and most flexible frameworks for Machine and Deep Learning and is used by companies such as Facebook, Google, Twitter, NVIDIA, AMD, Intel, and many others. In this course we cover the principles of Torch, its unique features, and how it can be applied in real-world applications. We step through numerous hands-on exercises all throughout, demonstrating and practicing the concepts learned. By the end of the course, participants will have a thorough understanding of Torch's underlying features and capabilities as well as its role and contribution within the AI space compared to other frameworks and libraries. Participants will have also received the necessary practice to implement Torch in their own projects. Audience Software developers and programmers wishing to enable Machine and Deep Learning within their applications Format of the course Overview of Machine and Deep Learning In-class coding and integration exercises Test questions sprinkled along the way to check understanding |
| drlpython | Deep Reinforcement Learning with Python | 21 godz. | Deep Reinforcement Learning refers to the ability of an "artificial agent" to learn by trial-and-error and rewards-and-punishments. An artificial agent aims to emulate a human's ability to obtain and construct knowledge on its own, directly from raw inputs such as vision. To realize reinforcement learning, deep learning and neural networks are used. Reinforcement learning is different from machine learning and does not rely on supervised and unsupervised learning approaches. In this instructor-led, live training, participants will learn the fundamentals of Deep Reinforcement Learning as they step through the creation of a Deep Learning Agent. By the end of this training, participants will be able to: Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning Apply advanced Reinforcement Learning algorithms to solve real-world problems Build a Deep Learning Agent Audience Developers Data Scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| MLFWR1 | Machine Learning Fundamentals with R | 14 godz. | The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the R programming platform and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications. |
| OpenNN | OpenNN: Implementing neural networks | 14 godz. | OpenNN is an open-source class library written in C++ which implements neural networks, for use in machine learning. In this course we go over the principles of neural networks and use OpenNN to implement a sample application. Audience Software developers and programmers wishing to create Deep Learning applications. Format of the course Lecture and discussion coupled with hands-on exercises. |
| appliedml | Applied Machine Learning | 14 godz. | 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. |
| opennmt | OpenNMT: Setting up a Neural Machine Translation System | 7 godz. | OpenNMT is a full-featured, open-source (MIT) neural machine translation system that utilizes the Torch mathematical toolkit. In this training participants will learn how to set up and use OpenNMT to carry out translation of various sample data sets. The course starts with an overview of neural networks as they apply to machine translation. Participants will carry out live exercises throughout the course to demonstrate their understanding of the concepts learned and get feedback from the instructor. By the end of this training, participants will have the knowledge and practice needed to implement a live OpenNMT solution. Source and target language samples will be pre-arranged per the audience's requirements. Audience Localization specialists with a technical background Global content managers Localization engineers Software developers in charge of implementing global content solutions Format of the course Part lecture, part discussion, heavy hands-on practice |
| mlintro | Introduction to Machine Learning | 7 godz. | This training course is for people that would like to apply basic Machine Learning techniques in practical applications. Audience Data scientists and statisticians that have some familiarity with machine learning and know how to program R. 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 a practical introduction 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. |
| Fairsec | Fairsec: Setting up a CNN-based machine translation system | 7 godz. | Fairseq is an open-source sequence-to-sequence learning toolkit created by Facebok for use in Neural Machine Translation (NMT). In this training participants will learn how to use Fairseq to carry out translation of sample content. By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution. Source and target language content samples can be prepared according to audience's requirements. Audience Localization specialists with a technical background Global content managers Localization engineers Software developers in charge of implementing global content solutions Format of the course Part lecture, part discussion, heavy hands-on practice |
| d2dbdpa | From Data to Decision with Big Data and Predictive Analytics | 21 godz. | Audience If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you. It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing. It is not aimed at people configuring the solution, those people will benefit from the big picture though. Delivery Mode During the course delegates will be presented with working examples of mostly open source technologies. Short lectures will be followed by presentation and simple exercises by the participants Content and Software used All software used is updated each time the course is run so we check the newest versions possible. It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with machine learning. |
| Fairseq | Fairseq: Setting up a CNN-based machine translation system | 7 godz. | Fairseq is an open-source sequence-to-sequence learning toolkit created by Facebok for use in Neural Machine Translation (NMT). In this training participants will learn how to use Fairseq to carry out translation of sample content. By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution. Audience Localization specialists with a technical background Global content managers Localization engineers Software developers in charge of implementing global content solutions Format of the course Part lecture, part discussion, heavy hands-on practice Note If you wish to use specific source and target language content, please contact us to arrange. |
| annmldt | Artificial Neural Networks, Machine Learning, Deep Thinking | 21 godz. | 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. |
| facebooknmt | Facebook NMT: Setting up a Neural Machine Translation System | 7 godz. | Fairseq is an open-source sequence-to-sequence learning toolkit created by Facebok for use in Neural Machine Translation (NMT). In this training participants will learn how to use Fairseq to carry out translation of sample content. By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution. Audience Localization specialists with a technical background Global content managers Localization engineers Software developers in charge of implementing global content solutions Format of the course Part lecture, part discussion, heavy hands-on practice Note If you wish to use specific source and target language content, please contact us to arrange. |
| deeplearning1 | Introduction to Deep Learning | 21 godz. | This course is general overview for Deep Learning without going too deep into any specific methods. It is suitable for people who want to start using Deep learning to enhance their accuracy of prediction. |
| tpuprogramming | TPU Programming: Building Neural Network Applications on Tensor Processing Units | 7 godz. | The Tensor Processing Unit (TPU) is the architecture which Google has used internally for several years, and is just now becoming available for use by the general public. It includes several optimizations specifically for use in neural networks, including streamlined matrix multiplication, and 8-bit integers instead of 16-bit in order to return appropriate levels of precision. In this instructor-led, live training, 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 Audience Developers Researchers Engineers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| sysagent | Systemy wieloagentowe | 7 godz. | |
| MicrosoftCognitiveToolkit | Microsoft Cognitive Toolkit 2.x | 21 godz. | 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. |
| pjn | Przetwarzanie języka naturalnego | 7 godz. | |
| snorkel | Snorkel: Rapidly process training data | 7 godz. | 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 |
| iop | Inteligencja obliczeniowa w praktyce | 7 godz. | |
| encogintro | Encog: Introduction to Machine Learning | 14 godz. | 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 |
| mtdintob | Metody Inteligencji Obliczeniowej | 7 godz. | |
| encogadv | Encog: Advanced Machine Learning | 14 godz. | 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 |
| cntk | Using Computer Network ToolKit (CNTK) | 28 godz. | Computer Network ToolKit (CNTK) is Microsoft's Open Source, Multi-machine, Multi-GPU, Highly efficent RNN training machine learning framework for speech, text, and images. Audience This course is directed at engineers and architects aiming to utilize CNTK in their projects. |
Upcoming Courses
| Szkolenie | Data Kursu | Cena szkolenia [Zdalne / Stacjonarne] |
|---|---|---|
| Using Computer Network ToolKit (CNTK) - Łódź, ul. Tatrzańska 11 | wt., 2018-07-03 09:00 | 6650PLN / 7900PLN |
| From Data to Decision with Big Data and Predictive Analytics - Lublin, ul. Spadochroniarzy 9 | wt., 2018-07-03 09:00 | 4990PLN / 5990PLN |
| Snorkel: Rapidly process training data - Olsztyn, ul. Gietkowska 6a | wt., 2018-07-03 09:00 | 1660PLN / 2160PLN |
| Fairseq: Setting up a CNN-based machine translation system - Katowice ul. Opolska 22 | śr., 2018-07-04 09:00 | 1660PLN / 2160PLN |
| Machine Learning Fundamentals with R - Rzeszów, Plac Wolności 13 | czw., 2018-07-05 09:00 | 3330PLN / 4080PLN |