Jelenia Góra

Pałac Wojanów, Wojanów 8, Jelenia Góra, Polska, 58-508

Wrocław

NobleProg, Ludwika Rydygiera 2a/22, Wroclaw, Polska, 50-249
Lokal znajduje się w samym centrum miasta (vis-à-vis hotelu HP Park Plaza), zaledwie 10 minut spacerem od Rynku, tuż przy ...

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Applied AI from Scratch in Python

28 godzin
This is a 4 day course introducing AI and it's application using the Python programming language. There is an option to have an additional day to undertake an AI project on completion of this course. 
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Deep Reinforcement Learning with Python

21 godzin
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. This instructor-led, live training (online or onsite) is aimed at developers and data scientists who wish to 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.
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
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Artificial Intelligence Overview

7 godzin
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.
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Artificial Intelligence (AI) in Automotive

14 godzin
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.
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From Zero to AI

35 godzin
This course is created for people who have no previous experience in probability and statistics.
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Artificial Neural Networks, Machine Learning, Deep Thinking

21 godzin
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.
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Applied AI from Scratch

28 godzin
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. 
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Applied Machine Learning

14 godzin
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.
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Deep Learning Neural Networks with Chainer

14 godzin
Chainer is an open source framework based on Python, built for accelerating research and implementing neural network models. It provides flexible, efficient, and simplified approaches to developing deep learning algorithms. This instructor-led, live training (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.
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
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Pattern Recognition

21 godzin
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.
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Introduction Deep Learning & Réseaux de neurones pour l’ingénieur

21 godzin
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.
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Artificial Intelligence (AI) for Mechatronics

21 godzin
Mechatronics (a.k.a. mechatronic engineering) is a combination of mechanical, electronics and computer science. This instructor-led, live training (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.
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
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Introduction to the use of neural networks

7 godzin
Szkolenie skierowane jest do osób, które chcą zapoznać się z podstawami sieci neuronowych oraz ich zastosowań.
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Neural Networks Fundamentals using TensorFlow as Example

28 godzin
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.
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Neural computing – Data science

14 godzin
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
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PaddlePaddle

21 godzin
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
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Recommender Systems with Python

14 godzin
A recommender system is an information filtering process that predicts the user's preferences. Python can be used to program deep learning, machine learning, and neural network recommender systems to help users discover new products and content. This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use Python to build recommender systems. By the end of this training, participants will be able to:
  • Create recommender systems at scale.
  • Apply collaborative filtering to build recommender systems.
  • Use Apache Spark to compute recommender systems on clusters.
  • Build a framework to test recommendation algorithms with Python.
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
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Sieci Neuronowe w R

14 godzin
Szkolenie jest wprowadzeniem do wdrożenia sieci neuronowych w życiu codziennym wykorzystując oprogramowanie R-project.
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TPU Programming: Building Neural Network Applications on Tensor Processing Units

7 godzin
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.
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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Understanding Deep Neural Networks

35 godzin
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
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OpenNN: Implementing Neural Networks

14 godzin
In this instructor-led, live training, we go over the principles of neural networks and use OpenNN to implement a sample application.
Format of the course
  • Lecture and discussion coupled with hands-on exercises.
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Opinie uczestników (10)

Trener bardzo zrozumiale wytłumaczył trudne i zaawansowane tematy.

Leszek K
Szkolenie - Artificial Intelligence Overview

Working from first principles in a focused way, and moving to applying case studies within the same day

Maggie Webb - Department of Jobs, Regions, and Precincts
Szkolenie - Artificial Neural Networks, Machine Learning, Deep Thinking

It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.

Jonathan Blease
Szkolenie - Artificial Neural Networks, Machine Learning, Deep Thinking

That it was applying real company data. Trainer had a very good approach by making trainees participate and compete

Jimena Esquivel - Zakład Usługowy Hakoman Andrzej Cybulski
Szkolenie - Applied AI from Scratch in Python

The trainer was a professional in the subject field and related theory with application excellently

Fahad Malalla - Tatweer Petroleum
Szkolenie - Applied AI from Scratch in Python

Świetny kontakt z uczestnikami, wiedza praktyczna co bardzo się ceni. Dostosowanie toku / tempa. Duuuży plus, mega pozytywny instruktor, aż szkoda że szkolenie trwało tylko 2 dni.

Marcin Mikielewicz - TECNOBIT SLU
Szkolenie - Introduction Deep Learning & Réseaux de neurones pour l’ingénieur

It felt like we were going through directly relevant information at a good pace (i.e. no filler material)

Maggie Webb - Department of Jobs, Regions, and Precincts
Szkolenie - Introduction to the use of neural networks

the interactive part, tailored to our specific needs

Thomas Stocker
Szkolenie - Introduction to the use of neural networks

dużo ćwiczeń, które bezpośrednio mogę wykorzystać w mojej pracy

Alior Bank S.A.
Szkolenie - Sieci Neuronowe w R

Tematyka DL nie jest mi obca, udało się poznac kilka optymalizacyjnych smaczków.

Marcin Staśko - LG Energy Solution Wrocław Sp. z o.o.
Szkolenie - Understanding Deep Neural Networks

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Sieci Neuronowe Konsultacje
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