Lokální instruktorem vedené Machine Learning školení České republice.
Reference
Topic selection. Style of training. Practice orientation
Commerzbank AG
Kurz: Neural Networks Fundamentals using TensorFlow as Example
All like it
蒙 李
Kurz: Machine Learning Fundamentals with Python
way of conducting and example given by the trainer
ORANGE POLSKA S.A.
Kurz: Machine Learning and Deep Learning
Possibility to discuss the proposed issues yourself
ORANGE POLSKA S.A.
Kurz: Machine Learning and Deep Learning
Communication with lecturers
文欣 张
Kurz: Artificial Neural Networks, Machine Learning, Deep Thinking
like it all
lisa xie
Kurz: Artificial Neural Networks, Machine Learning, Deep Thinking
In-depth coverage of machine learning topics, particularly neural networks. Demystified a lot of the topic.
Sacha Nandlall
Kurz: Python for Advanced Machine Learning
Opravdu jsem měl rád cvičení
L M ERICSSON LIMITED
Kurz: Machine Learning
Machine Translated
I liked the lab exercises.
Marcell Lorant - L M ERICSSON LIMITED
Kurz: Machine Learning
The Jupyter notebook form, in which the training material is available
L M ERICSSON LIMITED
Kurz: Machine Learning
There were many exercises and interesting topics.
L M ERICSSON LIMITED
Kurz: Machine Learning
Some great lab exercises analyzed and explained by the trainer in depth (e.g. covariants in linear regression, matching the real function)
L M ERICSSON LIMITED
Kurz: Machine Learning
It's just great that all material including the exercises is on the same page and then it gets updated on the fly. The solution is revealed at the end. Cool! Also, I do appreciate that Krzysztof took extra effort to understand our problems and suggested us possible techniques.
Attila Nagy - L M ERICSSON LIMITED
Kurz: Machine Learning
Big and up-to-date knowledge of leading and practical application examples.
ING Bank Śląski S.A.
Kurz: Introduction to Deep Learning
A lot of exercises, very good cooperation with the group.
Janusz Chrobot - ING Bank Śląski S.A.
Kurz: Introduction to Deep Learning
work on colaborators,
ING Bank Śląski S.A.
Kurz: Introduction to Deep Learning
It was obvious that the enthusiasts of the presented topics were leading. Used interesting examples during exercise.
ING Bank Śląski S.A.
Kurz: Introduction to Deep Learning
A wide range of topics covered and substantial knowledge of the leaders.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurz: Understanding Deep Neural Networks
Lack
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurz: Understanding Deep Neural Networks
Big theoretical and practical knowledge of the lecturers. Communicativeness of trainers. During the course, you could ask questions and get satisfactory answers.
Kamil Kurek - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurz: Understanding Deep Neural Networks
Practical part, where we implemented algorithms. This allowed for a better understanding of the topic.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurz: Understanding Deep Neural Networks
exercises and examples implemented on them
Paweł Orzechowski - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurz: Understanding Deep Neural Networks
Examples and issues discussed.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurz: Understanding Deep Neural Networks
Substantive knowledge, commitment, a passionate way of transferring knowledge. Practical examples after a theoretical lecture.
Janusz Chrobot - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurz: Understanding Deep Neural Networks
Practical exercises prepared by Mr. Maciej
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Kurz: Understanding Deep Neural Networks
I was benefit from the passion to teach and focusing on making thing sensible.
Zaher Sharifi - GOSI
Kurz: Advanced Deep Learning
Human identification and circuit board bad point detection
王 春柱 - 中移物联网
Kurz: Deep Learning for NLP (Natural Language Processing)
Demonstrate
中移物联网
Kurz: Deep Learning for NLP (Natural Language Processing)
About face area.
中移物联网
Kurz: Deep Learning for NLP (Natural Language Processing)
The informal exchanges we had during the lectures really helped me deepen my understanding of the subject
Explore
Kurz: Deep Reinforcement Learning with Python
It is showing many methods with pre prepared scripts- very nicely prepared materials & easy to traceback
Kamila Begej - GE Medical Systems Polska Sp. Zoo
Kurz: Machine Learning – Data science
I like that training was focused on examples and coding. I thought that it is impossible to pack so much content into three days of training, but I was wrong. Training covered many topics and everything was done in a very detailed manner (especially tuning of model's parameters - I didn't expected that there will be a time for this and I was gratly surprised).
Bartosz Rosiek - GE Medical Systems Polska Sp. Zoo
Kurz: Machine Learning – Data science
Alternation theory / practice effective!
CIRAD
Kurz: Introduction to Machine Learning with MATLAB
Progressive presentation and application of methods
Aurélien Briffaz - CIRAD
Kurz: Introduction to Machine Learning with MATLAB
Availability and adaptability, answers to questions
Jean-Michel MEOT - CIRAD
Kurz: Introduction to Machine Learning with MATLAB
Issues discussed, exercises carried out (examples), atmosphere of training, contact with the trainer, location.
Wojskowe Zakłady Uzbrojenia S.A. w Grudziądzu
Kurz: Octave not only for programmers
A lot of practical tips
Pawel Dawidowski - ABB Sp. z o.o.
Kurz: Deep Learning with TensorFlow
A lot of information related to the implementation of solutions
Michał Smolana - ABB Sp. z o.o.
Kurz: Deep Learning with TensorFlow
A multitude of practical tips and knowledge of the lecturer from a wide range of AI / IT / SQL / IoT issues.
ABB Sp. z o.o.
Kurz: Deep Learning with TensorFlow
I like that it focuses more on the how-to of the different text summarization methods
Kurz: Text Summarization with Python
spousta informací, zodpovězené všechny otázky, zajímavé příklady
A1 Telekom Austria AG
Kurz: Deep Learning for Telecom (with Python)
Machine Translated
Trenér byl odborníkem v oboru a související teorii s aplikací výborně
Fahad Malalla - Tatweer Petroleum
Kurz: Applied AI from Scratch in Python
Machine Translated
Začal jsem téměř nulovými znalostmi a nakonec jsem byl schopen budovat a trénovat své vlastní sítě.
Huawei Technologies Duesseldorf GmbH
Kurz: TensorFlow for Image Recognition
Machine Translated
Ewa has a passion for the subject and a huge wealth of knowledge. She impressed all of us with her knowledge and kept us all focused through the day.
Rock Solid Knowledge Ltd
Kurz: Machine Learning – Data science
Even with having to miss a day due to customer meetings, I feel I have a much clearer understanding of the processes and techniques used in Machine Learning and when I would use one approach over another. Our challenge now is to practice what we have learned and start to apply it to our problem domain
Richard Blewett - Rock Solid Knowledge Ltd
Kurz: Machine Learning – Data science
So much breadth and topics covered. I felt it was a huge subject to try and cover in 3 days - the trainer did what they could to cover everything almost exactly on time!
Rock Solid Knowledge Ltd
Kurz: Machine Learning – Data science
I like that it focuses more on the how-to of the different text summarization methods
Kurz: Text Summarization with Python
Machine Learning Podkategorie
Machine Learning Návrh Školení
In this instructor-led, live training, participants will learn how to create models for processing text based data using OpenNLP. Sample training data as well customized data sets will be used as the basis for the lab exercises.
By the end of this training, participants will be able to:
- Install and configure OpenNLP
- Download existing models as well as create their own
- Train the models on various sets of sample data
- Integrate OpenNLP with existing Java applications
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 OpenFace's components to create and deploy a sample facial recognition application.
By the end of this training, participants will be able to:
- Work with OpenFace's components, including dlib, OpenVC, Torch, and nn4 to implement face detection, alignment, and transformation
- Apply OpenFace to real-world applications such as surveillance, identity verification, virtual reality, gaming, and identifying repeat customers, etc.
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
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.
It is a broad overview of existing methods, motivations and main ideas in the context of pattern recognition.
After a short theoretical background, participants will perform simple exercise using open source (usually R) or any other popular software.
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.
By the end of this training, participants will be able to:
- Create a mobile app capable of image processing, text analysis and speech recognition
- Access pre-trained ML models for integration into iOS apps
- Create a custom ML model
- Add Siri Voice support to iOS apps
- Understand and use frameworks such as coreML, Vision, CoreGraphics, and GamePlayKit
- Use languages and tools such as Python, Keras, Caffee, Tensorflow, sci-kit learn, libsvm, Anaconda, and Spyder
Audience
- Developers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Format of the course
- Lecture and discussion coupled with hands-on exercises.
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.
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.
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.
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.
In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry. R will be used as the programming language.
Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.
By the end of this training, participants will be able to:
- Understand the fundamental concepts in machine learning
- Learn the applications and uses of machine learning in finance
- Develop their own algorithmic trading strategy using machine learning with R
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 apply machine learning techniques and tools for solving real-world problems in the finance industry.
Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.
By the end of this training, participants will be able to:
- Understand the fundamental concepts in machine learning
- Learn the applications and uses of machine learning in finance
- Develop their own algorithmic trading strategy using machine learning with Python
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Target Audience
- Investors and AI entrepreneurs
- Managers and Engineers whose company is venturing into AI space
- Business Analysts & Investors
By the end of this training, participants will:
- Understand the evolution and trends for machine learning.
- Know how machine learning is being used across different industries.
- Become familiar with the tools, skills and services available to implement machine learning within an organization.
- Understand how machine learning can be used to enhance data mining and analysis.
- Learn what a data middle backend is, and how it is being used by businesses.
- Understand the role that big data and intelligent applications are playing across industries.
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.
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.
Format of the Course
- Part lecture, part discussion, 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
In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry.
Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
This instructor-led, live training (onsite or remote) is aimed at developers and data scientists who wish to use Tensorflow 2.0 to build predictors, classifiers, generative models, neural networks and so on.
By the end of this training, participants will be able to:
- Install and configure TensorFlow 2.0.
- Understand the benefits of TensorFlow 2.0 over previous versions.
- Build deep learning models.
- Implement an advanced image classifier.
- Deploy a deep learning model to the cloud, mobile and IoT devices.
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.
- To learn more about TensorFlow, please visit: https://www.tensorflow.org/
Word2Vec is a method of computing vector representations of words introduced by a team of researchers at Google led by Tomas Mikolov.
Audience
This course is directed at researchers, engineers and developers seeking to utilize Deeplearning4J to construct Word2Vec models.
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
SyntaxNet is a neural-network Natural Language Processing framework for TensorFlow.
Word2Vec is used for learning vector representations of words, called "word embeddings". Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (Chapter 3.1 and 3.2 in Mikolov et al.).
Used in tandem, SyntaxNet and Word2Vec allows users to generate Learned Embedding models from Natural Language input.
Audience
This course is targeted at Developers and engineers who intend to work with SyntaxNet and Word2Vec models in their TensorFlow graphs.
After completing this course, delegates will:
- 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, embedding terms, building graphs and logging
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
In this instructor-led, live training, 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.
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
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