Lokální instruktorem vedené TensorFlow školení České republice.
Reference
★★★★★
★★★★★
I really appreciated the crystal clear answers of Chris to our questions.
Léo Dubus
Kurz: Neural Networks Fundamentals using TensorFlow as Example
Knowledgeable trainer
Sridhar Voorakkara
Kurz: Neural Networks Fundamentals using TensorFlow as Example
I was amazed at the standard of this class - I would say that it was university standard.
David Relihan
Kurz: Neural Networks Fundamentals using TensorFlow as Example
Very good all round overview.Good background into why Tensorflow operates as it does.
Kieran Conboy
Kurz: Neural Networks Fundamentals using TensorFlow as Example
I liked the opportunities to ask questions and get more in depth explanations of the theory.
Sharon Ruane
Kurz: Neural Networks Fundamentals using TensorFlow as Example
Very updated approach or api (tensorflow, kera, tflearn) to do machine learning
Paul Lee
Kurz: TensorFlow for Image Recognition
Given outlook of the technology: what technology/process might become more important in the future; see, what the technology can be used for
Commerzbank AG
Kurz: Neural Networks Fundamentals using TensorFlow as Example
Topic selection. Style of training. Practice orientation
Commerzbank AG
Kurz: Neural Networks Fundamentals using TensorFlow as Example
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
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)
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.
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.
DL (Deep Learning) is a subset of ML (Machine Learning).
Python is a popular programming language that contains libraries for Deep Learning for NLP.
Deep Learning for NLP (Natural Language Processing) allows a machine to learn simple to complex language processing. Among the tasks currently possible are language translation and caption generation for photos.
In this instructor-led, live training, participants will learn to use Python libraries for NLP as they create an application that processes a set of pictures and generates captions.
By the end of this training, participants will be able to:
- Design and code DL for NLP using Python libraries. - Create Python code that reads a substantially huge collection of pictures and generates keywords. - Create Python Code that generates captions from the detected keywords.
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
This course is suitable for Deep Learning researchers and engineers interested in utilizing available tools (mostly open source) for analyzing computer images
Embedding Projector is an open-source web application for visualizing the data used to train machine learning systems. Created by Google, it is part of TensorFlow.
This instructor-led, live training introduces the concepts behind Embedding Projector and walks participants through the setup of a demo project.
By the end of this training, participants will be able to:
- Explore how data is being interpreted by machine learning models - Navigate through 3D and 2D views of data to understand how a machine learning algorithm interprets it - Understand the concepts behind Embeddings and their role in representing mathematical vectors for images, words and numerals. - Explore the properties of a specific embedding to understand the behavior of a model - Apply Embedding Project to real-world use cases such building a song recommendation system for music lovers
Audience
- Developers - Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
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.
TensorFlow is a popular and machine learning library developed by Google for deep learning, numeric computation, and large-scale machine learning. TensorFlow 2.0, released in Jan 2019, is the newest version of TensorFlow and includes improvements in eager execution, compatibility and API consistency.
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/
TensorFlow Serving is a system for serving machine learning (ML) models to production.
In this instructor-led, live training, participants will learn how to configure and use TensorFlow Serving to deploy and manage ML models in a production environment.
By the end of this training, participants will be able to:
- Train, export and serve various TensorFlow models - Test and deploy algorithms using a single architecture and set of APIs - Extend TensorFlow Serving to serve other types of models beyond TensorFlow models
Audience
- Developers - Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
TensorFlow is a 2nd Generation API of Google's open source software library for Deep Learning. The system is designed to facilitate research in machine learning, and to make it quick and easy to transition from research prototype to production system.
Audience
This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects
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, building graphs and logging
This course explores, with specific examples, the application of Tensor Flow to the purposes of image recognition
Audience
This course is intended for engineers seeking to utilize TensorFlow for the purposes of Image Recognition
After completing this course, delegates will be able to:
- understand TensorFlow’s structure and deployment mechanisms - carry out installation / production environment / architecture tasks and configuration - assess code quality, perform debugging, monitoring - implement advanced production like training models, building graphs and logging
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
TensorFlow™ is an open source software library for numerical computation using data flow graphs.
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
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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