Lublin

Hotel Trzy Róże, Zemborzyce Dolne 96a, Lublin, Polska, 20-515

Sale szkoleniowe wyposażone są w nowoczesny sprzęt audiowizualny, umożliwiający efektywne prezentacje...

Zamość

Ośrodek Sportu i Rekreacji , Królowej Jadwigi 8 , Zamość, Polska, 22-400

Sala szkoleniowa, zlokalizowana w centralnej części Zamościa, stanowi idealne miejsce do organizacji wa...

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Embedding Projector: Visualizing Your Training Data

14 godzin
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
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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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Deep Learning for NLP (Natural Language Processing)

28 godzin
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
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Deep Learning for Vision

21 godzin
Audience This course is suitable for Deep Learning researchers and engineers interested in utilizing available tools (mostly open source) for analyzing computer images This course provide working examples.
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Fraud Detection with Python and TensorFlow

14 godzin
TensorFlow is an open source machine learning library. TensorFlow provides users the ability to use and create artificial intelligence for detecting and predicting fraud. This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use TensorFlow to analyze potential fraud data. By the end of this training, participants will be able to:
  • Create a fraud detection model in Python and TensorFlow.
  • Build linear regressions and linear regression models to predict fraud.
  • Develop an end-to-end AI application for analyzing fraud data.
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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Kubeflow on OpenShift

28 godzin
Kubeflow is a framework for running Machine Learning workloads on Kubernetes. TensorFlow is one of the most popular machine learning libraries. Kubernetes is an orchestration platform for managing containerized applications. OpenShift is a cloud application development platform that uses Docker containers, orchestrated and managed by Kubernetes, on a foundation of Red Hat Enterprise Linux. This instructor-led, live training (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to an OpenShift on-premise or hybrid cloud.
  • By the end of this training, participants will be able to:
  • Install and configure Kubernetes and Kubeflow on an OpenShift cluster.
  • Use OpenShift to simplify the work of initializing a Kubernetes cluster.
  • Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
  • Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
  • Call public cloud services (e.g., AWS services) from within OpenShift to extend an ML application.
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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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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Deep Learning with TensorFlow 2

21 godzin
TensorFlow is a popular 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 (online or onsite) is aimed at developers and data scientists who wish to use Tensorflow 2.x 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.x.
  • Understand the benefits of TensorFlow 2.x 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/
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Machine Learning with TensorFlow.js

14 godzin
TensorFlow.js is a JavaScript framework for machine learning. TensorFlow.js enables users to build and train machine learning models directly in JavaScript. This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use TensorFlow.js to identify patterns and generate predictions through machine learning models. By the end of this training, participants will be able to:
  • Build and train machine learning models with TensorFlow.js.
  • Run machine learning models in the browser or under Node.js.
  • Retrain pre-existing machine learning models using custom data.
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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TensorFlow Serving

7 godzin
TensorFlow Serving is a system for serving machine learning (ML) models to production. In this instructor-led, live training (online or onsite), 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
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
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Deep Learning with TensorFlow

21 godzin
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
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TensorFlow for Image Recognition

28 godzin
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
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TensorFlow Extended (TFX)

21 godzin
TensorFlow Extended (TFX) is an end-to-end platform for deploying production ML pipelines. This instructor-led, live training (online or onsite) is aimed at data scientists who wish to go from training a single ML model to deploying many ML models to production. By the end of this training, participants will be able to:
  • Install and configure TFX and supporting third-party tools.
  • Use TFX to create and manage a complete ML production pipeline.
  • Work with TFX components to carry out modeling, training, serving inference, and managing deployments.
  • Deploy machine learning features to web applications, mobile applications, IoT devices and more.
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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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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Natural Language Processing (NLP) with TensorFlow

35 godzin
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
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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
Więcej...

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Opinie uczestników (13)

Przyjemna atmosfera, obszerna wiedza trenera, wyczerpujące odpowiedzi na zadawane pytania, nawet takie wychodzące poza tematykę szkolenia.

Mateusz Pilecki - AGH
Szkolenie - Deep Learning with TensorFlow 2

Rozmaite przykłady jak stosować sieci neuronowe w różnych kontekstach. Notebooki były na wysokim poziomie. Można było się również dowiedzieć trochę o innych narzędziach przydatnych w pracy z Machine Learningiem.

Dominik Żurek - AGH
Szkolenie - Deep Learning with TensorFlow 2

Duża różnorodność podejść, danych, problemów do rozwiązania. Ogromna przestrzeń do zadawania pytań na tematy techniczne a także około branżowe.

Karol Talaga - AGH
Szkolenie - Deep Learning with TensorFlow 2

Kontakt z instruktorem oraz przyjazna atmosfera szkolenia

Piotr Pawełczak - AGH
Szkolenie - Deep Learning with TensorFlow 2

Super atmosfera w trakcie szkolenia. Bardzo dobry kontakt z Trenem oraz Panią Anią. Dobre proporcje pomiędzy wykładami i praktyką. Bardzo interesujący sposób przekazania wiedzy praktycznej. Stworzone w trakcie szkolenia skrypty są bardzo dobrym punktem startowym do kolejnych, samodzielnych projektów. Gorąco polecam szkolenia z NobleProg :)

Maciej Traczyk - PCO S.A.
Szkolenie - Deep Learning with TensorFlow 2

Fragmenty pracy własnej gdzie można było poeksperymentować na kodzie, oraz sama forma szkolenia - dzięki przeplataniu wykład/ćwiczenia/interakcje przetrwanie jednak długiego szkolenia było znacznie prostsze i przyjemniejsze

Michał Motyl - AGH
Szkolenie - Deep Learning with TensorFlow

Dobre tempo, zróżnicowane ciekawe materiały, odpowiednia ilość pracy samodzielnej i w grupach, teoria dobrze dopasowana do części praktycznej

Anna Nagi - AGH
Szkolenie - Deep Learning with TensorFlow

Sprawnie opanowany aspekt techniczny, duza dawka wiedzy

Igor Ratajczyk - AGH
Szkolenie - Deep Learning with TensorFlow

Nie było czegoś co mi się nie podobało.

Dominik Czyżyk - AGH
Szkolenie - Deep Learning with TensorFlow

Dużo informacji związanych z wdrożeniami rozwiązań

Michał Smolana - ABB Sp. z o.o.
Szkolenie - Deep Learning with TensorFlow

Very updated approach or api (tensorflow, kera, tflearn) to do machine learning

Paul Lee
Szkolenie - TensorFlow for Image Recognition

Tomasz really know the information well and the course was well paced.

Raju Krishnamurthy - Google
Szkolenie - TensorFlow Extended (TFX)

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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