Radom

Hotel Promenda, Bulwarowa 15, Radom, Poland, 26-600

The training room is located 5 kilometers from the city center and 8 kilometers from the Warsaw-Radom airport.

Explore Our Courses

Computer Vision with Google Colab and TensorFlow

 21 Hours
This instructor-led, live training in Radom (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab. By the end of this training, participants will be able to:
  • Build and train convolutional neural networks (CNNs) using TensorFlow.
  • Leverage Google Colab for scalable and efficient cloud-based model development.
  • Implement image preprocessing techniques for computer vision tasks.
  • Deploy computer vision models for real-world applications.
  • Use transfer learning to enhance the performance of CNN models.
  • Visualize and interpret the results of image classification models.
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Deep Learning with TensorFlow in Google Colab

 14 Hours
This instructor-led, live training in Radom (online or onsite) is aimed at intermediate-level data scientists and developers who wish to understand and apply deep learning techniques using the Google Colab environment. By the end of this training, participants will be able to:
  • Set up and navigate Google Colab for deep learning projects.
  • Understand the fundamentals of neural networks.
  • Implement deep learning models using TensorFlow.
  • Train and evaluate deep learning models.
  • Utilize advanced features of TensorFlow for deep learning.
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Applied AI from Scratch

 28 Hours
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 Hours
In this instructor-led, live training in Radom, 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.
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Deep Learning for Vision

 21 Hours
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 Hours
This instructor-led, live training in Radom (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.
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Deep Learning with TensorFlow 2

 21 Hours
This instructor-led, live training in Radom (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.
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TensorFlow Serving

 7 Hours
In this instructor-led, live training in Radom (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.
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Deep Learning with TensorFlow

 21 Hours
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 Hours
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 Hours
This instructor-led, live training in Radom (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.
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TPU Programming: Building Neural Network Applications on Tensor Processing Units

 7 Hours
In this instructor-led, live training in Radom, 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.
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Natural Language Processing (NLP) with TensorFlow

 35 Hours
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 Hours
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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Testimonials (6)

The trainer explained the content well and was engaging throughout. He stopped to ask questions and let us come to our own solutions in some practical sessions. He also tailored the course well for our needs.

Robert Baker

Course - Deep Learning with TensorFlow 2.0

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

Raju Krishnamurthy - Google

Course - TensorFlow Extended (TFX)

Organization, adhering to the proposed agenda, the trainer's vast knowledge in this subject

Ali Kattan - TWPI

Course - Natural Language Processing with TensorFlow

Many practical tips

Pawel Dawidowski - ABB Sp. z o.o.

Course - Deep Learning with TensorFlow

Machine Translated

The instructors have extensive theoretical and practical knowledge. The instructors are communicative. During the course, participants could ask questions and receive satisfying answers.

Kamil Kurek - ING Bank Slaski S.A.; Kamil Kurek Programowanie

Course - Understanding Deep Neural Networks

Machine Translated

Very updated approach or CPI (tensor flow, era, learn) to do machine learning.

Paul Lee

Course - TensorFlow for Image Recognition

Provisional Courses

TensorFlow Extended (TFX)

2025-07-07 09:00
21 hours
Radom
3870 PLN (Online)
4020 PLN (Classroom)

TensorFlow Serving

2025-07-21 09:00
7 hours
Radom
1980 PLN (Online)
2030 PLN (Classroom)

Computer Vision with Google Colab and TensorFlow

2025-08-04 09:00
21 hours
Radom
3870 PLN (Online)
4020 PLN (Classroom)

Applied AI from Scratch

2025-08-18 09:00
28 hours
Radom
4130 PLN (Online)
4330 PLN (Classroom)

Deep Learning for NLP (Natural Language Processing)

2025-09-01 09:00
28 hours
Radom
5160 PLN (Online)
5360 PLN (Classroom)

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