Swatar, City Centre

Regus Swatar, City Centre, 2nd Floor, Tower Business Centre, Swatar, Malta, BKR4013

The Swatar City Centre is on the second floor of Tower Business Centre, in the Swatar suburb of Birkirkara, a key commercial district. On the same road is the HSBC global call centre. Malta is a popular location for back office services, especially call centres, because of its reliable telecoms infrastructure and English speaking population. The Swatar City Centre is near major financial services, offshore banks and other leading companies. The country's main industries are expanding from tourism and freight to diverse manufacturing including pharmaceuticals, electronics, rubber, plastics, packaging, security systems, marine products and biomedical equipment. The centre is opposite a new state-of-the-art hospital, also a major interchange for the public transport network. Swatar borders on the university town of Msida and the centre's location near Birkirkara bypass makes it convenient for Malta International Airport and the capital city Valletta. There is a public garden, named Swatar Garden, which is situated at Balliju Guttenberg Street. The garden is equipped with European standard facilities such as; a 5-a-side football ground, energy efficient lightning and safety and security measures

Explore Our Courses

Explainability in Deep Learning: Demystifying Black-Box Models

 21 Hours
This instructor-led, live training in Swatar (online or onsite) is aimed at advanced-level professionals who wish to explore state-of-the-art XAI techniques for deep learning models, with a focus on building interpretable AI systems. By the end of this training, participants will be able to:
  • Understand the challenges of explainability in deep learning.
  • Implement advanced XAI techniques for neural networks.
  • Interpret decisions made by deep learning models.
  • Evaluate the trade-offs between performance and transparency.
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Applied AI from Scratch in Python

 28 Hours
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 Hours
This instructor-led, live training in Swatar (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.
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Artificial Intelligence (AI) Overview

 7 Hours
This course has been created for managers, solutions architects, innovation officers, CTOs, software architects and anyone who is interested in an overview of applied artificial intelligence and the nearest forecast for its development.
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Artificial Intelligence (AI) in Automotive

 14 Hours
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 Hours
This instructor-led, live training in Swatar (online or onsite) is aimed at beginner-level participants who wish to learn essential concepts in probability, statistics, programming, and machine learning, and apply these to AI development. By the end of this training, participants will be able to:
  • Understand basic concepts in probability and statistics, and apply them to real-world scenarios.
  • Write and understand procedural, functional, and object-oriented programming code.
  • Implement machine learning techniques such as classification, clustering, and neural networks.
  • Develop AI solutions using rules engines and expert systems for problem-solving.
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Artificial Neural Networks, Machine Learning, Deep Thinking

 21 Hours
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 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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Applied Machine Learning

 14 Hours
This instructor-led, live training in Swatar (online or onsite) is aimed at intermediate-level data scientists and statisticians who wish to prepare data, build models, and apply machine learning techniques effectively in their professional domains. By the end of this training, participants will be able to:
  • Understand and implement various Machine Learning algorithms.
  • Prepare data and models for machine learning applications.
  • Conduct post hoc analyses and visualize results effectively.
  • Apply machine learning techniques to real-world, sector-specific scenarios.
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Artificial Neural Networks, Machine Learning and Deep Thinking

 21 Hours
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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Deep Learning Neural Networks with Chainer

 14 Hours
This instructor-led, live training in Swatar (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.
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Pattern Recognition

 21 Hours
This instructor-led, live training in Swatar (online or onsite) 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. By the end of this training, participants will be able to:
  • Apply core statistical methods to pattern recognition.
  • Use key models like neural networks and kernel methods for data analysis.
  • Implement advanced techniques for complex problem-solving.
  • Improve prediction accuracy by combining different models.
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Introduction Deep Learning & Réseaux de neurones pour l’ingénieur

 21 Hours
Type: Theoretical training with applications decided in advance with the students on Lasagne or Keras according to the educational group Teaching method: presentation, discussions and case studies Artificial intelligence, after having disrupted many scientific fields, has begun to revolutionize a large number of economic sectors (industry, medicine, communication, etc.). However, its presentation in the mainstream media is often a fantasy, very far from what the domains of Machine Learning or Deep Learning really are. The purpose of this training is to provide engineers who already have mastery of IT tools (including a basic software programming basis) with an introduction to Deep Learning as well as to its different areas of specialization and therefore to the main network architectures. existing today. If the mathematical basics are covered during the course, a BAC+2 level of mathematics is recommended for greater comfort. It is absolutely possible to ignore the mathematical axis and retain only a “system” vision, but this approach will enormously limit your understanding of the subject.
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Matlab for Deep Learning

 14 Hours
In this instructor-led, live training, participants will learn how to use Matlab to design, build, and visualize a convolutional neural network for image recognition. By the end of this training, participants will be able to:
  • Build a deep learning model
  • Automate data labeling
  • Work with models from Caffe and TensorFlow-Keras
  • Train data using multiple GPUs, the cloud, or clusters
Audience
  • Developers
  • Engineers
  • Domain experts
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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Artificial Intelligence (AI) for Mechatronics

 21 Hours
This instructor-led, live training in Swatar (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.
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Introduction to the Use of Neural Networks

 7 Hours
The training is aimed at people who want to learn the basics of neural networks and their applications.
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Neural computing – Data science

 14 Hours
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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Neural Network in R

 14 Hours
This course is an introduction to applying neural networks in real world problems using R-project software.
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TPU Programming: Building Neural Network Applications on Tensor Processing Units

 7 Hours
In this instructor-led, live training in Swatar, 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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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)

Hunter is fabulous, very engaging, extremely knowledgeable and personable. Very well done.

Rick Johnson - Laramie County Community College

Course - Artificial Intelligence (AI) Overview

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

Fahad Malalla - Tatweer Petroleum

Course - Applied AI from Scratch in Python

Very flexible.

Frank Ueltzhoffer

Course - Artificial Neural Networks, Machine Learning and Deep Thinking

I liked the new insights in deep machine learning.

Josip Arneric

Course - Neural Network in R

Ann created a great environment to ask questions and learn. We had a lot of fun and also learned a lot at the same time.

Gudrun Bickelq

Course - Introduction to the use of neural networks

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

Course - Artificial Neural Networks, Machine Learning, Deep Thinking

Provisional Upcoming Courses (Contact Us For More Information)

Artificial Intelligence (AI) in Automotive

2025-06-23 09:30
14 hours
Swatar, City Centre

Artificial Neural Networks, Machine Learning and Deep Thinking

2025-07-07 09:30
21 hours
Swatar, City Centre

Understanding Deep Neural Networks

2025-07-21 09:30
35 hours
Swatar, City Centre

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