Delivered either online or on-site, our instructor-led Neural Network training courses combine interactive discussion with hands-on practice to demonstrate how to build Neural Networks. We utilize a variety of mostly open-source toolkits and libraries, while also exploring how to leverage advanced hardware such as GPUs, along with optimization techniques for distributed computing and big data. These courses are built around popular programming languages like Python, Java, and R, and employ powerful libraries including TensorFlow, Torch, Caffe, and Theano. Our curriculum covers both the theoretical foundations and practical implementation of various architectures, including Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN).
You can choose between “online live training” and “onsite live training.” Online live training (also known as “remote live training”) takes place through an interactive, remote desktop. Alternatively, onsite live training is available at your customer premises in Gaia or at NobleProg’s corporate training centers in Gaia.
NobleProg – Your Local Training Provider
Holiday Inn Porto - Gaia
220, Rua Diogo Macedo, 220, Vila Nova de Gaia 4400-107 , Gaia, Portugal
From Porto by Metro
If you're coming from Porto city centre:
Take the Metro Line D (yellow line) toward Vila d’Este.
Get off at João de Deus.
From João de Deus, walk about 13 minutes to: Rua Diogo Macedo 220, 4400-107 Vila Nova de Gaia.
From Vila Nova de Gaia train station
If by “local train station” you mean Vila Nova de Gaia-Devesas:
The address is approximately 12 minutes walking from Vila Nova de Gaia station according to the accommodation's location information.
You can therefore walk directly rather than taking the metro.
This instructor-led, live training in Gaia (online or onsite) is targeted at advanced-level professionals who wish to investigate cutting-edge XAI techniques for deep learning models, focusing on the development of 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.
This hands-on training in Gaia empowers programmers to construct AI models from the ground up using Python. Participants will gain proficiency in supervised learning, neural networks, and unsupervised techniques by leveraging scikit-learn and Apache Spark. The programme emphasises practical Jupyter exercises focused on solving real-world problems.
This live, instructor-led training in Gaia explores both the theoretical basis and practical execution of Deep Reinforcement Learning using Python. Attendees will learn to construct and train DRL agents with TensorFlow or PyTorch, applying critical algorithms such as DQN and PPO to address intricate real-world challenges.
An introductory course on Gaia that delves into the principles of AI, ranging from intelligent agents to machine learning. This training empowers executives and architects to evaluate emerging AI trends, integrate practical solutions, and enhance business agility through automated strategies.
Discover how Machine Learning and Deep Learning are reshaping the automotive industry. This Gaia course explores fundamental concepts, progressing from simple automation to autonomous decision-making, and includes insights into neural networks as well as practical TensorFlow examples for real-world implementation.
This three-day program in Gaia provides a comprehensive exploration of the theoretical and practical aspects of Artificial Neural Networks, Machine Learning, and Deep Learning. Participants will examine various network architectures, learning mechanisms, and underlying mathematical foundations, progressing from fundamental perceptrons to sophisticated deep learning methodologies.
This instructor-led, live training in Gaia (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.
This introductory module outlines the core concepts of neural networks within Gaia, covering AI fundamentals, neuron mechanics, activation functions, and network architecture. Learners will acquire knowledge of backpropagation, training methodologies, and real-world use cases, including stock market forecasting.
This 35-hour course on Gaia explores the fundamentals of deep neural networks, including CNNs, RNNs, and generative models such as GANs. Participants will gain practical experience with Theano and TensorFlow, learning to build, train, and deploy production-grade deep learning models for real-world applications.
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Testimonials (4)
Getting people that never used AI some repetition in prompting and people that do use AI to consider different methods to using it.
Matthew Gay - Tarsus Pharmaceuticals
Course - Artificial Intelligence (AI) Overview
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
It felt like we were going through directly relevant information at a good pace (i.e. no filler material)
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Introduction to the use of neural networks
That it was applying real company data.
Trainer had a very good approach by making trainees participate and compete
Jimena Esquivel - Zaklad Uslugowy Hakoman Andrzej Cybulski
Course - Applied AI from Scratch in Python
Provisional Upcoming Courses (Contact Us For More Information)
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