Delivered either online or on-site, these instructor-led live Deep Learning (DL) training courses use hands-on practice to demonstrate the fundamentals and real-world applications of Deep Learning, covering key areas such as deep machine learning, deep structured learning, and hierarchical learning.
Deep Learning training can be accessed as "online live training" or "onsite live training". Online live training (also known as "remote live training") is conducted through an interactive, remote desktop. Onsite live training can be facilitated locally at customer premises in Gaia or within 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) targets intermediate-level developers, data scientists, and AI practitioners who aim to leverage TensorFlow Lite for Edge AI applications.
By the end of this training, participants will be able to:
Grasp the fundamentals of TensorFlow Lite and its role in Edge AI.
Develop and optimize AI models using TensorFlow Lite.
Deploy TensorFlow Lite models on various edge devices.
Utilize tools and techniques for model conversion and optimization.
Implement practical Edge AI applications using TensorFlow Lite.
Directed by an instructor, this live training in Gaia (accessible online or on-site) is designed for advanced professionals seeking to expand their proficiency in computer vision. It highlights how to harness TensorFlow's potential within Google Colab to engineer advanced vision models.
By the conclusion of this programme, participants will have the capability to:
Construct and train convolutional neural networks (CNNs) with TensorFlow.
Exploit Google Colab for efficient and scalable cloud-based model creation.
Execute image preprocessing methods relevant to computer vision tasks.
Integrate computer vision models into real-world operational contexts.
Leverage transfer learning to boost the efficacy of CNN models.
Interpret and visualize data from image classification outputs.
This instructor-led, live training in Gaia (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.
This instructor-led, live training in Gaia (online or onsite) is aimed at advanced-level professionals who wish to specialise in cutting-edge deep learning techniques for NLU.
By the end of this training, participants will be able to:
Understand the key differences between NLU and NLP models.
Apply advanced deep learning techniques to NLU tasks.
Explore deep architectures such as transformers and attention mechanisms.
Leverage future trends in NLU for building sophisticated AI systems.
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 instructor-led, live training in Gaia (online or onsite) is aimed at intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts who wish to expand their knowledge and skills in deep learning for text-to-image generation.
By the end of this training, participants will be able to:
Understand advanced deep learning architectures and techniques for text-to-image generation.
Implement complex models and optimizations for high-quality image synthesis.
Optimize performance and scalability for large datasets and complex models.
Tune hyperparameters for better model performance and generalization.
Integrate Stable Diffusion with other deep learning frameworks and tools
This instructor-led live training in Gaia (online or onsite) is designed for advanced professionals aiming to leverage AI techniques to revolutionise drug discovery and development processes.
Upon completion of this training, participants will be able to:
Understand the role of AI in drug discovery and development.
Apply machine learning techniques to predict molecular properties and interactions.
Use deep learning models for virtual screening and lead optimization.
Integrate AI-driven approaches into the clinical trial process.
This instructor-led, live training in Gaia (online or onsite) is designed for developers at beginner to intermediate levels who wish to utilize Large Language Models for various natural language tasks.
Upon completion of this training, participants will be able to:
Configure a development environment integrated with a prominent LLM.
Construct a foundational LLM and fine-tune it using a custom dataset.
Apply LLMs to various natural language tasks such as text summarization, question answering, and text generation.
Debug and evaluate LLMs using tools like TensorBoard, PyTorch Lightning, and Hugging Face Datasets.
This instructor-led, live training in (online or onsite) is aimed at data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases.
By the end of this training, participants will be able to:
Understand the principles of Stable Diffusion and how it works for image generation.
Build and train Stable Diffusion models for image generation tasks.
Apply Stable Diffusion to various image generation scenarios, such as inpainting, outpainting, and image-to-image translation.
Optimize the performance and stability of Stable Diffusion models.
In this instructor-led, live training in Gaia, attendees will master the most pertinent and state-of-the-art machine learning techniques in Python while developing a range of demo applications that handle image, music, text, and financial data.
Upon completion of this training, participants will be able to:
Implement machine learning algorithms and techniques to address complex problems.
Apply deep learning and semi-supervised learning methods to applications involving image, music, text, and financial data.
Maximize the potential of Python algorithms.
Utilize libraries and packages such as NumPy and Theano.
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 instructor-led, live training in Gaia (online or on-site) is aimed at software developers, data analysts, and technical professionals who wish to use TensorFlow 2.x and Keras to build, train, and deploy deep learning models for computer vision, natural language processing, and multimodal applications.
This instructor-led, live training in Gaia (online or onsite) is designed for data scientists who wish to utilise TensorFlow to analyse 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 analysing fraud data.
During this instructor-led live training, participants will discover how to utilize Matlab to design, construct, and visualize a convolutional neural network specifically for image recognition.
Upon completion of this course, participants will be able to:
Construct a deep learning model
Automate the data labeling process
Utilize models from Caffe and TensorFlow-Keras
Train data employing multiple GPUs, cloud resources, or clusters
Audience
Developers
Engineers
Domain experts
Course Format
A blend of lectures, discussions, exercises, and extensive hands-on practice
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 instructor-led live training in Gaia (online or onsite) is designed for developers and data scientists seeking to utilize TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and similar applications.
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.
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 (6)
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
The training was organized and well-planned out, and I come out of it with systematized knowledge and a good look at topics we looked at
Magdalena - Samsung Electronics Polska Sp. z o.o.
Course - Deep Learning with TensorFlow 2
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
In-depth coverage of machine learning topics, particularly neural networks. Demystified a lot of the topic.
Sacha Nandlall
Course - Python for Advanced Machine Learning
Provisional Upcoming Courses (Contact Us For More Information)
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