Neural Networks Training in Porto

This instructor-led, live training in Porto (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.

Applied AI from Scratch in Python empowers programmers and data analysts with the essential techniques required to build machine learning solutions from the ground up using Python. The course covers the core principles of supervised learning (classification and regression), unsupervised learning (clustering and anomaly detection), and advanced neural network architectures. It examines proven methodologies for utilizing scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. Participants will learn to implement practical ML models, assess algorithm limitations, and complete applied projects designed for real-world problem-solving.

Deep Reinforcement Learning (DRL) merges the principles of reinforcement learning with deep learning architectures, empowering agents to make decisions through environmental interaction. This technology drives numerous contemporary AI innovations, including autonomous vehicles, robotic control systems, algorithmic trading, and adaptive recommendation engines. By utilizing reward-based learning, DRL enables artificial agents to learn strategies, optimize policies, and execute autonomous decisions via trial and error.

This instructor-led live training, available online or onsite, is designed for intermediate-level developers and data scientists seeking to master and apply Deep Reinforcement Learning techniques to develop intelligent agents capable of autonomous decision-making within complex environments.

Upon completion of this training, participants will be able to:

• Grasp the theoretical foundations and mathematical principles underpinning Reinforcement Learning.
• Implement core RL algorithms, such as Q-Learning, Policy Gradients, and Actor-Critic methods.
• Construct and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
• Apply DRL to practical applications, including games, robotics, and decision optimization.
• Troubleshoot, visualize, and optimize training performance with modern tools.

Format of the Course

• Interactive lectures and guided discussions.
• Hands-on exercises and practical implementations.
• Live coding demonstrations and project-based applications.

Course Customization Options

• To request a customized version of this course (for example, utilizing PyTorch instead of TensorFlow), please contact us to arrange.

An exploration of artificial intelligence fundamentals demonstrates how intelligent technology transforms digital strategy, automation, and decision-making processes within enterprise operations. The course examines core concepts including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning paradigms, alongside aspects of communication, perception, and autonomous action. It equips executives and architects to evaluate AI-driven transformation opportunities, assess emerging technology trends, and implement practical intelligent solutions to enhance business agility.

This course explores the application of AI—focusing on Machine Learning and Deep Learning—within the automotive industry. It aids in identifying which technologies can be (potentially) applied across various scenarios in a vehicle, ranging from basic automation and image recognition to autonomous decision-making.

Artificial Neural Networks are computational data models employed in the development of Artificial Intelligence (AI) systems capable of executing "intelligent" tasks. These networks are widely utilized in Machine Learning (ML) applications, which serve as one of the primary implementations of AI. Deep Learning constitutes a specialized subset of Machine Learning.

This instructor-led, live training in Porto (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.

This instructor-led, live training in Porto (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 course provides a foundational understanding of neural networks, machine learning algorithms, and deep learning (both algorithms and applications).

Part 1 (40%) of this training focuses on fundamentals, helping you select the appropriate technology such as TensorFlow, Caffe, Theano, DeepDrive, Keras, and others.

Part 2 (20%) of the training introduces Theano, a Python library designed to simplify the creation of deep learning models.

Part 3 (40%) of the training is extensively based on TensorFlow, Google's open-source software library for deep learning. All examples and hands-on exercises will be conducted using TensorFlow.

Audience

This course is designed for engineers looking to utilize TensorFlow for their deep learning projects.

Upon completion, participants will be able to:

• Gain a solid understanding of deep neural networks (DNN), CNNs, and RNNs
• Comprehend TensorFlow’s structure and deployment mechanisms
• Perform installation, production environment setup, architecture tasks, and configuration
• Assess code quality, debug, and monitor systems
• Implement advanced production-like tasks such as training models, building graphs, and logging