Neural Networks Training in Plovdiv

This instructor-led, live training in Plovdiv (online or onsite) is designed for advanced professionals who wish to explore state-of-the-art XAI techniques for deep learning models, focusing on the development of interpretable AI systems.

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

• Grasp the challenges associated with explainability in deep learning.
• Apply advanced XAI techniques to neural networks.
• Interpret the decisions generated by deep learning models.
• Assess the balance between model performance and transparency.

Applied AI from Scratch in Python empowers programmers and data analysts with the fundamental techniques required to construct machine learning solutions entirely from the ground up using Python. The course covers essential principles of supervised learning, including classification and regression, as well as unsupervised learning methods like clustering and anomaly detection, alongside advanced neural network architectures. It explores established practices for utilizing scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate practical AI development. Participants will learn to implement functional ML models, assess the limitations of various algorithms, and execute applied projects designed for real-world problem-solving.

Deep Reinforcement Learning (DRL) merges reinforcement learning principles with deep learning architectures, empowering agents to make decisions through interaction with their environments. This approach drives many modern AI innovations, including self-driving vehicles, robotics control, algorithmic trading, and adaptive recommendation systems. DRL enables artificial agents to learn strategies, optimize policies, and make autonomous decisions via trial and error using reward-based learning.

This instructor-led live training (available online or onsite) is designed for intermediate-level developers and data scientists who want to learn and apply Deep Reinforcement Learning techniques to build intelligent agents capable of autonomous decision-making in complex environments.

Upon completing this training, participants will be able to:

• Grasp the theoretical foundations and mathematical principles of Reinforcement Learning.
• Implement core RL algorithms, including Q-Learning, Policy Gradients, and Actor-Critic methods.
• Construct and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
• Apply DRL to practical applications such as gaming, robotics, and decision optimization.
• Troubleshoot, visualize, and optimize training performance using 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 (e.g., using PyTorch instead of TensorFlow), please contact us to arrange.

Understanding the fundamentals of artificial intelligence highlights how intelligent technologies are transforming digital strategy, automation, and decision-making processes across enterprise operations. This course covers essential topics including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning approaches, alongside areas such as communication, perception, and autonomous behavior. It equips executives and architects with the insights needed to evaluate AI-driven transformation opportunities, assess emerging technology trends, and implement practical intelligent solutions to enhance business agility.

This course provides an in-depth exploration of AI, with a specific focus on Machine Learning and Deep Learning, within the Automotive Industry. It is designed to help participants identify which technologies can be effectively applied across various automotive scenarios, ranging from basic automation and image recognition to complex autonomous decision-making processes.

Artificial Neural Networks (ANNs) are computational models utilized in the creation of Artificial Intelligence (AI) systems that can execute complex, intelligent tasks. These networks are a core component of Machine Learning (ML) applications, which represent one of the primary implementations of AI. Deep Learning is specifically a specialized subset of Machine Learning.

This instructor-led, live training in Plovdiv (available online or onsite) is designed for researchers and developers who wish to use Chainer to build and train neural networks in Python, while ensuring the code is 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 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 Plovdiv (online or onsite) offers an introduction to the fields of pattern recognition and machine learning. It covers practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.

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

• Apply fundamental statistical methods to pattern recognition.
• Utilize essential models such as neural networks and kernel methods for data analysis.
• Implement advanced techniques to solve complex problems.
• Enhance prediction accuracy by integrating various models.

This course starts by providing conceptual knowledge about neural networks and machine learning algorithms, including deep learning (algorithms and applications).

Part-1 (40%) of this training focuses more on fundamentals, but will help you choose 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 will be extensively based on TensorFlow, Google's open-source software library API for Deep Learning. All examples and hands-on exercises will be done 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 of 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, and monitoring.
• be able to implement advanced production-like training models, building graphs, and logging.