Introduction to the Use of Neural Networks Training Course

This program explores the application of AI—with a specific focus on Machine Learning and Deep Learning—within the automotive industry. It aims to identify technologies that can be (potentially) utilized across various automotive scenarios, ranging from basic automation and image recognition to autonomous decision-making processes.

This course is designed for managers, solution architects, innovation officers, CTOs, software architects, and any professional seeking a comprehensive understanding of applied artificial intelligence and its near-term development trajectory.

This instructor-led, live training in Italy (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.

An Artificial Neural Network serves as a computational data model utilized in the creation of Artificial Intelligence (AI) systems capable of executing "intelligent" tasks. Neural Networks are frequently employed in Machine Learning (ML) applications, which represent one implementation of AI. Deep Learning constitutes a subset of Machine Learning.

This four-day course provides an introduction to AI and its applications. An optional fifth day is available to complete an AI project upon finishing the course.

This four-day course provides an introduction to Artificial Intelligence and its practical applications using the Python programming language. Upon completion of this course, participants have the option to dedicate an additional day to working on a dedicated AI project.

This instructor-led, live training in Italy (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.

An Artificial Neural Network is a computational data model utilized in the creation of Artificial Intelligence (AI) systems capable of executing "intelligent" tasks. These networks are frequently employed in Machine Learning (ML) applications, which represent one form of AI implementation. Deep Learning constitutes a specific subset of Machine Learning.

This instructor-led, live training in Italy (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 Italy (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.

Deep Reinforcement Learning (DRL) merges the principles of reinforcement learning with deep learning architectures, empowering agents to make informed decisions through interaction with their surroundings. This technology drives numerous contemporary AI innovations, including self-driving cars, robotic control systems, algorithmic trading, and adaptive recommendation engines. By leveraging reward-based learning, DRL enables artificial agents to master strategies, refine policies, and execute autonomous decisions through trial and error.

This instructor-led live training, available both online and onsite, targets intermediate developers and data scientists eager to master and apply Deep Reinforcement Learning techniques. The course focuses on building 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 underpinnings of 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.
• Deploy DRL solutions in real-world scenarios, including gaming, robotics, and decision optimization.
• Utilize modern tools to troubleshoot, visualize, and optimize training performance.

Course Format

• Interactive lectures paired with guided discussions.
• Practical exercises and hands-on implementations.
• Live coding demonstrations and project-based applications.

Customization Options

• For customized versions of this course (e.g., utilizing PyTorch instead of TensorFlow), please reach out to us to arrange your specific requirements.

This instructor-led training session features presentations alongside hands-on computer-based examples and case study exercises utilizing relevant neural and deep network libraries.

This course serves as an introduction to leveraging neural networks for real-world problem-solving using R-project software.

This course provides a conceptual foundation in neural networks, machine learning algorithms, and deep learning (algorithms and applications).

Part-1 (40%) of this training focuses heavily on fundamentals, aiding you in selecting the appropriate technology: TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.

Part-2 (20%) of this training introduces Theano - a Python library that simplifies the creation of deep learning models.

Part-3 (40%) of the training is heavily centered on Tensorflow - the API for Google's open-source software library for Deep Learning. All examples and hands-on exercises will be conducted in TensorFlow.

Audience

This course is designed for engineers looking to leverage TensorFlow for their Deep Learning projects.

Upon completion of this course, delegates will:

• possess a solid understanding of deep neural networks (DNN), CNN, and RNN.
• comprehend the structure and deployment mechanisms of TensorFlow.
• be capable of performing installation, production environment setup, architecture tasks, and configuration.
• be able to evaluate code quality, perform debugging, and monitoring.
• be able to implement advanced production-grade tasks such as model training, graph construction, and logging.

This instructor-led, live training in Italy (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.