Introduction to the Use of Neural Networks Training Course

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

This program is designed for managers, solutions architects, innovation officers, CTOs, software architects, and anyone interested in gaining a comprehensive understanding of applied artificial intelligence and its near-term development trends.

This instructor-led live training in Romania (online or onsite) is designed for beginners who want to master key concepts in probability, statistics, programming, and machine learning to apply them in AI development.

By the end of this training, participants will be able to:

• Understand basic probability and statistics and apply them to practical scenarios.
• Write and interpret procedural, functional, and object-oriented code.
• Implement machine learning techniques such as classification, clustering, and neural networks.
• Develop AI solutions using rule engines and expert systems for problem-solving.

An Artificial Neural Network is a computational data model utilized in developing Artificial Intelligence (AI) systems that can perform "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 is a four-day course introducing AI and it's application. There is an option to have an additional day to undertake an AI project on completion of this course.

This intensive 4-day program provides a comprehensive introduction to Artificial Intelligence and its practical application using Python. Upon completion, participants may choose to extend the course by one additional day to undertake a dedicated AI project.

This instructor-led, live training in Romania (online or onsite) targets 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.

Artificial Neural Networks are computational data models utilized in the development of Artificial Intelligence (AI) systems that can execute "intelligent" tasks. Neural Networks are widely employed in Machine Learning (ML) applications, which serve as one implementation of AI. Deep Learning constitutes a subset of Machine Learning.

This instructor-led live training in Romania (online or onsite) is designed for researchers and developers who want to use Chainer to build and train neural networks in Python, ensuring the code remains easy to debug.

By the end of this training, participants will be able to:

• Set up the required development environment to begin creating neural network models.
• Define and implement neural network models using clear and understandable source code.
• Execute examples and modify existing algorithms to optimize deep learning training models, leveraging GPUs for high performance.

This instructor-led, live training in Romania (online or onsite) offers a comprehensive introduction to the fields of pattern recognition and machine learning. It explores practical applications across 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 tasks.
• Leverage essential models such as neural networks and kernel methods for data analysis.
• Implement advanced techniques to address complex problem-solving scenarios.
• Enhance prediction accuracy by integrating various models.

Deep Reinforcement Learning (DRL) integrates reinforcement learning concepts with deep learning models to allow agents to make decisions through environmental interaction. This approach drives numerous contemporary AI innovations, including autonomous vehicles, robotic control, algorithmic trading, and dynamic recommendation systems. DRL empowers artificial agents to learn strategies, refine policies, and execute autonomous decisions via trial-and-error processes driven by rewards.

This instructor-led training session (available online or onsite) is designed for intermediate developers and data scientists eager to master and apply Deep Reinforcement Learning techniques to construct intelligent agents capable of making autonomous decisions in complex scenarios.

Upon completion of this course, participants will be equipped to:

• Grasp the theoretical underpinnings and mathematical principles of Reinforcement Learning.
• Code fundamental 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 for practical applications like gaming, robotics, and decision optimization.
• Utilize modern tools to troubleshoot, visualize, and enhance training performance.

Course Format

• Interactive lectures paired with guided discussions.
• Practical exercises and real-world implementations.
• Live coding demonstrations and project-based learning.

Customization Options

• To request a tailored version of this course (for instance, using PyTorch instead of TensorFlow), please reach out to us to make arrangements.

This instructor-led course combines theoretical presentations with hands-on computer-based examples and case study exercises, utilizing relevant neural and deep learning libraries.

This course provides an introduction to implementing neural networks for real-world applications using the R programming environment.

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

The first part (40%) of this training emphasizes fundamentals, enabling you to select the most appropriate technology stack, such as TensorFlow, Caffe, Theano, DeepDrive, Keras, and others.

The second part (20%) introduces Theano, a Python library designed to simplify the creation of deep learning models.

The third part (40%) of the training focuses extensively on TensorFlow, Google's open-source library for deep learning. All examples and hands-on exercises will be conducted using TensorFlow.

Audience

This course is designed for engineers aiming to leverage TensorFlow for their deep learning projects.

Upon completion of this course, participants will:

• possess a strong understanding of deep neural networks (DNNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs)
• comprehend TensorFlow’s architecture and deployment mechanisms
• be capable of performing installation, production environment setup, and architectural configuration tasks
• be able to assess code quality, perform debugging, and monitor performance
• implement advanced production workflows, including model training, graph construction, and logging

This instructor-led, live training Romania (online or onsite) is designed for advanced professionals keen on exploring state-of-the-art XAI techniques for deep learning models, emphasizing the creation 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.