Introduction to the Use of Neural Networks Training Course

This course provides an overview of artificial intelligence, with a focus on machine learning and deep learning, as applied to the automotive industry. It is designed to help participants identify which technologies can potentially be utilized in various scenarios within a vehicle, ranging from basic automation to image recognition and autonomous decision-making for government applications.

This course has been designed for government managers, solutions architects, innovation officers, CTOs, software architects, and any other professionals interested in gaining an overview of applied artificial intelligence and the near-term forecast for its development for government.

This instructor-led, live training in US Empire (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 for government.

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 for government.
• Write and understand procedural, functional, and object-oriented programming code for government applications.
• Implement machine learning techniques such as classification, clustering, and neural networks for government projects.
• Develop AI solutions using rules engines and expert systems for problem-solving in the public sector.

Artificial Neural Networks are computational data models utilized in the development of Artificial Intelligence (AI) systems designed to perform "intelligent" tasks. These networks are frequently employed in Machine Learning (ML) applications, which represent one implementation of AI for government. Deep Learning is a subset of ML.

This four-day course provides an introduction to artificial intelligence (AI) and its applications for government. An optional fifth day is available for participants to engage in an AI project following the conclusion of the course.

This four-day course provides an introduction to artificial intelligence (AI) and its applications using the Python programming language. An optional fifth day is available for government participants to engage in an AI project upon completion of the course.

This instructor-led, live training in US Empire (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 for government and other professional domains.

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

• Understand and implement various Machine Learning algorithms for government use cases.
• Prepare data and models for machine learning applications in a public sector context.
• Conduct post hoc analyses and visualize results effectively for government reporting.
• Apply machine learning techniques to real-world, sector-specific scenarios, including those relevant to government operations.

Artificial Neural Networks are computational data models utilized in the development of Artificial Intelligence (AI) systems designed to perform "intelligent" tasks. These networks are frequently employed in Machine Learning (ML) applications, which represent one implementation of AI for government. Deep Learning is a specialized subset of ML.

This instructor-led, live training in US Empire (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python, ensuring the code is easy to debug and maintain for government applications.

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

• Set up the necessary development environment to start creating neural network models for government use.
• Define and implement neural network models using clear and understandable source code.
• Execute examples and modify existing algorithms to optimize deep learning training models while leveraging GPUs for high performance, ensuring alignment with public sector workflows and governance requirements.

This instructor-led, live training in US Empire (online or onsite) provides an introduction to 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 for government.

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

• Apply core statistical methods to pattern recognition for government use.
• Use key models like neural networks and kernel methods for data analysis in public sector contexts.
• Implement advanced techniques for complex problem-solving within government workflows.
• Improve prediction accuracy by combining different models, enhancing governance and accountability.

Deep Reinforcement Learning (DRL) integrates the principles of reinforcement learning with deep learning architectures to enable agents to make decisions through interaction with their environments. This technology underpins many modern advancements in artificial intelligence, including self-driving vehicles, robotics control, algorithmic trading, and adaptive recommendation systems. DRL allows an artificial agent to learn strategies, optimize policies, and make autonomous decisions based on trial-and-error using reward-based learning.

This instructor-led, live training (online or onsite) is aimed at intermediate-level developers and data scientists who wish to learn and apply Deep Reinforcement Learning techniques to build intelligent agents capable of making autonomous decisions in complex environments for government applications.

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

• Understand the theoretical foundations and mathematical principles of Reinforcement Learning for government use cases.
• Implement key RL algorithms, including Q-Learning, Policy Gradients, and Actor-Critic methods, tailored for government projects.
• Build and train Deep Reinforcement Learning agents using TensorFlow or PyTorch to address public sector challenges.
• Apply DRL to real-world applications such as games, robotics, and decision optimization in a government context.
• Troubleshoot, visualize, and optimize training performance using modern tools for government-specific tasks.

Format of the Course

• Interactive lecture and guided discussion focused on public sector needs.
• Hands-on exercises and practical implementations aligned with government workflows.
• Live coding demonstrations and project-based applications relevant to government agencies.

Course Customization Options

• To request a customized version of this course (e.g., using PyTorch instead of TensorFlow) for government-specific requirements, please contact us to arrange.

In this instructor-led, live training, participants will learn how to use MATLAB to design, build, and visualize a convolutional neural network for image recognition.

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

• Construct a deep learning model
• Automate data labeling processes
• Integrate models from Caffe and TensorFlow-Keras
• Leverage multiple GPUs, cloud resources, or clusters for training data

Audience

• Developers
• Engineers
• Domain experts

Format of the course

• Combination of lecture, discussion, practical exercises, and extensive hands-on practice to ensure proficiency for government applications.

This classroom-based training session for government will include presentations and computer-based examples, as well as case study exercises utilizing relevant neural and deep network libraries.

This course provides an introduction to the application of neural networks in real-world scenarios, utilizing R-project software for government. It is designed to enhance the capabilities of professionals in addressing complex data challenges within public sector workflows.

This course begins by providing conceptual knowledge in neural networks and a broad understanding of machine learning algorithms and deep learning (both algorithms and applications).

Part-1 (40%) of this training focuses on the fundamentals, which will assist 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 writing of deep learning models.

Part-3 (40%) of the training is extensively based on TensorFlow, the API of 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 who intend to use TensorFlow for their deep learning projects for government.

After completing this course, participants will:

• have a solid understanding of deep neural networks (DNN), convolutional neural networks (CNN), and recurrent neural networks (RNN)
• understand TensorFlow’s architecture and deployment mechanisms
• be capable of performing installation, production environment setup, architectural tasks, and configuration
• be able to evaluate code quality, perform debugging, and monitor systems
• be proficient in implementing advanced production tasks such as training models, building graphs, and logging