Advanced Machine Learning with R 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.

The instructor-led, live training session, offered via US (virtual or in-person), is designed for biological scientists seeking to comprehend the underlying mechanics of AlphaFold and apply its predictive models to support experimental workflows. This program is specifically developed for government professionals involved in biological research.

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

• Grasp the fundamental principles governing AlphaFold.
• Articulate the operational mechanisms of the AlphaFold system.
• Accurately interpret AlphaFold predictions and associated analytical results.

Artificial Neural Networks are computational models utilized in the creation of Artificial Intelligence (AI) systems designed to perform tasks that require "intelligent" capabilities. These networks are frequently employed in Machine Learning (ML) applications, which represent one approach to implementing AI. Deep Learning, a specialized subset of ML, further advances these techniques for government and other sectors.

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 participants to complete an AI project upon finishing the course. This training is designed to enhance skills and capabilities for government professionals, ensuring they are well-equipped to integrate AI solutions into their workflows.

This instructor-led, live training in US (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python, ensuring that the code is easy to debug. By the end of this training, participants will be able to: - Set up the necessary 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 while leveraging GPUs for high performance. This training is designed to align with best practices for government research and development initiatives.

This instructor-led, live training (online or onsite) provides an introduction to the field of pattern recognition and machine learning. It covers practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics for government use. By the end of this training, participants will be able to: - Apply core statistical methods to pattern recognition tasks. - Utilize key 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 different models.

Deep Reinforcement Learning (DRL) integrates the principles of reinforcement learning with deep learning architectures, enabling agents to make decisions through interaction with their environments. This technology is pivotal in driving many modern artificial intelligence advancements, 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 learning using a reward system.

This instructor-led, live training (online or onsite) is designed for intermediate-level developers and data scientists who wish to gain proficiency in Deep Reinforcement Learning techniques. The goal is to build intelligent agents capable of autonomous decision-making in complex environments, which can be particularly useful for government applications.

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

• Understand the theoretical foundations and mathematical principles underlying Reinforcement Learning.
• Implement key RL algorithms such as Q-Learning, Policy Gradients, and Actor-Critic methods.
• Develop and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
• Apply DRL to real-world scenarios, including games, robotics, and decision optimization for government use cases.
• Troubleshoot, visualize, and optimize the training performance of DRL models 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, such as using PyTorch instead of TensorFlow, please contact us to arrange.

This instructor-led, live training in US (online or onsite) is aimed at intermediate-level developers, data scientists, and AI practitioners who wish to leverage TensorFlow Lite for Edge AI applications for government.

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

• Understand the fundamentals of TensorFlow Lite and its role in Edge AI for government.
• Develop and optimize AI models using TensorFlow Lite for deployment in government settings.
• Deploy TensorFlow Lite models on various edge devices used in public sector applications.
• Utilize tools and techniques for model conversion and optimization suitable for government workflows.
• Implement practical Edge AI applications using TensorFlow Lite to enhance public sector operations.

This instructor-led, live training (online or onsite) is designed for data scientists who aim to accelerate real-time machine learning applications and deploy them at scale for government use.

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

• Install the OpenVINO toolkit for government applications.
• Accelerate a computer vision application using an FPGA in a government environment.
• Execute various CNN layers on the FPGA to enhance performance.
• Scale the application across multiple nodes within a Kubernetes cluster to support government operations.

This instructor-led, live training (available online or onsite) is designed for developers or data scientists who wish to utilize Horovod to execute distributed deep learning training and scale it up to operate across multiple GPUs in parallel. By the end of this training, participants will be able to: - Establish the required development environment to initiate deep learning training. - Install and configure Horovod to train models using TensorFlow, Keras, PyTorch, and Apache MXNet. - Scale deep learning training with Horovod to run efficiently on multiple GPUs, enhancing performance for government applications.

This instructor-led, live training in US (online or onsite) is aimed at engineers who wish to develop, deploy, and execute machine learning models on very small embedded devices for government use.

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

• Install TensorFlow Lite for government applications.
• Load machine learning models onto an embedded device to enable it to perform tasks such as speech detection and image classification without network connectivity.
• Incorporate artificial intelligence into hardware devices to enhance functionality and efficiency in public sector workflows.

This course begins with an introduction to the conceptual knowledge of neural networks and machine learning algorithms, including deep learning (algorithms and applications). Part-1 (40%) of this training focuses on fundamental concepts, which will assist in selecting the appropriate technology: TensorFlow, Caffe, Theano, DeepDrive, Keras, etc. Part-2 (20%) introduces Theano, a Python library that simplifies the process of writing deep learning models. Part-3 (40%) 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 who intend to use TensorFlow for their deep learning projects for government. Upon completing this course, participants will: - Have a comprehensive understanding of deep neural networks (DNN), convolutional neural networks (CNN), and recurrent neural networks (RNN). - Understand the structure and deployment mechanisms of TensorFlow. - Be capable of performing installation, production environment setup, architecture tasks, and configuration. - Be able to assess code quality, perform debugging, and monitoring. - Be proficient in implementing advanced production tasks such as training models, building graphs, and logging.

This instructor-led, live training (online or onsite) is designed for government professionals at an advanced level who wish to explore state-of-the-art XAI techniques for deep learning models, with a focus on building interpretable AI systems for government. 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.