Deep Learning Training in Virginia

This instructor-led, live training in Virginia (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 advanced-level professionals who aim to deepen their understanding of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab. By the end of this training, participants will be able to: - Build and train convolutional neural networks (CNNs) using TensorFlow. - Leverage Google Colab for scalable and efficient cloud-based model development. - Implement image preprocessing techniques for computer vision tasks. - Deploy computer vision models for real-world applications. - Use transfer learning to enhance the performance of CNN models. - Visualize and interpret the results of image classification models. This training is tailored to ensure that professionals can apply these skills effectively in their roles, particularly in projects for government.

This instructor-led, live training in [location] (online or onsite) is designed for intermediate-level data scientists and developers who aim to understand and apply deep learning techniques using the Google Colab environment. By the end of this training, participants will be able to: - Set up and navigate Google Colab for government deep learning projects. - Understand the fundamentals of neural networks. - Implement deep learning models using TensorFlow. - Train and evaluate deep learning models. - Utilize advanced features of TensorFlow for deep learning.

This instructor-led, live training (online or onsite) is designed for government professionals at the advanced level who wish to specialize in cutting-edge deep learning techniques for Natural Language Understanding (NLU). By the end of this training, participants will be able to: - Understand the key differences between NLU and Natural Language Processing (NLP) models. - Apply advanced deep learning techniques to NLU tasks. - Explore deep architectures such as transformers and attention mechanisms. - Leverage future trends in NLU for building sophisticated AI systems for government use.

This instructor-led, live training in Virginia (online or onsite) is designed for intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts who wish to expand their knowledge and skills in deep learning for text-to-image generation. By the end of this training, participants will be able to: - Understand advanced deep learning architectures and techniques for generating images from text. - Implement complex models and optimizations to produce high-quality image synthesis. - Optimize performance and scalability for large datasets and sophisticated models. - Tune hyperparameters to enhance model performance and generalization. - Integrate Stable Diffusion with other deep learning frameworks and tools, ensuring alignment with public sector workflows and governance requirements for government.

This instructor-led, live training (available online or onsite) is designed for advanced-level professionals who seek to harness artificial intelligence techniques to transform drug discovery and development processes for government and industry. By the end of this training, participants will be able to: - Comprehend the role of AI in drug discovery and development. - Apply machine learning methods to predict molecular properties and interactions. - Utilize deep learning models for virtual screening and lead optimization. - Incorporate AI-driven approaches into the clinical trial process.

This instructor-led, live training in [location] (online or onsite) is aimed at biologists who wish to understand how AlphaFold operates and utilize AlphaFold models as guides in their experimental studies for government research. By the end of this training, participants will be able to: - Understand the fundamental principles of AlphaFold. - Learn how AlphaFold functions. - Learn how to interpret AlphaFold predictions and results.

This instructor-led, live training (available online or onsite) is designed for government data scientists and machine learning engineers at the beginner to intermediate level who wish to enhance the performance of their deep learning models. By the end of this training, participants will be able to: - Comprehend the principles of distributed deep learning. - Install and configure DeepSpeed for government applications. - Scale deep learning models on distributed hardware using DeepSpeed. - Implement and experiment with DeepSpeed features to optimize performance and improve memory efficiency.

This instructor-led, live training (online or onsite) is designed for government developers at beginner to intermediate levels who wish to utilize Large Language Models for various natural language tasks. By the end of this training, participants will be able to: - Set up a development environment that includes a popular LLM. - Create and fine-tune a basic LLM on a custom dataset. - Apply LLMs to different natural language tasks such as text summarization, question answering, text generation, and more. - Debug and evaluate LLMs using tools like TensorBoard, PyTorch Lightning, and Hugging Face Datasets. This training is tailored to enhance the capabilities of government developers in leveraging advanced technologies for government applications.

This instructor-led, live training (available online or onsite) is designed for data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases. By the end of this training, participants will be able to: - Understand the principles of Stable Diffusion and its application in image generation. - Develop and train Stable Diffusion models for various image generation tasks. - Apply Stable Diffusion techniques to different scenarios, such as inpainting, outpainting, and image-to-image translation. - Enhance the performance and stability of Stable Diffusion models. This training is tailored to align with public sector workflows, governance, and accountability requirements for government.

In this instructor-led, live training for government participants in [location], attendees will gain proficiency in the most relevant and cutting-edge machine learning techniques using Python. Through hands-on exercises, they will develop a series of demo applications involving image, music, text, and financial data. By the end of this training, participants will be able to: - Implement machine learning algorithms and techniques for solving complex problems. - Apply deep learning and semi-supervised learning methods to applications involving image, music, text, and financial data. - Optimize Python algorithms to their maximum potential. - Utilize libraries and packages such as NumPy and Theano.

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.

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.

In this instructor-led, live training for government participants, attendees will learn how to implement deep learning models for the telecommunications sector using Python. The course will guide participants through the creation of a deep learning credit risk model. By the end of this training, participants will be able to: - Comprehend the core principles of deep learning. - Explore the various applications and uses of deep learning in the telecom industry. - Utilize Python, Keras, and TensorFlow to develop deep learning models for telecom. - Construct their own deep learning customer churn prediction model using Python.

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 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 instructor-led, live training in Virginia (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 relevant to public sector workflows. - Write and understand procedural, functional, and object-oriented programming code suitable for government applications. - Implement machine learning techniques such as classification, clustering, and neural networks to support data-driven decision-making for government. - Develop AI solutions using rules engines and expert systems for problem-solving in governance and accountability contexts.

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 for government. Participants have the option to extend their learning by one additional day to work on an AI project upon completing the initial course.

This instructor-led, live training in Virginia (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 models utilized in the development of Artificial Intelligence (AI) systems designed to perform complex and intelligent tasks. These networks are frequently employed in Machine Learning (ML) applications, which represent one form of AI implementation. Deep Learning, a specialized subset of ML, further enhances these capabilities by leveraging multi-layered neural network architectures for government and other sectors.

Caffe is a deep learning framework designed with expression, speed, and modularity in mind.

This course explores the application of Caffe as a deep learning framework for image recognition using MNIST as an example.

Audience

This course is suitable for deep learning researchers and engineers interested in utilizing Caffe as a framework for government applications.

After completing this course, delegates will be able to:

• Understand Caffe’s structure and deployment mechanisms
• Perform installation, production environment setup, architecture tasks, and configuration
• Evaluate code quality, conduct debugging, and implement monitoring
• Implement advanced production techniques such as training models, implementing layers, and logging

This instructor-led, live training in Virginia (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.

The Computer Network ToolKit (CNTK) is an open-source, multi-machine, multi-GPU machine learning framework developed by Microsoft. It is highly efficient for training recurrent neural networks (RNNs) and is applicable to speech, text, and image processing. **Audience** This course is designed for engineers and architects who aim to integrate CNTK into their projects for government and other sectors.

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.

This course provides a comprehensive overview of Deep Learning, designed to introduce the fundamental concepts without delving too deeply into specific methodologies. It is ideal for individuals and organizations seeking to enhance their predictive accuracy through the application of Deep Learning techniques for government and other public sector initiatives.

This instructor-led, live training in Virginia (online or onsite) is aimed at researchers and developers who wish to install, set up, customize, and use the DeepMind Lab platform for government applications to develop general artificial intelligence and machine learning systems.

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

• Customize DeepMind Lab to build and run an environment that meets specific learning and training requirements for government projects.
• Leverage DeepMind Lab's 3D simulation environment to train learning agents from a first-person viewpoint, enhancing the realism of training scenarios for government applications.
• Facilitate agent evaluation within a 3D game-like world to develop advanced intelligence capabilities aligned with public sector workflows and governance.

Machine learning, a subset of artificial intelligence, enables computers to learn and improve from experience without being explicitly programmed. For government applications, deep learning, a specialized area within machine learning, utilizes advanced techniques like neural networks to analyze and interpret complex data patterns and structures.

This instructor-led, live training (online or onsite) is aimed at business analysts, data scientists, and developers who wish to build and implement deep learning models to enhance revenue growth and address challenges in the public sector. By the end of this training, participants will be able to: - Understand the fundamental concepts of machine learning and deep learning. - Gain insights into the future of business and industry through the application of ML and DL. - Develop business strategies and solutions using deep learning techniques. - Learn how to apply data science and deep learning to solve complex business problems. - Build deep learning models using tools such as Python, Pandas, TensorFlow, CNTK, Torch, Keras, and others, tailored for government use cases.

Machine learning is a subset of Artificial Intelligence that enables computers to learn without explicit programming. Deep learning, a specialized area within machine learning, employs methods for data representation and structures such as neural networks. Python is a high-level programming language renowned for its clear syntax and readability. In this instructor-led, live training, participants will gain the skills to implement deep learning models for banking using Python. The course includes step-by-step guidance on creating a deep learning credit risk model. By the end of this training, participants will be able to: - Understand the core principles of deep learning. - Explore the applications and benefits of deep learning in the banking sector. - Utilize Python, Keras, and TensorFlow to develop deep learning models for banking. - Construct their own deep learning credit risk model using Python. **Audience** - Developers - Data Scientists **Format of the Course** - Part lecture, part discussion, with exercises and extensive hands-on practice tailored for government and industry professionals.

In this instructor-led, live training session, participants will learn to utilize Python libraries for natural language processing (NLP) as they develop an application that processes a set of images and generates descriptive captions. By the end of this training, participants will be able to: - Design and code deep learning models for NLP using Python libraries. - Create Python scripts that analyze a large collection of images and extract relevant keywords. - Develop Python code that generates coherent captions based on the identified keywords. This training is designed to enhance technical skills in data processing and machine learning, specifically tailored for government applications.

Audience

This course is designed for deep learning researchers and engineers who are interested in leveraging available tools, primarily open source, to analyze computer images. The course provides practical, working examples to enhance skills and knowledge for government and other public sector applications.

This instructor-led, live training (online or onsite) is designed for government data scientists who wish to accelerate real-time machine learning applications and deploy them at scale. By the end of this training, participants will be able to: - Install the OpenVINO toolkit for government use. - Accelerate a computer vision application using an FPGA. - Execute various CNN layers on the FPGA. - Scale the application across multiple nodes in a Kubernetes cluster.

This instructor-led, live training (available online or onsite) is designed for government data scientists who wish to utilize TensorFlow to analyze potential fraud data. By the end of this training, participants will be able to: - Develop a fraud detection model using Python and TensorFlow. - Construct linear regressions and linear regression models to predict fraudulent activities. - Create an end-to-end AI application for analyzing fraud data for government use.

This instructor-led, live training in Virginia (online or onsite) is aimed at developers or data scientists who wish to use Horovod to run distributed deep learning trainings and scale it up to run across multiple GPUs in parallel for government applications. By the end of this training, participants will be able to: - Set up the necessary development environment to start running deep learning trainings. - Install and configure Horovod to train models with TensorFlow, Keras, PyTorch, and Apache MXNet. - Scale deep learning training with Horovod to run on multiple GPUs for efficient and scalable model development.

This instructor-led, live training in Virginia (online or onsite) is designed for technical personnel who wish to apply deep learning models to image recognition applications for government. By the end of this training, participants will be able to: - Install and configure Keras. - Rapidly prototype deep learning models. - Implement a convolutional neural network. - Implement a recurrent neural network. - Execute a deep learning model on both CPU and GPU systems.

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 - Train data using multiple GPUs, cloud resources, or clusters **Audience** - Developers - Engineers - Domain experts **Format of the Course** - Part lecture, part discussion, with exercises and substantial hands-on practice for government applications.

This instructor-led, live training in Virginia (online or onsite) is designed for government professionals at the beginner to intermediate level who wish to enhance their understanding of machine learning algorithms, deep learning techniques, and AI-driven decision-making. The course provides hands-on experience with machine learning concepts, deep learning models, and practical implementations using R. By the end of this training, participants will be able to: - Understand the fundamentals of machine learning and deep learning. - Apply various machine learning algorithms for regression, classification, clustering, and anomaly detection. - Utilize deep learning architectures such as artificial neural networks (ANNs). - Implement both supervised and unsupervised learning models. - Evaluate model performance and optimize hyperparameters. - Use R for data analysis, visualization, and machine learning applications for government.

This training is designed for individuals seeking to understand the fundamentals of neural networks and their applications for government.

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 instructor-led, live training in Virginia (online or onsite) is aimed at software engineers who wish to program in Python with OpenCV 4 for deep learning applications. By the end of this training, participants will be able to: - View, load, and classify images and videos using OpenCV 4. - Implement deep learning techniques in OpenCV 4 with TensorFlow and Keras. - Run deep learning models and generate comprehensive reports from images and videos, enhancing capabilities for government projects.

In this instructor-led, live training, participants will learn advanced techniques for Machine Learning with R as they develop a real-world application. By the end of this training, participants will be able to: - Understand and implement unsupervised learning techniques. - Apply clustering and classification methods to make predictions based on real-world data. - Visualize data to quickly gain insights, make decisions, and further refine analysis. - Improve the performance of a machine learning model using hyper-parameter tuning. - Deploy a model into production for use in a larger application. - Apply advanced machine learning techniques to address questions involving social network data, big data, and more, ensuring alignment with public sector workflows and governance for government.

This course provides an introduction to utilizing neural networks for solving real-world problems with R-project software. It is designed to equip participants with the skills necessary to apply these advanced techniques effectively and efficiently, particularly in contexts relevant to government operations and data analysis.

This instructor-led, live training (online or onsite) is aimed at developers and data scientists who wish to utilize TensorFlow 2.x for government to build predictors, classifiers, generative models, neural networks, and more. By the end of this training, participants will be able to: - Install and configure TensorFlow 2.x. - Understand the advantages of TensorFlow 2.x over previous versions. - Construct deep learning models. - Implement an advanced image classifier. - Deploy a deep learning model to the cloud, mobile devices, and IoT systems.

This instructor-led, live training in Virginia (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.

In this instructor-led, live training in Virginia (online or onsite), participants will learn how to configure and use TensorFlow Serving to deploy and manage machine learning models in a production environment for government.

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

• Train, export, and serve various TensorFlow models.
• Test and deploy algorithms using a unified architecture and set of APIs.
• Extend TensorFlow Serving to support other types of models beyond those developed with TensorFlow.

TensorFlow is the second-generation API of Google's open-source software library for deep learning. This system is designed to support research in machine learning and to streamline the transition from research prototype to production systems. ### Audience This course is intended for engineers seeking to use TensorFlow for their deep learning projects, particularly those working in government agencies or public sector organizations. After completing this course, delegates will: - Understand TensorFlow’s structure and deployment mechanisms - Be able to perform installation, production environment setup, architecture tasks, and configuration - Be able to assess code quality, conduct debugging, and implement monitoring - Be able to execute advanced production tasks such as training models, building graphs, and logging for government applications

This course delves into the application of TensorFlow for image recognition, providing specific examples to enhance understanding. **Audience** This course is designed for engineers who aim to leverage TensorFlow for government purposes in the field of Image Recognition. **Learning Objectives** Upon completing this course, participants will be able to: - Understand TensorFlow’s architecture and deployment processes - Perform installation, production environment setup, and configuration tasks - Evaluate code quality, conduct debugging, and implement monitoring - Execute advanced production tasks such as training models, constructing graphs, and logging for government applications

This instructor-led, live training in [location] (online or onsite) is aimed at data scientists who wish to transition from training a single machine learning (ML) model to deploying multiple ML models into production environments. By the end of this training, participants will be able to: - Install and configure TensorFlow Extended (TFX) and supporting third-party tools for government use. - Use TFX to create and manage a comprehensive ML production pipeline. - Work with TFX components to conduct modeling, training, serving inference, and managing deployments. - Deploy machine learning features to web applications, mobile applications, IoT devices, and other platforms.

TensorFlow™ is an open-source software library designed for numerical computation using data flow graphs.

SyntaxNet is a neural-network framework for natural language processing that operates within TensorFlow.

Word2Vec is a method used to learn vector representations of words, known as "word embeddings." Word2Vec is particularly efficient in learning these embeddings from raw text and offers two models: the Continuous Bag-of-Words (CBOW) model and the Skip-Gram model (as detailed in Chapter 3.1 and 3.2 of Mikolov et al.).

When used together, SyntaxNet and Word2Vec enable the generation of learned embedding models from natural language input.

Audience

This course is designed for developers and engineers who intend to work with SyntaxNet and Word2Vec models within their TensorFlow graphs for government applications.

After completing this course, participants will:

• Understand TensorFlow’s structure and deployment mechanisms
• Be capable of performing installation, production environment setup, architectural tasks, and configuration
• Be able to evaluate code quality, perform debugging, and monitoring
• Be proficient in implementing advanced production tasks such as training models, embedding terms, building graphs, and logging

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.