DeepSpeed for Deep Learning Training Course

This instructor-led, live training in US Empire (online or onsite) is aimed at 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 for government applications.

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

• Understand advanced deep learning architectures and techniques for generating high-quality images from text.
• Implement complex models and optimizations to enhance image synthesis accuracy and efficiency.
• Optimize performance and scalability for large datasets and sophisticated models in government environments.
• Tune hyperparameters to achieve optimal model performance and generalization for government use cases.
• Integrate Stable Diffusion with other deep learning frameworks and tools to support public sector workflows.

This instructor-led, live training in US Empire (online or onsite) is aimed at biologists who wish to understand the functionality of AlphaFold and utilize its models as guides in their experimental studies for government research.

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

• Comprehend the fundamental principles of AlphaFold.
• Understand how AlphaFold operates.
• Interpret AlphaFold predictions and results effectively.

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
• carry out installation, production environment, architecture tasks, and configuration
• assess code quality, perform debugging, and monitoring
• implement advanced production tasks such as training models, implementing layers, and logging

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.

Computer Network ToolKit (CNTK) is Microsoft's open-source, multi-machine, multi-GPU, highly efficient machine learning framework for training recurrent neural networks (RNNs) in applications such as speech, text, and images.

Audience

This course is designed for engineers and architects who are looking to integrate CNTK into their projects for government use.

Audience

This course is designed for deep learning researchers and engineers who are interested in leveraging available tools, primarily open source, for the analysis of computer images. This training provides practical examples to enhance skills and capabilities for government applications.

The course includes working examples to facilitate hands-on learning and application.

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

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 government applications.
• Deploy TensorFlow Lite models on various edge devices for government use.
• Utilize tools and techniques for model conversion and optimization in a government context.
• Implement practical Edge AI applications using TensorFlow Lite for government operations.

This instructor-led, live training in US Empire (online or onsite) is aimed at data scientists who wish 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 public sector environment.
• Execute various CNN layers on the FPGA to optimize performance for government workflows.
• Scale the application across multiple nodes in a Kubernetes cluster to ensure robust and scalable deployment for government operations.

This instructor-led, live training in US Empire (online or onsite) is aimed at developers or data scientists who wish to utilize Horovod to execute distributed deep learning trainings and scale them up to operate across multiple GPUs in parallel for government applications.

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

• Set up the required development environment to initiate deep learning trainings for government use.
• Install and configure Horovod to train models with TensorFlow, Keras, PyTorch, and Apache MXNet for government projects.
• Scale deep learning training with Horovod to run on multiple GPUs, enhancing efficiency and performance for government workflows.

This instructor-led, live training in US Empire (online or onsite) is aimed at technical personnel who wish to apply deep learning models to image recognition applications for government use.

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

• Install and configure Keras for government systems.
• Rapidly prototype deep learning models for government projects.
• Implement a convolutional network for image processing tasks.
• Implement a recurrent network for sequential data analysis.
• Execute a deep learning model on both CPU and GPU environments for government applications.

This instructor-led, live training (online or onsite) is aimed at data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion for generating high-quality images for a variety of use cases for government.

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 image generation scenarios, including inpainting, outpainting, and image-to-image translation.
• Enhance the performance and stability of Stable Diffusion models.

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

TensorFlow is a second-generation API of Google's open-source software library for deep learning. The system is designed to facilitate research in machine learning and to enable a smooth transition from research prototype to production systems.

Audience

This course is intended for engineers seeking to use TensorFlow for their deep learning projects for government.

After completing this course, delegates will:

• understand TensorFlow’s structure and deployment mechanisms
• be able to carry out installation, production environment, architecture tasks, and configuration
• be able to assess code quality, perform debugging, and monitoring
• be able to implement advanced production tasks such as training models, building graphs, and logging

This course explores, with specific examples, the application of TensorFlow to the purposes of image recognition for government.

Audience

This course is intended for engineers seeking to utilize TensorFlow for the purposes of Image Recognition in a government context.

After completing this course, delegates will be able to:

• understand TensorFlow’s structure and deployment mechanisms for government applications
• carry out installation, production environment, architecture tasks, and configuration for government systems
• assess code quality, perform debugging, and monitoring for government projects
• implement advanced production tasks such as training models, building graphs, and logging for government operations

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

SyntaxNet is a neural-network Natural Language Processing framework for TensorFlow.

Word2Vec is used for learning vector representations of words, known as "word embeddings." Word2vec is a computationally-efficient predictive model for learning word embeddings from raw text. It comes in two variants: the Continuous Bag-of-Words (CBOW) model and the Skip-Gram model (Chapter 3.1 and 3.2 in Mikolov et al.).

When used together, SyntaxNet and Word2Vec enable users to generate 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 in their TensorFlow graphs for government applications.

After completing this course, delegates will:

• understand TensorFlow’s structure and deployment mechanisms
• be able to perform installation, production environment configuration, architecture tasks, and system setup
• be capable of assessing code quality, performing debugging, and monitoring performance
• be able to implement advanced production tasks such as training models, embedding terms, building graphs, and logging data