Matlab for Deep Learning Training Course

This instructor-led, live training in US (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 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.

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 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.

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.

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 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 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 in US (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 US (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.

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.

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.

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

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