OpenFace: Creating Facial Recognition Systems Training Course

This instructor-led, live training in the UAE (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.

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

• Understand advanced deep learning architectures and techniques for text-to-image generation.
• Implement complex models and optimizations for high-quality image synthesis.
• Optimize performance and scalability for large datasets and complex models.
• Tune hyperparameters for better model performance and generalization.
• Integrate Stable Diffusion with other deep learning frameworks and tools

This instructor-led, live training in the UAE (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.

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

• Understand the basic principles of AlphaFold.
• Learn how AlphaFold works.
• Learn how to interpret AlphaFold predictions and results.

Caffe is a deep learning framework designed with emphasis on expressiveness, speed, and modularity.

This course delves into the application of Caffe as a deep learning tool for image recognition, using MNIST as an illustrative example.

Audience

This course is ideal for deep learning researchers and engineers who wish to leverage Caffe as their framework.

Upon completion of this course, participants will be able to:

• grasp the structure and deployment methods of Caffe
• execute installation tasks, manage production environments, and configure architecture
• evaluate code quality, conduct debugging, and monitor performance
• implement advanced functionalities such as training models, creating layers, and logging

This instructor-led, live training in the UAE (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while making the code easy to debug.

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

• Set up the necessary development environment to start developing neural network models.
• Define and implement neural network models using a comprehensible source code.
• Execute examples and modify existing algorithms to optimize deep learning training models while leveraging GPUs for high performance.

The Computer Network ToolKit (CNTK) is an open-source, multi-machine, multi-GPU machine learning framework developed by Microsoft. It specializes in efficient RNN training for applications involving speech, text, and images.

Audience

This course targets engineers and architects who wish to integrate CNTK into their projects.

Audience

This course is ideal for Deep Learning researchers and engineers who wish to leverage existing tools, primarily open-source, for the analysis of digital imagery.

The course includes practical examples.

This instructor-led, live training in the UAE (online or onsite) is aimed at intermediate-level developers, data scientists, and AI practitioners who wish to leverage TensorFlow Lite for 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.
• Develop and optimize AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models on various edge devices.
• Utilize tools and techniques for model conversion and optimization.
• Implement practical Edge AI applications using TensorFlow Lite.

This instructor-led, live training in the UAE (online or onsite) is aimed at 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.
• Accelerate a computer vision application using an FPGA.
• Execute different CNN layers on the FPGA.
• Scale the application across multiple nodes in a Kubernetes cluster.

This instructor-led, live training in the UAE (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.

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.

This instructor-led, live training in the UAE (online or onsite) is aimed at technical persons who wish to apply deep learning model to image recognition applications.

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

• Install and configure Keras.
• Quickly prototype deep learning models.
• Implement a convolutional network.
• Implement a recurrent network.
• Execute a deep learning model on both a CPU and GPU.

This instructor-led, live training in (online or onsite) is aimed at 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 how it works for image generation.
• Build and train Stable Diffusion models for image generation tasks.
• Apply Stable Diffusion to various image generation scenarios, such as inpainting, outpainting, and image-to-image translation.
• Optimize the performance and stability of Stable Diffusion models.

This instructor-led, live training in the UAE (online or onsite) is aimed at engineers who wish to write, load and run machine learning models on very small embedded devices.

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

• Install TensorFlow Lite.
• Load machine learning models onto an embedded device to enable it to detect speech, classify images, etc.
• Add AI to hardware devices without relying on network connectivity.

TensorFlow is an open-source software library for Deep Learning developed by Google. It serves as a second-generation API aimed at simplifying machine learning research while enabling swift transitions from prototypes to operational systems.

Audience

This course targets engineers who wish to leverage TensorFlow in their Deep Learning initiatives.

Upon completion of this course, participants will:

• grasp the architecture and deployment strategies of TensorFlow
• be proficient in installation, environment setup, architectural tasks, and configuration
• possess the skills to evaluate code quality, debug, and monitor systems
• master advanced production techniques such as training models, constructing graphs, and logging data

This course delves into the practical application of TensorFlow for image recognition through specific examples.

Audience

The course is designed for engineers aiming to leverage TensorFlow for image recognition tasks.

Upon completion, participants will be able to:

• grasp the structure and deployment methods of TensorFlow
• execute installation, production environment setup, architecture tasks, and configurations
• evaluate code quality, conduct debugging, and monitor performance
• implement advanced production techniques such as training models, constructing graphs, and logging data

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 utilized to create vector representations of words, known as "word embeddings." It is a highly efficient model for generating word embeddings from raw text. Word2vec offers two models: the Continuous Bag-of-Words (CBOW) and the Skip-Gram model (referenced in Chapter 3.1 and 3.2 of Mikolov et al.).

When used together, SyntaxNet and Word2Vec enable users to generate learned embedding models from natural language input.

Audience

This course is aimed at developers and engineers who plan to work with SyntaxNet and Word2Vec models within their TensorFlow graphs.

Upon completion of this course, participants will:

• grasp the structure and deployment methods of TensorFlow
• be capable of performing installation, setting up production environments, configuring architecture tasks
• assess code quality, conduct debugging, and monitoring
• implement advanced production techniques such as training models, embedding terms, constructing graphs, and logging