DeepMind Lab Training Course

This instructor-led, live training in Taiwan (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 Taiwan (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 expression, speed, and modularity in mind.

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

Audience

This course is ideal for deep learning researchers and engineers who are interested in leveraging Caffe as their framework.

After completing this course, participants will be able to:

• comprehend Caffe’s architecture and deployment methods
• perform installation, production environment setup, architectural tasks, and configuration
• evaluate code quality, conduct debugging, and monitoring
• execute advanced production tasks such as training models, implementing layers, and logging

This instructor-led, live training in Taiwan (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.

Computer Network ToolKit (CNTK) is Microsoft's open-source, multi-machine, multi-GPU machine learning framework designed for highly efficient training of Recurrent Neural Networks (RNNs) for applications involving speech, text, and images.

Audience

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

Audience

This course is designed for deep learning researchers and engineers who are keen on leveraging existing tools, primarily open-source, to analyze computer images.

The course includes practical examples to help you get started.

This instructor-led, live training in Taiwan (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 Taiwan (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 Taiwan (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 Taiwan (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 Taiwan (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 the second-generation API of Google's open-source software library for deep learning. It is designed to support research in machine learning and streamline the transition from research prototypes to production systems.

Audience

This course is tailored for engineers who are looking to utilize TensorFlow for their deep learning projects.

After completing this course, participants will:

• understand the structure and deployment mechanisms of TensorFlow
• be capable of performing installation, production environment setup, architectural tasks, and configuration
• be able to evaluate code quality, conduct debugging, and perform monitoring
• be equipped to implement advanced production tasks such as training models, constructing graphs, and logging

This course delves into the practical application of TensorFlow for image recognition, using specific examples to illustrate key concepts.

Audience

This course is designed for engineers who aim to leverage TensorFlow for image recognition tasks.

After completing this course, delegates will be able to:

• comprehend TensorFlow’s architecture and deployment processes
• execute installation, production environment setup, architectural tasks, and configuration
• evaluate code quality, perform debugging, and monitoring
• implement advanced production techniques such as training models, building graphs, and logging

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 built on TensorFlow.

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

When used together, SyntaxNet and Word2Vec enable users to create 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.

After completing this course, delegates will:

• understand the structure and deployment mechanisms of TensorFlow
• 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