大會實作訓練

深度學習是實作 AI 的強大方式，透過其所使用的多層次人工智慧神經網路，即可在物體偵測、語音辨識和語言翻譯等工作上達到最為先進的精確度。電腦現在可使用深度學習技術，從資料中學習和辨識出對專家編寫的應用軟體而言過於複雜或細微的模式。

在本實作坊中，你將進行電腦視覺和自然語言處理方面的實作練習，藉此瞭解深度學習的運作方式。你將會從零開始訓練深度學習模型、學習工具和技巧，追求高度準確的成果。也會學習如何運用免費的頂尖預先訓練好的模型，如此可以節省時間，並讓深度學習應用程式即刻運作。參加本實作坊可學會：

• 練習訓練深度學習模型所需的基本技術和工具
• 熟悉通用的深度學習資料類型和模型架構
• 透過資料增強來強化資料集以提升模型精準度
• 運用模型之間的遷移學習，以較少的資料和運算達成高效成果
• 使用現代的深度學習框架，更具信心地打造自己的專案

課程結束後可進行線上測驗，通過後可取得電子認證證書。

由於交通攝影機日益增加、自駕車蓬勃發展和智慧城市的前景可期，對於更快速及更有效率的物件偵測與追蹤模型的需求也逐漸提高。利用 NVIDIA DeepStream 實行智慧影像分析（IVA ），進行偵測、辨識、追蹤和定義物體的軌跡，並透過訓練和評估深度學習模型，搭配轉移學習技術來提升模型的效率和準確性，讓大規模影像分析變得輕而易舉。本實作課程您將學會：

• 使用硬體加速解碼方法，有效率地處理和準備影片內容
• 訓練和評估深度學習模型，並運用「轉移學習」技術來提升這些模型的效率和準確性，以減少資料匱乏的問題
• 探索研發高品質神經網路模型所需的策略與平衡，以追蹤龐大影片資料集中的移動物體

課程結束後可進行線上測驗，通過後可取得電子認證證書。

越來越多的現代深度學習挑戰利用了更大的資料集和更複雜的模型，導致需要更大量的計算效能來更有效地訓練模型。本實作課程將教您使用多個 GPU 訓練深度神經網絡的技術，以縮短資料密集型應用程式所需的訓練時間。通過使用深度學習工具、框架和工作流程來進行神經網絡訓練，您將學習實現 Horovod 多 GPU 的概念，以減少編寫高效能的分佈式軟體的複雜性。您將學會：

• 多 GPU 訓練方法
• 大型訓練會碰上的演算法與工程挑戰
• 利用重要工程技術應對大規模運算的任務，並克服上述挑戰的重要技術
• 如何將單一 GPU 實作轉換為 Horovod 多 GPU 實作
• 在使用多個 GPU 進行訓練時，維持高準確度的技術

課程結束後可進行線上測驗，通過後可取得電子認證證書。

The CUDA computing platform enables the acceleration of CPU-only applications to run on the world’s fastest massively parallel GPUs. Experience C/C++ application acceleration by:

- Accelerating CPU-only applications to run their latent parallelism on GPUs
- Utilizing essential CUDA memory management techniques to optimize accelerated applications
- Exposing accelerated application potential for concurrency and exploiting it with CUDA streams
- Leveraging Nsight Systems to guide and check your work

Upon completion, you’ll be able to accelerate and optimize existing C/C++ CPU-only applications using the most essential CUDA techniques and Nsight Systems. You’ll understand an iterative style of CUDA development that will allow you to ship accelerated applications fast.

RAPIDS is a collection of data science libraries that allows end-to-end GPU acceleration for data science workflows. In this training, you'll:

- Use cuDF and Dask to ingest and manipulate massive datasets directly on the GPU
- Apply a wide variety of GPU-accelerated machine learning algorithms, including XGBoost, cuGRAPH, and cuML, to perform data analysis at massive scale
- Perform multiple analysis tasks on massive datasets in an effort to stave off a simulated epidemic outbreak affecting the UK

Upon completion, you'll be able to load, manipulate, and analyze data orders of magnitude faster than before, enabling more iteration cycles and drastically improving productivity.

You’ll learn how to use Transformer-based natural language processing models for text classification tasks, such as categorizing documents. You will also learn how to leverage Transformer-based models for named-entity recognition (NER) tasks and how to analyze various model features, constraints, and characteristics to determine which model is best suited for a particular use case based on metrics, domain specificity, and available resources.

By participating in this workshop, you’ll be able to:

- Understand how text embeddings have rapidly evolved in NLP tasks such as Word2Vec, recurrent neural network (RNN)-based embeddings, and Transformers
- See how Transformer architecture features, especially self-attention, are used to create language models without RNNs
- Use self-supervision to improve the Transformer architecture in BERT, Megatron, and other variants for superior NLP results
- Leverage pre-trained, modern NLP models to solve multiple tasks such as text classification, NER, and question answering
- Manage inference challenges and deploy refined models for live applications

The amount of information moving through our world’s telecommunications infrastructure makes it one of the most complex and dynamic systems that humanity has ever built. In this workshop, you’ll implement multiple AI-based solutions to solve an important telecommunications problem: identifying network intrusions.

In this workshop, you’ll:

- Implement three different anomaly detection techniques: accelerated XGBoost, deep learning-based autoencoders, and generative adversarial networks (GANs)
- Build and compare supervised learning with unsupervised learning-based solutions
- Discuss other use cases within your industry that could benefit from modern computing approaches

Upon completion, you'll be able to detect anomalies within large datasets using supervised and unsupervised machine learning.

The amount of information moving through our world’s telecommunications infrastructure makes it one of the most complex and dynamic systems that humanity has ever built. In this workshop, you’ll implement multiple AI-based solutions to solve an important telecommunications problem: identifying network intrusions.

In this workshop, you’ll:

- Implement three different anomaly detection techniques: accelerated XGBoost, deep learning-based autoencoders, and generative adversarial networks (GANs)
- Build and compare supervised learning with unsupervised learning-based solutions
- Discuss other use cases within your industry that could benefit from modern computing approaches

Upon completion, you'll be able to detect anomalies within large datasets using supervised and unsupervised machine learning.

Modern deep learning challenges leverage increasingly larger datasets and more complex models. As a result, significant computational power is required to train models effectively and efficiently. In this course, you will learn how to scale deep learning training to multiple GPUs. Using multiple GPUs for deep learning can significantly shorten the time required to train lots of data, making solving complex problems with deep learning feasible.

This course will teach you how to use multiple GPUs to train neural networks. You'll learn:

- Approaches to multi-GPU training
- Algorithmic and engineering challenges to large-scale training
- Key techniques used to overcome the challenges mentioned above

Upon completion, you'll be able to effectively parallelize training of deep neural networks using Horovod.

Learn how to design, train, and deploy deep neural networks for autonomous vehicles using the DRIVE development platform.

You'll learn how to:

- Work with CUDA code, memory management, and GPU acceleration on the DRIVE AGX
- Train a semantic segmentation neural network
- Optimize, validate, and deploy a trained neural network using TensorRT

Upon completion, you'll be able to create and optimize perception components for autonomous vehicles using NVIDIA DRIVE.

Writing CUDA C++ applications that efficiently and correctly utilize all available GPUs on a node drastically improves performance over single-GPU code, and makes the most cost-effective use out of compute nodes with multiple GPUs. In this workshop you will learn to utilize multiple GPUs on a single node by:

- Learning how to launch kernels on multiple GPUs, each working on a subsection of the required work
- Learning how to use concurrent CUDA Streams to overlap memory copy with computation on multiple GPUs

Upon completion, you will be able to build robust and efficient CUDA C++ applications that can leverage all available GPUs on a single node.

Discover how deep learning works through hands-on exercises in computer vision and natural language processing. You’ll train deep learning models from scratch, learning tools and tricks to achieve highly accurate results. You’ll also learn to leverage freely available, state-of-the-art pre-trained models to save time and get your deep learning application up and running quickly.

By participating in this is workshop, you’ll:

- Learn the fundamental techniques and tools required to train a deep learning model
- Gain experience with common deep learning data types and model architectures
- Enhance datasets through data augmentation to improve model accuracy
- Leverage transfer learning between models to achieve efficient results with less data and computation
- Build confidence to take on your own project with a modern deep learning framework

The amount of information moving through our world’s telecommunications infrastructure makes it one of the most complex and dynamic systems that humanity has ever built. In this workshop, you’ll implement multiple AI-based solutions to solve an important telecommunications problem: identifying network intrusions.

In this workshop, you’ll:

- Implement three different anomaly detection techniques: accelerated XGBoost, deep learning-based autoencoders, and generative adversarial networks (GANs)
- Build and compare supervised learning with unsupervised learning-based solutions
- Discuss other use cases within your industry that could benefit from modern computing approaches

Upon completion, you'll be able to detect anomalies within large datasets using supervised and unsupervised machine learning.

The CUDA computing platform enables the acceleration of CPU-only applications to run on the world’s fastest massively parallel GPUs. Experience C/C++ application acceleration by:

- Accelerating CPU-only applications to run their latent parallelism on GPUs
- Utilizing essential CUDA memory management techniques to optimize accelerated applications
- Exposing accelerated application potential for concurrency and exploiting it with CUDA streams
- Leveraging Nsight Systems to guide and check your work

Upon completion, you’ll be able to accelerate and optimize existing C/C++ CPU-only applications using the most essential CUDA techniques and Nsight Systems. You’ll understand an iterative style of CUDA development that will allow you to ship accelerated applications fast.

You’ll learn how to use Transformer-based natural language processing models for text classification tasks, such as categorizing documents. You will also learn how to leverage Transformer-based models for named-entity recognition (NER) tasks and how to analyze various model features, constraints, and characteristics to determine which model is best suited for a particular use case based on metrics, domain specificity, and available resources.

By participating in this workshop, you’ll be able to:

- Understand how text embeddings have rapidly evolved in NLP tasks such as Word2Vec, recurrent neural network (RNN)-based embeddings, and Transformers
- See how Transformer architecture features, especially self-attention, are used to create language models without RNNs
- Use self-supervision to improve the Transformer architecture in BERT, Megatron, and other variants for superior NLP results
- Leverage pre-trained, modern NLP models to solve multiple tasks such as text classification, NER, and question answering
- Manage inference challenges and deploy refined models for live applications

Learn how to design, train, and deploy deep neural networks for autonomous vehicles using the DRIVE development platform.

You'll learn how to:

- Work with CUDA code, memory management, and GPU acceleration on the DRIVE AGX
- Train a semantic segmentation neural network
- Optimize, validate, and deploy a trained neural network using TensorRT

Upon completion, you'll be able to create and optimize perception components for autonomous vehicles using NVIDIA DRIVE.