カンファレンスのワークショップとトレーニング

Audience Level: Beginner Technical

Prerequisite(s): An understanding of fundamental programming concepts in Python such as functions, loops, dictionaries, and arrays.

Session Abstract: 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

Audience Level: Advanced Technical

Prerequisite(s):
> Experience with Python coding and use of library functions and parameters
> Fundamental understanding of a deep learning framework such as TensorFlow, PyTorch, or Keras
> Basic understanding of neural networksDiscover 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.

Session Abstract: 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

Audience Level: Beginner Technical

Prerequisite(s):
Experience with Python, ideally including pandas and NumPy

Session Abstract: 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.

Audience Level: Beginner Technical

Prerequisite(s): Experience with Python and deep networks

Session Abstract: Learn how to identify anomalies and failures in time-series data, estimate the remaining useful life of the corresponding parts, and use this information to map anomalies to failure conditions.

You'll learn how to:

> Leverage predictive maintenance to manage failures and avoid costly unplanned downtimes
> Identify key challenges around identifying anomalies that can lead to costly breakdowns
> Use time-series data to predict outcomes using machine learning classification models with XGBoost
> Apply predictive maintenance procedures by using a long short-term memory ( LSTM)-based model to predict device failure
> Experiment with autoencoders to detect anomalies by using the time-series sequences from the previous steps

Upon completion, you’ll understand how to use AI to predict the condition of equipment and estimate when maintenance should be performed.

Audience Level: Beginner Technical

Prerequisite(s): Basic Python competency including familiarity with variable types, loops, conditional statements, functions, and array manipulations. NumPy competency including the use of ndarrays and ufuncs. No previous knowledge of CUDA programming is assumed.

Session Abstract: This course explores how to use Numba—the just-in-time, type-specializing Python function compiler—to accelerate Python programs to run on massively parallel NVIDIA GPUs. You’ll learn how to:

> Use Numba to compile CUDA kernels from NumPy universal functions (ufuncs)
> Use Numba to create and launch custom CUDA kernels
> Apply key GPU memory management techniques

Upon completion, you’ll be able to use Numba to compile and launch CUDA kernels to accelerate your Python applications on NVIDIA GPUs.

Audience Level: Advanced Technical

Prerequisite(s): Python programming experience, basic understanding of neural networks, and a fundamental understanding of a deep learning framework such as TensorFlow or PyTorch

Session Abstract: Learn how to quickly build and deploy production quality conversational AI applications with real-time transcription and natural language processing (NLP) capabilities.

Conversational AI pipelines are complex and expensive to develop from scratch. In this course, you'll learn to build a conversational AI service using the NVIDIA Riva framework. Riva provides a complete, GPU-accelerated software stack, making it easy for developers to quickly create, deploy, and run end-to-end, real-time conversational AI applications that can understand terminology unique to each company and its customers. The Riva framework includes pretrained conversational AI models, tools, and optimized services for speech, vision, and natural language understanding (NLU) tasks. With Riva, developers can create customized language-based AI services for intelligent virtual assistants, virtual customer service agents, real-time transcription, multi-user diarization,chatbots and much more.

In this course, you will integrate NVIDIA Riva automatic speech recognition (ASR) and named entity recognition (NER) models with a web-based application to produce transcriptions of audio inputs with highlighted relevant text. You'll then customize the NER model using NVIDIA TAO Toolkit to provide different targeted highlights for the application. Finally, you'll explore production-level deployment performance and scaling considerations of Riva services with Helm Charts and Kubernetes clusters.

You'll learn how to:
> Deploy and enable pretrained ASR and NER models on Riva for a Conversational AI Application
> Explore the Riva ASR service with audio examples
> Fine-tune and deploy a domain-specific NER model with TAO Toolkit
> Deploy a production-level conversational AI application with a Helm Chart for scaling in Kubernetes clusters

Upon completion, you’ll be able to build Conversational AI applications and deploy them as services.

Audience Level: Beginner Technical

Prerequisite(s): An understanding of fundamental programming concepts in Python such as functions, loops, dictionaries, and arrays

Session Abstract: 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

Audience Level: Advanced Technical

Prerequisite(s):
>Experience with Python coding and use of library functions and parameters
> Fundamental understanding of a deep learning framework such as TensorFlow, PyTorch, or Keras
> Basic understanding of neural networks

Session Abstract: 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

Audience Level: Beginner Technical

Prerequisite(s): Basic C/C++ competency including familiarity with variable types, loops, conditional statements, functions, and array manipulations.

Session Abstract: 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.

Audience Level: Beginner Technical

Prerequisite(s):
>Intermediate knowledge of Python, including
understanding of list comprehension
>Data science experience using Python
>Familiarity with NumPy and matrix mathematics

Session Abstract: Deep learning-based recommender systems are the secret ingredient behind personalized online experiences and powerful decision support tools in retail, entertainment, healthcare, finance, and other industries. This workshop covers the fundamental tools and techniques for building highly effective recommender systems, as well as how to deploy GPU-accelerated solutions for real-time recommendations.

By participating in this is workshop, you’ll learn how to:
> Build a content-based recommender system using the open-source cuDF library and Apache Arrow
> Construct a collaborative filtering recommender system using alternating least squares (ALS) and CuPy
> Design a wide and deep neural network using TensorFlow 2 to create a hybrid recommender system
> Optimize performance for both training and inference using large, sparse datasets
> Deploy a recommender model as a high-performance web service

Audience Level: Beginner Technical

Prerequisite(s):
>Intermediate knowledge of Python, including
understanding of list comprehension
>Data science experience using Python
>Familiarity with NumPy and matrix mathematics

Session Abstract: Deep learning-based recommender systems are the secret ingredient behind personalized online experiences and powerful decision support tools in retail, entertainment, healthcare, finance, and other industries. This workshop covers the fundamental tools and techniques for building highly effective recommender systems, as well as how to deploy GPU-accelerated solutions for real-time recommendations.

By participating in this is workshop, you’ll learn how to:
> Build a content-based recommender system using the open-source cuDF library and Apache Arrow
> Construct a collaborative filtering recommender system using alternating least squares (ALS) and CuPy
> Design a wide and deep neural network using TensorFlow 2 to create a hybrid recommender system
> Optimize performance for both training and inference using large, sparse datasets
> Deploy a recommender model as a high-performance web service

Audience Level: Beginner Technical

Prerequisite(s): Experience with CNNs and Python

Session Abstract: 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.

Audience Level: Beginner Technical

Prerequisite(s): Experience with Python, ideally including pandas and NumPy

Session Abstract: 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.

Audience Level: Beginner Technical

Prerequisite(s): Experience with Python and deep networks

Session Abstract: Learn how to identify anomalies and failures in time-series data, estimate the remaining useful life of the corresponding parts, and use this information to map anomalies to failure conditions.

You'll learn how to:
> Leverage predictive maintenance to manage failures and avoid costly unplanned downtimes
> Identify key challenges around identifying anomalies that can lead to costly breakdowns
> Use time-series data to predict outcomes using machine learning classification models with XGBoost
> Apply predictive maintenance procedures by using a long short-term memory ( LSTM)-based model to predict device failure
> Experiment with autoencoders to detect anomalies by using the time-series sequences from the previous steps

Upon completion, you’ll understand how to use AI to predict the condition of equipment and estimate when maintenance should be performed.

Audience Level: Beginner Technical

Prerequisite(s): Experience with Python, ideally including pandas and NumPy

Session Abstract: 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.

Audience Level: Beginner Technical

Prerequisite(s): Experience with Python, ideally including pandas and NumPy

Session Abstract: 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.

Audience Level: Beginner Technical

Prerequisite(s): An understanding of fundamental programming concepts in
Python such as functions, loops, dictionaries, and arrays.

Session Abstract: 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

Audience Level: Advanced Technical

Prerequisite(s):
> Experience with Python coding and use of library functions and parameters
> Fundamental understanding of a deep learning framework such as TensorFlow, PyTorch, or Keras
> Basic understanding of neural networks

Session Abstract: 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

Audience Level: Advanced Technical

Prerequisite(s): Python programming experience, basic understanding of neural networks, and a fundamental understanding of a deep learning framework such as TensorFlow or PyTorch

Session Abstract: Learn how to quickly build and deploy production quality conversational AI applications with real-time transcription and natural language processing (NLP) capabilities.

Conversational AI pipelines are complex and expensive to develop from scratch. In this course, you'll learn to build a conversational AI service using the NVIDIA Riva framework. Riva provides a complete, GPU-accelerated software stack, making it easy for developers to quickly create, deploy, and run end-to-end, real-time conversational AI applications that can understand terminology unique to each company and its customers. The Riva framework includes pretrained conversational AI models, tools, and optimized services for speech, vision, and natural language understanding (NLU) tasks. With Riva, developers can create customized language-based AI services for intelligent virtual assistants, virtual customer service agents, real-time transcription, multi-user diarization,chatbots and much more.

In this course, you will integrate NVIDIA Riva automatic speech recognition (ASR) and named entity recognition (NER) models with a web-based application to produce transcriptions of audio inputs with highlighted relevant text. You'll then customize the NER model using NVIDIA TAO Toolkit to provide different targeted highlights for the application. Finally, you'll explore production-level deployment performance and scaling considerations of Riva services with Helm Charts and Kubernetes clusters.

You'll learn how to:
> Deploy and enable pretrained ASR and NER models on Riva for a Conversational AI Application
> Explore the Riva ASR service with audio examples
> Fine-tune and deploy a domain-specific NER model with TAO Toolkit
> Deploy a production-level conversational AI application with a Helm Chart for scaling in Kubernetes clusters

Upon completion, you’ll be able to build Conversational AI applications and deploy them as services.

Audience Level: Beginner Technical

Prerequisite(s): An understanding of fundamental programming concepts in
Python such as functions, loops, dictionaries, and arrays.

Session Abstract: 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

Audience Level: Advanced Technical

Prerequisite(s):
> Experience with Python coding and use of library functions and parameters
> Fundamental understanding of a deep learning framework such as TensorFlow, PyTorch, or
Keras
> Basic understanding of neural networks

Session Abstract: 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

Audience Level: Advanced Technical

Prerequisite(s): Competency in the Python programming language and experience training deep learning models in Python

Session Abstract: 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.

対象者レベル: 初心者向けテクニカル

参加条件: 

• Python の経験
• データ処理とディープラーニングの基礎を理解していること

ワークショップの概要: このワークショップでは、時系列データの中から異常と障害を特定する方法、該当パーツの残りの耐用年数を見積もる方法、異常を障害条件に関連付ける方法について学習します。AI モデル トレーニングのために時系列データを準備する方法、XGBoost アンサンブル ツリー モデルを開発する方法、長/短期記憶 (LSTM) ネットワークを使用してディープラーニング モデルを開発する方法、予知保全のために異常を検出するオートエンコーダーを作成する方法について学習します。修了後、AI を利用して機器の状態を予測し、保守を実施する時期を予測できるようになります。

対象者レベル: 初心者向けテクニカル

参加条件: 

• 変数の種類、ループ、条件付き命令、関数、配列操作を理解しているなど、C/C++ の基礎能力
• CUDA プログラミングの知識は問われません

ワークショップの概要: このワークショップでは、超並列 GPU と CUDA® で実行する C/C++ アプリケーションを高速化するための基礎的なツールと手法について学習します。コードの記述方法、CUDA によるコード並列化の構成方法、CPU と GPU のアクセラレーター間のメモリ移行最適化方法、新しいタスク (完全に機能するが CPU のみの微粒子シミュレーターを高速化し、大幅なパフォーマンス アップが観察できる) で理解されたワークフローの実装方法について学習します。ワークショップの終了時には、GPU 対応のアプリケーションを自分で新しく作成するための追加リソースを利用できます。