컨퍼런스 워크숍

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.

워크숍 레벨: 초급 기술

전제 조건: 함수, 루프, 사전 및 어레이 등 Python의 기본 프로그래밍 개념 이해

세션 설명: 이 워크숍에서는 딥 러닝이 컴퓨터 비전 및 자연어 처리 분야의 실습 예제를 어떻게 해결하는지 설명합니다. 고도로 정확한 결과를 얻기 위해 처음부터 도구와 요령을 익히며 딥 러닝 모델을 트레이닝해보세요. 또한 무료로 이용 가능한 사전 훈련된 첨단 모델을 활용하여 시간을 절약하고 딥 러닝 애플리케이션을 빠르게 시작해 실행하는 방법을 설명합니다.

본 워크숍에 참여함으로써 얻을 수 있는 이점은 다음과 같습니다:
> 딥 러닝 모델 트레이닝에 필요한 기본 기법과 도구 학습
> 일반적인 딥 러닝 데이터 유형 및 모델 아키텍처에 대한 경험 쌓기
> 모델 정확도 향상을 위해 데이터 확장를 통해 데이터세트 강화
> 모델 간 전이 학습을 활용하여 더 적은 데이터와 컴퓨팅 성능으로 효율적인 결과 달성
> 첨단 딥 러닝 프레임워크로 자체 프로젝트를 수행할 수 있는 자신감 획득

워크숍 레벨: 중급 기술

전제 조건:
> Python 코딩 및 라이브러리 함수 및 매개변수 사용 경험
> TensorFlow, PyTorch 또는 Keras 등의 딥 러닝 프레임워크에 대한 기본적 이해
> 뉴럴 네트워크에 대한 기본적 이해

세션 설명: 문서 분류 등의 텍스트 분류 작업에 트랜스포머 기반 자연어 처리 모델을 사용하는 방법을 배우게 됩니다. 또한 명명된 NER(엔터티 인식) 작업을 위해 트랜스포터 기반 모델을 활용하는 방법과 지표, 도메인 특수성, 사용 가능한 리소스를 바탕으로 특정 사용 사례에 가장 적합한 모델이 어느 것인지 결정하기 위해 다양한 모델 기능, 구속 조건 및 특성을 분석하는 방법도 배웁니다.

이 워크숍에 참여함으로써 다음의 내용을 배우게 됩니다:
> Word2Vec, RNN(순환 신경망) 기반 임베딩, 트랜스포머와 같은 NLP 작업에서 텍스트 임베딩의 빠른 발전 양상 이해
> 트랜스포머 아키텍처 기능, 특히 셀프 어텐션 기능을 사용하여 RNN 없이 언어 모델을 생성하는 방법 확인
> 셀프 수퍼비전 기능을 사용하여 BERT, Megatron, 기타 변형 모델의 트랜스포머 아키텍처 개선 및 뛰어난 NLP 결과 달성
> 사전 학습된 첨단 NLP 모델을 활용하여 텍스트 분류, NER, 질문-답변 등의 여러 작업 해결
> 추론 과제를 관리하고 실시간 애플리케이션을 위해 정교한 모델 구축

워크숍 레벨: 중급 기술

전제 조건: Python 프로그래밍 언어 및 Python에서 딥러닝 모델 구축 경험

세션 설명: 현대의 딥 러닝은 점점 더 큰 데이터 세트와 더 복잡한 모델 활용을 요구합니다. 그 결과, 모델을 효과적이고 효율적으로 훈련시키기 위해서는 상당한 연산 능력이 필요합니다. 이번 과정에서는 딥 러닝 트레이닝을 멀티 GPU로 확장하는 방법에 대해 알아봅니다. 딥 러닝에 멀티 GPU를 사용하면 많은 데이터를 훈련하는 데 필요한 시간을 크게 단축할 수 있어 딥 러닝으로 복잡한 문제를 해결할 수 있습니다.

이 과정에서는 멀티 GPU를 사용하여 신경망을 훈련하는 방법에 대해 설명합니다:
> 다중 GPU 트레이닝에 대한 접근법
> 라지 스케일 트레이닝에 대한 알고리즘 및 엔지니어링 과제
> 위에서 언급된 과제들을 극복하기 위해 사용되는 주요 기법

이 과정을 마치면 Horovod를 활용하여 심층 신경망의 트레이닝을 효과적으로 멀티 GPU에 적용할 수 있습니다.