INSTITUTO DE DEEP LEARNING DE NVIDIA
Te capacitamos para que resuelvas los problemas más desafiantes del mundo
El Instituto de Deep Learning de NVIDIA (DLI) ofrece capacitación práctica en inteligencia artificial, procesamiento acelerado y ciencia de datos acelerada. Los desarrolladores, científicos de datos, investigadores y estudiantes pueden obtener experiencia práctica con GPU en cloud y obtener un certificado de competencia para respaldar el crecimiento profesional. Comienza con DLI para acceder a capacitación online a tu propio ritmo para personas, workshops dirigidos por instructores para equipos y materiales de cursos descargables para educadores universitarios.
Para los estudiantes independientes y los equipos pequeños, recomendamos la capacitación online a tu propio ritmo a través de DLI y los cursos en línea a través de nuestros socios. Con DLI, tendrás acceso a un servidor acelerado por GPU totalmente configurado en el cloud, obtendrás habilidades prácticas para su trabajo y tendrás la oportunidad de obtener un certificado de competencia en la materia.
Explore the fundamentals of deep learning by training neural networks and using results to improve performance and capabilities.
Prerequisites: Familiarity with basic programming fundamentals such as functions and variables
Technologies: Caffe, DIGITS
Duration: 8 hours
Price: $90 (excludes tax, if applicable)
Explore how to build a deep learning classification project with computer vision models using an NVIDIA® Jetson™ Nano Developer Kit.
Prerequisites: Familiarity with Python (helpful, not required)
Technologies: PyTorch, Jetson Nano
Duration: 8 hours
Price: Free
Learn how to optimize TensorFlow models to generate fast inference engines in the deployment stage.
Prerequisites: Experience with TensorFlow and Python
Technologies: TensorFlow, Python, NVIDIA TensorRT™ (TF-TRT)
Duration: 2 hours
Price: $30 (excludes tax, if applicable)
Learn how to scale deep learning training to multiple GPUs with Horovod, the open-source distributed training framework originally built by Uber and hosted by the LF AI Foundation.
Prerequisites: Competency in Python and experience training deep learning models in Python
Technologies: Horovod, TensorFlow, Keras, Python
Duration: 2 hours
Price: $30 (excludes tax, if applicable)
Learn how to categorize segments of an image.
Prerequisites: Basic experience training neural networks
Technologies: TensorFlow
Duration: 2 hours
Price: $30 (excludes tax, if applicable)
Explore how to classify and forecast time-series data, such as modeling a patient's health over time, using recurrent neural networks (RNNs).
Prerequisites: Basic experience with deep learning
Technologies: Keras
Duration: 2 hours
Price: $30 (excludes tax, if applicable)
Learn how to transfer the look and feel of one image to another image by extracting distinct visual features using convolutional neural networks (CNNs).
Prerequisites: Experience with CNNs
Technologies: Torch, CNNs
Duration: 2 hours
Price: $30 (excludes tax, if applicable)
Leverage the power of a neural network with autoencoders to create high-quality images from low-quality source images.
Prerequisites: Experience with CNNs
Technologies: Keras, CNNs
Duration: 2 hours
Price: $30 (excludes tax, if applicable)
Explore an introduction to deep learning for radiology and medical imaging by applying CNNs to classify images in a medical imaging dataset.
Prerequisites: Basic experience with Python
Technologies: PyTorch, Python
Duration: 2 hours
Price: $30 (excludes tax, if applicable)
Learn how to apply deep learning techniques to detect the 1p19q co-deletion biomarker from MRI imaging.
Prerequisites: Basic experience with CNNs and Python
Technologies: TensorFlow, CNNs, Python
Duration: 2 hours
Price: $30 (excludes tax, if applicable)
Learn how to use Coarse-to-Fine Context Memory (CFCM) to improve traditional architectures for medical image segmentation and classification tasks.
Prerequisites: Experience with CNNs and long short term memory (LSTMs)
Technologies: TensorFlow, CNNs, CFCM
Duration: 2 hours
Price: $30 (excludes tax, if applicable)
Learn how to use generative adversarial networks (GANs) for medical imaging by applying them to the creation and segmentation of brain MRIs.
Prerequisites: Experience with CNNs
Technologies: TensorFlow, GANs, CNNs
Duration: 2 hours
Price: $30 (excludes tax, if applicable)
Learn how to build hardware-accelerated applications for intelligent video analytics (IVA) with DeepStream and deploy them at scale to transform video streams into insights.
Prerequisites: Experience with C++ and Gstreamer
Technologies: DeepStream3, C++, Gstreamer
Duration: 2 hours
Price: $30 (excludes tax, if applicable)
Learn how to build DeepStream applications to annotate video streams using object detection and classification networks.
Prerequisites: Basic familiarity with C
Technologies: DeepStream, TensorRT, Jetson Nano
Duration: 8 hours; Self-paced
Price: Free
Learn how to accelerate and optimize existing C/C++ CPU-only applications to leverage the power of GPUs using the most essential CUDA techniques and the Nsight Systems profiler.
Prerequisites: Basic C/C++ competency including familiarity with variable types, loops, conditional statements, functions, and array manipulations.
Technologies: C/C++, CUDA
Duration: 8 hours
Price: $90 (excludes tax, if applicable)
Explore how to use Numba—the just-in-time, type-specializing Python function compiler—to create and launch CUDA kernels to accelerate Python programs on GPUs.
Prerequisites: Basic Python competency including familiarity with variable types, loops, conditional statements, functions, and array manipulations. NumPy competency including the use of ndarrays and ufuncs.
Technologies: CUDA, Python, Numba, NumPy
Duration: 8 hours
Price: $90 (excludes tax, if applicable)
Aprenda a crear aplicaciones CUDA C++ sólidas y eficientes que pueden aprovechar todas las GPU disponibles en un solo nodo.
REQUISITOS PREVIOS: Competencia en redacción de aplicaciones en CUDA C / C ++.
HERRAMIENTAS, BIBLIOTECAS, FRAMEWORKS: C, C++
DURACION: 4 horas
IDIOMA: Inglés
PRECIO: $30 (no incluye impuestos, si corresponde)
Aprenda a mejorar el rendimiento de sus aplicaciones CUDA C/C++ superponiendo transferencias de memoria desde y hacia la GPU con cálculos en la GPU.
REQUISITOS PREVIOS: Competencia en redacción de aplicaciones en CUDA C/C++..
HERRAMIENTAS, BIBLIOTECAS, FRAMEWORKS: C, C++
DURACION: 4 horas
IDIOMA: Inglés
PRECIO: $30 (no incluye impuestos, si corresponde)
Explore how to build and optimize accelerated heterogeneous applications on multiple GPU clusters using OpenACC, a high-level GPU programming language.
Prerequisites: Basic experience with C/C++
Technologies: OpenACC, C/C++
Duration: 8 hours
Languages: English
Price: $90 (excludes tax, if applicable)
Learn how to reduce complexity and improve portability and efficiency of your code by using a containerized environment for high-performance computing (HPC) application development.
Prerequisites: Proficiency programming in C/C++ and professional experience working on HPC applications
Technologies: Docker, Singularity, HPCCM, C/C++
Duration: 2 hours
Price: $30 (excludes tax, if applicable)
Learn how to accelerate C/C++ or Fortran applications using OpenACC to harness the power of GPUs.
Prerequisites: Basic experience with C/C++
Technologies: C/C++, OpenACC
Duration: 2 hours
Price: $30 (excludes tax, if applicable)
Learn how to perform multiple analysis tasks on large datasets using RAPIDS, a collection of data science libraries that allows end-to-end GPU acceleration for data science workflows.
Prerequisites: Experience with Python, including pandas and NumPy
Technologies: RAPIDS, NumPy, XGBoost, DBSCAN, K-Means, SSSP, Python
Duration: 6 hours
Price: $90 (excludes tax, if applicable)
Learn to build a GPU-accelerated, end-to-end data science workflow using RAPIDS open-source libraries for massive performance gains.
Prerequisites: Advanced competency in Pandas, NumPy, and scikit-learn
Technologies: RAPIDS, cuDF, cuML, XGBoost
Duration: 2 hours
Price: $30 (excludes tax, if applicable)
Explore an introduction to AI, GPU computing, NVIDIA AI software architecture, and how to implement and scale AI workloads in the data center. You'll understand how AI is transforming society and how to deploy GPU computing to the data center to facilitate this transformation.
Prerequisites: Basic knowledge of enterprise networking, storage, and data center operations
Technologies: Artificial intelligence, machine learning, deep learning, GPU hardware and software
Duration: 4 hours
Price: $30 (excludes tax, if applicable)
DLI colabora con organizaciones educativas líderes para expandir el alcance de la capacitación de deep learning a los desarrolladores de todo el mundo.
Para los equipos interesados en la capacitación, recomendamos workshops de día completo dirigidos por instructores certificados por DLI. Puede solicitar un workshop de día completo en el sitio o de forma remota para su equipo. Con DLI, tendrá acceso a un servidor en el cloud totalmente configurado y acelerado por GPU, obtendrá habilidades prácticas para su trabajo y tendrá la oportunidad de obtener un certificado de competencia en la materia.
Eche un vistazo a la experiencia DLI en este breve video.
Businesses worldwide are using artificial intelligence (AI) to solve their greatest challenges. Healthcare professionals use AI to enable more accurate, faster diagnoses in patients. Retail businesses use it to offer personalized customer shopping experiences. Automakers use AI to make personal vehicles, shared mobility, and delivery services safer and more efficient. Deep learning is a powerful approach to implementing AI that uses multi-layered artificial neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, and language translation. Using deep learning, computers are now able to learn and recognize patterns from data that are considered too complex or subtle for expert-written software.
In this workshop, you’ll learn how deep learning works through hands-on exercises in computer vision and natural language processing. You will 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 today.
By participating in this is workshop you will:
Prerequisites: Understanding of fundamental programming concepts in Python such as functions, loops,dictionaries, and arrays.
Tools, libraries, and frameworks: Tensorflow, Keras, Pandas, Numpy
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.
Recommender systems work by understanding the preferences, previous decisions, and other characteristics of many people. For example, recommenders can help a streaming media service understand the types of movies an individual enjoys, which movies they’ve actually watched, and the languages they understand. Training a neural network to generalize this mountain of data and quickly provide specific recommendations for similar individuals or situations requires massive amounts of computation, which can be accelerated dramatically by GPUs. Organizations seeking to provide more delightful user experiences, deeper engagement with their customers, and better informed decisions can realize tremendous value by applying properly designed and trained recommender systems.
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 workshop, you’ll learn how to:
Prerequisites:
Tools, libraries, and frameworks: CuDF, CuPy, TensorFlow 2, and NVIDIA Triton™ Inference Server
Applications for Natural Language Processing (NLP) have exploded in the past decade. With the proliferation of AI assistants, and organizations infusing their businesses with more interactive human/machine experiences, understanding how NLP techniques can be used to manipulate, analyze, and generate text-based data is essential. Modern techniques can be used to capture the nuance, context, and sophistication of language, just as humans do. And when designed correctly, developers can use these techniques to build powerful NLP applications that provide natural and seamless human-computer interactions within Chat Bots, AI Voice Agents, and many more.
Deep learning models have gained widespread popularity for NLP because of their ability to accurately generalize over a range of contexts and languages. Transformer-based models, such as Bidirectional Encoder Representations from Transformers (BERT), have revolutionized progress in NLP by offering accuracy comparable to human baselines on benchmarks like SQuAD for question-answer, entity recognition, intent recognition, sentiment analysis, and more. NVIDIA provides software and hardware that helps you quickly build state-of-the-art NLP models. You can speed-up the training process up to 4.5x with mixed-precision, and easily scale performance to multi-GPU across multiple server nodes without compromising accuracy.
In this workshop, 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:
Prerequisites:
Herramientas, bibliotecas y frameworks: PyTorch, pandas, NVIDIA NeMo ™, Servidor de Inferencia NVIDIA Triton™
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:
Upon completion, you'll be able to effectively parallelize training of deep neural networks using Horovod.
Prerequisites: Competency in the Python programming language and experience training deep learning models in Python
Technologies: Python, Tensorflow
Learn how to design, train, and deploy deep neural networks for autonomous vehicles using the NVIDIA DRIVE™ development platform.
You'll learn how to:
Upon completion, you'll be able to create and optimize perception components for autonomous vehicles using NVIDIA DRIVE.
Prerequisites: Experience with CNNs and C++
Technologies: TensorFlow, TensorRT, Python, CUDA C++, DIGITS
AI is revolutionizing the acceleration and development of robotics across a broad range of industries. Explore how to create robotics solutions on a Jetson for embedded applications.
You’ll learn how to:
Upon completion, you’ll know how to deploy high-performance deep learning applications for robotics.
Prerequisites: Basic familiarity with deep neural networks, basic coding experience in Python or similar language
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:
Upon completion, you'll be able to detect anomalies within large datasets using supervised and unsupervised machine learning.
Prerequisites: Experience with CNNs and Python
Technologies: RAPIDS, Keras, GANs, XGBoost
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:
Upon completion, you’ll understand how to use AI to predict the condition of equipment and estimate when maintenance should be performed.
Prerequisites: Experience with Python and deep neural networks
Technologies: TensorFlow, Keras
Explore cómo crear un modelo de deep learning para automatizar la verificación de condensadores en la placa de circuito impreso (PCB) de NVIDIA utilizando un conjunto de datos de producción real. Esto puede reducir el costo de verificación y aumentar el rendimiento de la producción en una variedad de casos de uso de fabricación. Aprenderá a:
Al finalizar, podrá diseñar, entrenar, probar e implementar los componentes básicos de una tubería de inspección industrial acelerada por hardware.
Requisitos previos : experiencia con Python y redes neuronales convolucionales (CNN)
Tecnologías: TensorFlow, NVIDIA TensorRT™, Keras
With the increase in traffic cameras, growing prospect of autonomous vehicles, and promising outlook of smart cities, there's a rise in demand for faster and more efficient object detection and tracking models. This involves identification, tracking, segmentation and prediction of different types of objects within video frames.
In this workshop, you’ll learn how to:
Upon completion, you'll be able to design, train, test and deploy building blocks of a hardware-accelerated traffic management system based on parking lot camera feeds.
Prerequisites: Experience with deep networks (specifically variations of CNNs), intermediate-level experience with C++ and Python
Technologies: deep learning, intelligent video analytics, deepstream 3.0, tensorflow, iva, fmv, opencv, accelerated video decoding/encoding, object detection and tracking, anomaly detection, deployment, optimization, data preparation
This workshop explores how to apply convolutional neural networks (CNNs) to MRI scans to perform a variety of medical tasks and calculations. You’ll learn how to:
Upon completion, you’ll be able to apply CNNs to MRI scans to conduct a variety of medical tasks.
Prerequisites: Basic familiarity with deep neural networks; basic coding experience in Python or a similar language
Technologies: R, MXNet, TensorFlow, Caffe, DIGITS
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:
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.
Prerequisites: Basic C/C++ competency including familiarity with variable types, loops, conditional statements, functions, and array manipulations.
Technologies: C/C++, CUDA
This workshop 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:
Prerequisites: Basic Python competency including familiarity with variable types, loops, conditional statements, functions, and array manipulations. NumPy competency including the use of ndarrays and ufuncs.
Technologies: CUDA, Python, Numba, NumPy
Este workshop cubre cómo escribir aplicaciones CUDA C ++ que utilizan de manera eficiente y correcta todas las GPU disponibles en un solo nodo, mejorando significativamente el rendimiento de sus aplicaciones y haciendo el uso más rentable de los sistemas con múltiples GPU.
Al participar en este workshop, aprenderá a:
Prerrequisitos:
Tecnologías: CUDA C++, nvcc, Sistemas Nsight
RAPIDS is a collection of data science libraries that allows end-to-end GPU acceleration for data science workflows. In this training, you'll:
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.
Prerequisites: Experience with Python, ideally including pandas and NumPy
Technologies: RAPIDS, NumPy, XGBoost, DBSCAN, K-Means, SSSP, Python
Si te interesa recibir una capacitación empresarial más integral, la solución para empresas de DLI ofrece un paquete de capacitación y conferencias para satisfacer las necesidades únicas de tu organización. DLI ofrece capacitaciones práctica online y presenciales, sesiones informativas para ejecutivos e informes empresariales, para ayudar a tu empresa a transformarse en una organización de inteligencia artificial. Contáctanos para saber más.
If you would like to receive updates on upcoming DLI public workshops, sign up to receive communications.
NVIDIA DLI ofrece materiales de cursos descargables para educadores universitarios y capacitación en línea gratuita a su propio ritmo para estudiantes a través de los kits de enseñanza de DLI. Los educadores también pueden obtener la certificación para impartir talleres de DLI en el campus a través del Programa de Embajadores Universitarios.
Los kits de enseñanza DLI están disponibles para educadores universitarios calificados a los que les interesan las soluciones de cursos de deep learning, procesamiento acelerado y robótica. Los educadores pueden integrar materiales de conferencias, cursos prácticos, recursos en cloud de GPU y más en su plan de estudios.
El Programa de Embajadores Universitarios de DLI certifica a educadores calificados para impartir workshops prácticos de DLI a profesores, estudiantes e investigadores universitarios sin costo alguno. Se invita a los educadores a descargar los kits de enseñanza de DLI para poder cumplir con los requisitos para participar en el Programa de Embajadores.
DLI tiene embajadores universitarios certificados en cientos de universidades, incluidas las siguientes:
DLI trabaja con socios de la industria para crear su contenido y ofrecer talleres dirigidos por nuestros instructores en todo el mundo. Estos son algunos de nuestros principales socios.
Explora una amplia gama de recursos técnicos sobre inteligencia artificial y procesamiento acelerado.