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.

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    WORKSHOPS A CARGO
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    CAPACITACIÓN
    PARA UNIVERSIDADES

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.

CAPACITACIÓN ONLINE CON DLI

Certificate Available

CURSOS DE DEEP LEARNING

ASPECTOS BÁSICOS DE DEEP LEARNING

  • Aspectos Básicos de Deep Learning para la Visión Artificial 

    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)

  • Comenzar a Usar la IA en Jetson Nano

    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

  • Optimización e Implementación de Modelos TensorFlow con TensorRT

    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)

  • Deep learning a Escala con Horovod

    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)

  • Comenzar a Usar la Segmentación de Imágenes

    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)

  • Modelado de Datos de Series Temporales con Redes Neuronales Recurrentes en Keras

    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)

DEEP LEARNING PARA LA CREACIÓN DE CONTENIDO DIGITAL

  • Transferencia de Estilos de Imagen con Torch

    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)

  • Superresolución de Imagen Usando Autocodificadores

    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)

DEEP LEARNING PARA LA ATENCIÓN DE LA SALUD

  • Clasificación de Imágenes Médicas con el Conjunto de Datos MedNIST

    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)

  • Clasificación de Imágenes con TensorFlow: Radiómica - Clasificación del Estado del Cromosoma 1p19q

    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)

  • Memoria Contextual Amplia a Detallada para Imágenes Médicas

    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)

  • Aumento de Datos y Segmentación con Redes Generativas para Crear Imágenes Médicas

    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)

DEEP LEARNING PARA EL ANÁLISIS DE VIDEOS INTELIGENTE

  • Workflows de IA para el Análisis de Videos Inteligente con DeepStream

    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)

  • Comenzar a Usar DeepStream para el Análisis de Videos en Jetson Nano

    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

CURSOS DE PROCESAMIENTO ACELERADO

  • Aspectos Básicos del Procesamiento Acelerado con CUDA C/C++ 

    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)

  • Aspectos Básicos del Procesamiento Acelerado con CUDA Python

    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)

  • Aspectos Básicos del Procesamiento Acelerado con OpenACC

    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)

  • Procesamiento de alto rendimiento con contenedores

    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)

  • OpenACC: Duplicar el Rendimiento en 4 Pasos

    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)

CURSOS DE LA CIENCIA DE DATOS ACELERADA

  • Aspectos Básicos de la Ciencia de Datos Acelerada con RAPIDS

    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)

  • Acelerar los Workflows de Ciencia de Datos con RAPIDS

    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)

CURSOS DE IA PARA TI

  • Introducción a la IA en el Data Center

    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)

CAPACITACIÓN ONLINE CON SOCIOS

DLI colabora con organizaciones educativas líderes para expandir el alcance de la capacitación de deep learning a los desarrolladores de todo el mundo.

UPCOMING INSTRUCTOR-LED WORKSHOPS

DLI offers public instructor-led workshops around the world at conferences and universities. View the schedule below to find a workshop near you.

Para equipos grandes o estudiantes independientes que están interesados en la capacitación, recomendamos los workshops de día completo dirigidos por instructores certificados por DLI. Puedes solicitar un workshop de día completo en el sitio o de entrega remota para tu equipo. 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.

Certificate Available

WORKSHOPS DE DEEP LEARNING

ASPECTOS BÁSICOS DE DEEP LEARNING

  • Aspectos Básicos de Deep Learning para Múltiples GPUs 

    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.

    Prerequisites: Competency in the Python programming language and experience training deep learning models in Python

    Technologies: Python, Tensorflow

  • Aspectos Básicos de Deep Learning para la Visión Artificial 

    Explore the fundamentals of deep learning by training neural networks and using results to improve performance and capabilities.

    In this workshop, you’ll learn the basics of deep learning by training and deploying neural networks. You’ll learn how to:

    • Implement common deep learning workflows, such as image classification and object detection
    • Experiment with data, training parameters, network structure, and other strategies to increase performance and capability
    • Deploy your neural networks to start solving real-world problems

    Upon completion, you’ll be able to start solving problems on your own with deep learning.

    Prerequisites: Familiarity with basic programming fundamentals such as functions and variables

    Technologies: Caffe, DIGITS

  • Aspectos Básicos de Deep Learning para Diversos Tipos de Datos 

    This workshop explores how convolutional and recurrent neural networks can be combined to generate effective descriptions of content within images and video clips.

    Learn how to train a network using TensorFlow and the Microsoft Common Objects in Context (COCO) dataset to generate captions from images and video by:

    • Implementing deep learning workflows like image segmentation and text generation
    • Comparing and contrasting data types, workflows, and frameworks
    • Combining computer vision and natural language processing

    Upon completion, you’ll be able to solve deep learning problems that require multiple types of data inputs.

    Prerequisites: Familiarity with basic Python (functions and variables); prior experience training neural networks.

    Technologies: TensorFlow

  • Fundamentals of Deep Learning for Multi-GPUs 

    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.

    Prerequisites: Competency in the Python programming language and experience training deep learning models in Python

    Technologies: Python, Tensorflow

  • Fundamentals of Deep Learning for Multiple Data Types 

    This workshop explores how convolutional and recurrent neural networks can be combined to generate effective descriptions of content within images and video clips.

    Learn how to train a network using TensorFlow and the Microsoft Common Objects in Context (COCO) dataset to generate captions from images and video by:

    • Implementing deep learning workflows like image segmentation and text generation
    • Comparing and contrasting data types, workflows, and frameworks
    • Combining computer vision and natural language processing

    Upon completion, you’ll be able to solve deep learning problems that require multiple types of data inputs.

    Prerequisites: Familiarity with basic Python (functions and variables); prior experience training neural networks.

    Technologies: TensorFlow

  • Aspectos Básicos de Deep Learning para el Procesamiento de Lenguajes Naturales 

    Learn the latest deep learning techniques to understand textual input using natural language processing (NLP). You’ll learn how to:

    • Convert text to machine-understandable representations and classical approaches
    • Implement distributed representations (embeddings) and understand their properties
    • Train machine translators from one language to another

    Upon completion, you’ll be proficient in NLP using embeddings in similar applications.

    Prerequisites: Basic experience with neural networks and Python programming; familiarity with linguistics

    Technologies: TensorFlow, Keras

DEEP LEARNING POR INDUSTRIA

  • Deep Learning para Vehículos Autónomos: Perception

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

    You'll learn how to:

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

    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

  • Deep Learning para Robótica

    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:

    • Apply computer vision models to perform detection
    • Prune and optimize the model for embedded application
    • Train a robot to actuate the correct output based on the visual input

    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

  • Aplicaciones de IA para la Detección de Anomalías

    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. 

    Prerequisites: Experience with CNNs and Python

    Technologies: RAPIDS, Keras, GANs, XGBoost

  • Aplicaciones de IA para el Mantenimiento Predictivo

    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.

    Prerequisites: Experience with Python and deep neural networks

    Technologies: TensorFlow, Keras

  • Deep Learning para la Inspección Industrial

    This workshop explores how convolutional and recurrent neural networks can be combined to generate effective descriptions of content within images and video clips.

    Learn how to train a network using TensorFlow and the Microsoft Common Objects in Context (COCO) dataset to generate captions from images and video by:

    • Implementing deep learning workflows like image segmentation and text generation
    • Comparing and contrasting data types, workflows, and frameworks
    • Combining computer vision and natural language processing

    Upon completion, you’ll be able to solve deep learning problems that require multiple types of data inputs.

    Prerequisites: Familiarity with basic Python (functions and variables); prior experience training neural networks.

    Technologies: TensorFlow

  • Deep Learning para el Análisis de Videos Inteligente

    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:

    • Efficiently process and prepare video feeds using hardware accelerated decoding methods
    • Train and evaluate deep learning models and leverage ""transfer learning"" techniques to elevate efficiency and accuracy of these models and mitigate data sparsity issues
    • Explore the strategies and trade-offs involved in developing high-quality neural network models to track moving objects in large-scale video datasets
    • Optimize and deploy video analytics inference engines by acquiring the DeepStream SDK

    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

  • Deep Learning para el Análisis de Imágenes de Atención de la Salud

    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:

    • Perform image segmentation on MRI images to determine the location of the left ventricle
    • Calculate ejection fractions by measuring differences between diastole and systole using CNNs applied to MRI scans to detect heart disease
    • Apply CNNs to MRI scans of low-grade gliomas (LGGs) to determine 1p/19q chromosome co-deletion status

    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

WORKSHOPS DE PROCESAMIENTO ACELERADO

  • Aspectos Básicos del Procesamiento Acelerado con CUDA C/C++ 

    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.

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

    Technologies: C/C++, CUDA

  • Aspectos Básicos del Procesamiento Acelerado con CUDA Python

    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:

    • 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.

    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

WORKSHOPS DE LA CIENCIA DE DATOS ACELERADA

  • Aspectos Básicos de la Ciencia de Datos Acelerada con RAPIDS

    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.

    Prerequisites: Experience with Python, ideally including pandas and NumPy

    Technologies: RAPIDS, NumPy, XGBoost, DBSCAN, K-Means, SSSP, Python

NETWORKING WORKSHOPS

SOLUCIÓN PARA EMPRESAS

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.

PUBLIC WORKSHOPS

If you would like to receive updates on upcoming DLI public workshops, sign up to receive communications.

CUMULUS BOOTCAMPS

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.

KITS DE ENSEÑANZA

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.

 

Mejora los Planes de Estudio con los Kits de Enseñanza de NVIDIA

PROGRAMA DE EMBAJADORES UNIVERSITARIOS

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.

 

Ampliar las Fronteras de la Educación

DLI tiene embajadores universitarios certificados en cientos de universidades, incluidas las siguientes:

Arizona State University
Columbia
The Hong Kong University Of Science And Technology
Massachusetts Institute of Technology
NUS - National University of Singapore
University of Oxford
Arizona State University
Columbia
The Hong Kong University Of Science And Technology
Massachusetts Institute of Technology
NUS - National University of Singapore
University of Oxford
NVIDIA GTC

SOCIOS

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.