This site requires Javascript in order to view all its content. Please enable Javascript in order to access all the functionality of this web site. Here are the instructions how to enable JavaScript in your web browser.

Instructor-Led Workshop
Building Intelligent Recommender Systems

Request a workshop for your organization

Register for public workshops

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 predict the types of movies an individual will enjoy based on the movies they’ve previously 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.

Learning Objectives

By participating in this 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 and NVTabular data loaders 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

Download workshop datasheet (PDF 79.8 KB)

Workshop Outline

Introduction (15 mins)	Meet the instructor. Create an account at courses.nvidia.com/join
Matrix-Based Recommender Systems (120 mins)	Implement collaborative filtering with singular value decomposition (SVD): Read sparse data into a GPU using CuPy. Perform ALS efficiently with NumPy broadcasting rules. Build a content-based filter with cuDF.
Break (60 mins)
Training Wide and Deep Recommenders (120 mins)	Build a wide and deep network using TensorFlow 2: Build a deep network using Keras. Build a wide and deep network using TensorFlow feature columns. Efficiently ingest training data with tf.data. Case study 1: See real-world examples of recommender system model architectures.
Break (15 mins)
Challenges of Deploying Recommendation Systems to Production (120 mins)	Deploy a recommender system in a production environment: Acquire a trained model configuration for deployment. Build a container for deployment. Deploy the trained model using NVIDIA Triton Inference Server.
Final Review (15 mins)	Review key learnings and answer questions. Learn to build your own training environment from the DLI base environment container. Complete the assessment and earn a certificate. Take the workshop survey. Case study 2: Review real-world challenges of at-scale recommender systems.

Workshop Details

Duration: 8 hours

Price: Contact us for pricing.

Prerequisites:

Intermediate knowledge of Python, including understanding of list comprehension
Data science experience using Python
Familiarity with NumPy and matrix mathematics

Suggested resources to satisfy prerequisites: Python Tutorial, Kaggle Learn Machine Learning Course, NumPy Tutorial

Technologies: CuDF, CuPy, TensorFlow 2, NVIDIA Triton^™ Inference Server

Assessment Type: Skills-based coding assessments evaluate students’ ability to debug and correct production-quality recommendation pipelines.

Certificate: Upon successful completion of the assessment, participants will receive an NVIDIA DLI certificate to recognize their subject matter competency and support professional career growth.

Hardware Requirements: Desktop or laptop computer capable of running the latest version of Chrome or Firefox. Each participant will be provided with dedicated access to a fully configured, GPU-accelerated server in the cloud.

Language: English

Upcoming Workshops

Central European Time

Tue, May 4, 2021

9:00 a.m.–5:00 p.m.

Register now

Pacific Time

GTC

Tue, May 11, 2021

9:00 a.m.–5:00 p.m.

Register now

If your organization is interested in boosting and developing key skills in AI, accelerated data science, or accelerated computing, you can request instructor-led training from the NVIDIA DLI.

Request a Workshop

Questions?

Contact Us for Questions on
Deep Learning Training

Section

Section

First Name

Last Name

Business Email Address

Organization/University Name

Industry

Job Title

Job Role

Location

City

State

Reason for requesting contact

本人知悉且同意 NVIDIA Corporation 基于调研、活动组织的目的对本人的上述信息的收集和处理，并已经阅读并同意 <a href="https://www.nvidia.cn/about-nvidia/privacy-policy/" target="_blank">NVIDIA 隐私政策</a>。

本人知悉且同意，因调研、活动组织的必须，以及相对应的 NVIDIA 内部管理和系统操作的需要，上述信息会被传输到位于美国的 NVIDIA Corporation 按照符合 <a href="https://www.nvidia.cn/about-nvidia/privacy-policy/" target="_blank">NVIDIA 隐私政策</a>的方式进行存储，您可以通过发送邮件至 <a href="mailto:privacy@nvidia.com">privacy@nvidia.com</a> 进行联系以解决相关问题，实现可适用的数据保护法所规定的权利。

Instructor-Led Workshop
Building Intelligent Recommender Systems

Learning Objectives

Workshop Outline

Workshop Details

Upcoming Workshops

Tue, May 4, 2021

Tue, May 11, 2021

Questions?

Contact Us for Questions on
Deep Learning Training

Read our FAQs.

Inquire about NVIDIA Deep Learning Institute services.

For technical questions, check out the NVIDIA Developer Forums.

Instructor-Led Workshop Building Intelligent Recommender Systems

Learning Objectives

Workshop Outline

Workshop Details

Upcoming Workshops

Tue, May 4, 2021

Tue, May 11, 2021

Questions?

Contact Us for Questions on Deep Learning Training

Read our FAQs.

Inquire about NVIDIA Deep Learning Institute services.

For technical questions, check out the NVIDIA Developer Forums.

Instructor-Led Workshop
Building Intelligent Recommender Systems

Contact Us for Questions on
Deep Learning Training