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American poker pro Bryan Paris has cashed for more than $12 million on the virtual tables so far, and in 2016 was the second player to hit $10 million in online cashes - a feat only 33 players have ever achieved (according to PocketFives’ All-Time Leaderboard).

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This Natural8 Team Hot ambassador has also cashed for nearly $800,000 on the live poker circuit, counting EPT Vienna and Prague among his favourite stops.

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Paris has been playing poker since 2002, when he first played home games with his high school friends. Before that, he was into the popular Magic: The Gathering card game, and found the transition to poker a seamless one. Paris moved to Canada for a bit in 2011, but now calls Amsterdam home.

His preferred game type is the plain and simple No Limit Hold'em tournaments.

“To start my sessions I'll frequently ramp up to 20 tables or more, but it tails off quickly as I bust things. The most I ever played was probably somewhere north of 30 back in the old days, although I certainly would not recommend going that high. I'm probably most comfortable around 12-15. Having too few tables actually risks me losing focus unless they are for substantial stakes.”

Most recently, Paris added a victory in the popular $210 Beat the Pros bounty event - where players earn a seat in a $5,000 Freeroll for knocking out sponsored pros, ambassadors and other featured participants - to his 2020 resume.

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NVIDIA’s Deep Learning Institute (DLI) delivers practical, hands-on training and certification in AI at the edge for developers, educators, students, and lifelong learners. This is a great way to get the critical AI skills you need to thrive and advance in your career. You can even earn certificates to demonstrate your understanding of Jetson and AI when you complete these free, open-source courses.

Jetson AI Certification

NVIDIA offers two certification tracks— Jetson AI Specialist that anyone can complete and Jetson AI Ambassador for educators and instructors.

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Jetson AI Specialist

Want to get started learning about AI? This certification can be completed by anyone, and recognizes your competency in Jetson and AI using a hands-on, project-based assessment. While this track is ideal for advanced learners to build on their existing AI knowledge, beginners can follow the in-depth video tutorials and get up to speed quickly.

Recommended Prerequisites

• Basic familiarity with Python and Linux

Jetson AI Ambassador

This certification is for educators and recognizes competency in teaching AI on Jetson using a hands-on, project-based assessment and an interview with the NVIDIA team. This track is ideal for educators or instructors who want to be fully prepared to teach AI to their students.

In addition to certification, we offer freely available curriculum and open-source platforms for educators to build their AI curriculum.

Benefits

• Expense reimbursement (i.e. catering, travel, and other qualifying workshop expenses) up to USD$500 per approved event held free exclusively for academic students and staff
• Consideration for free developer kits (up to five) through the Jetson Nano 2GB Developer Kit Grant program
• Formal inclusion in DLI Certified Instructor Program
• Additional benefits are listed here.

Recommended Prerequisites

• Basic familiarity with Python and Linux
• Teaching or training experience at an academic institution or formal training program

The requirements for each of the certifications are noted below.

This is the flow for the certifications.

Testimonials

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We know how important it is to provide all students with opportunities to impact the future of technology. We’re excited to utilize the NVIDIA Jetson AI Specialist certification materials with our students as they work toward becoming leaders in the fields of AI and robotics.

Christine Nguyen, STEM curriculum director at Boys & Girls Club of Western Pennsylvania

NVIDIA’s Jetson AI Certification materials thoroughly cover the fundamentals with the added advantage of hands-on project-based learning. I believe these benefits provide a great foundation for students to prepare for university robotics courses and compete in robotics competitions.

Jack Silberman PhD, Lecturer, UC San Diego, Jacobs School of Engineering, Contextual Robotics Institute

Jetson AI Fundamentals Course Outline

Jetson AI Fundamentals Tutorial Playlist

Four hours of online content

Open-source content

Hardware Requirements

• NVIDIA Jetson Nano™ Developer Kit or Jetson Nano 2GB Developer Kit*
• MicroSD card (64 GB UHS-1 recommended, 32 GB UHS-1 minimum)
• Micro-B USB cable
• Power supply
• Camera (Logitech C270 USB webcam or Raspberry Pi camera module v2)
• PC or laptop with an SD card slot (Windows, Mac, or Linux)
• Recommended: NVIDIA JetBot

Command-Line Interface and Linux Commands

In this video, we’ll cover how to navigate the Linux system from the terminal and command line. You’ll also see how to move, copy, delete, and edit files. (Courtesy of Paul McWhorter)

Introduction to Python

In this video lesson, we explore how to code in Python. You’ll learn about printing, user input, for loops, if statements, conditionals, while loops, and arrays. (Courtesy of Paul McWhorter)

In this course, you'll use Jupyter notebooks on your own Jetson Nano to build deep learning classification and regression projects with computer vision models.

Register to the course and follow the videos below that walk you through it.

Episode 1

This episode covers setting up your Jetson Nano for the very first time with JetPack.

Episode 2

This episode covers how to log into your Jetson Nano remotely and run a Docker container to access JupyterLab and the course notebooks. You'll test out your camera in the Hello Camera Jupyter notebook for the first time.

Episode 3

This episode walks through a PyTorch project to train a deep neural network on your Jetson Nano, and experiment with image classification using data you collect with your camera.

Episode 4

This episode covers how to train an image regression deep neural network model to infer X-Y coordinates for specific objects in images you collect with your camera.

JetBot is an open-source AI robot platform that gives makers, students, and enthusiasts everything they need to build creative, fun, smart AI applications. It’s powered by the Jetson Nano Developer Kit, which supports multiple sensors and neural networks in parallel for object recognition, collision avoidance, and more. The Jetbot wiki is here and you can follow the videos below to understand and learn more about its capabilities. You can buy JetBot from one of our partners here. Note: JetBot is an optional component of the Jetson AI Fundamentals Course.

Episode 1 - JetBot Intro and Hardware

The Jetson Nano JetBot is a great introduction to robotics and deep learning. There are several options in building your own hardware, here we share some valuable tips building yours.

Episode 2 - JetBot Software Setup

After building your JetBot hardware, we go through the process of setting up the software using a container based approach. Using containers allows us to load all of the necessary deep learning libraries without having to worry about dependencies on other software packages. We also test the motors!

Episode 3 - JetBot Collision Avoidance

With the software loaded, and the motors running the JetBot is now ready to use deep learning to help avoid collisions when navigating. We gather images and train a model using transfer learning to avoid running into things, and then load it on to the JetBot to test.

Episode 4 - JetBot Road Following

Here we train JetBot to follow a path. We gather training data, and then use regression to train a model to predict a good path forward. Once the model is on the JetBot, the JetBot drives along the path.

Hello AI World is a great way to start using Jetson and experience the power of AI. In just a couple of hours, you can have a set of deep learning inference demos up and running for real-time image classification, object detection, and segmentation on your Jetson Developer Kit with the JetPack SDK™ and NVIDIA TensorRT™. You can also collect your own datasets and train your own DNN models onboard Jetson using PyTorch.

Episode 1 - Hello AI World Setup

Download and run the Hello AI World container on Jetson Nano, test your camera feed, and see how to stream it over the network via RTP.

Episode 2 - Image Classification Inference

Code your own Python program for image classification using Jetson Nano and deep learning, then experiment with realtime classification on a live camera stream.

Episode 3 - Training Image Classification Models

Learn how to train image classification models with PyTorch onboard Jetson Nano, and collect your own classification datasets to create custom models.

Episode 4 - Object Detection Inference

Code your own Python program for object detection using Jetson Nano and deep learning, then experiment with realtime detection on a live camera stream.

Episode 5 - Training Object Detection Models

Learn how to train object detection models with PyTorch onboard Jetson Nano, and collect your own detection datasets to create custom models.

Episode 6 - Semantic Segmentation

Experiment with fully-convolutional semantic segmentation networks on Jetson Nano, and run realtime segmentation on a live camera stream.

Based on your understanding of the material, you’re required to build and submit an open-source project that uses NVIDIA Jetson and incorporates elements of AI (machine learning or deep learning) with GPU acceleration, along with a video demonstrating the project in action. For example, you could collect your own dataset and train a new DNN model for a specific application, add a new autonomous mode to JetBot, or create a smart home / IoT device using AI - these need not be limited only to topics covered in the course. For inspiration, see the Jetson Community Projects page - the possibilities are endless!

In order to pass the certification, your project will be reviewed based on the following criteria:

• AI (5 points) - The project uses deep learning, machine learning, and/or computer vision in a meaningful way, and demonstrates a fundamental understanding of creating applications with AI. Factors include the effectiveness, technical complexity, and performance of your AI solution on Jetson.
• Impact / Originality (5 points) - The concept of your project is novel and applies AI to solve or address challenges or issues faced by yourself or society. Also, our ideas and work are either original or derivative in a significant way.
• Reproducibility (5 points) - Any plans, code, and resources needed for someone else to build and use the project are included in the repository and are easy to follow.
• Presentation and Documentation (5 points) - The video effectively demonstrates and explains various aspects of the project, and there exists a clear, complete readme in the repository that documents any steps needed to build/run the project along with diagrams and images. Note that educators should have an oral presentation component to their video to highlight their teaching abilities.

Within approximately 10-14 days, you will receive your project scores, and if approved, your appropriate Jetson AI certificate will be delivered.

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