This workshop is intended to bring together the Machine Learning (ML), Artificial Intelligence (AI) and High Performance Computing (HPC) communities. In recent years, much progress has been made in Machine Learning and Artificial Intelligence in general. This progress required heavy use of high performance computers and accelerators. Moreover, ML and AI have become a "killer application" for HPC and, consequently, driven much research in this area as well. These facts point to an important cross-fertilization that this workshop intends to nourish. We invite authors to submit original work to HPML.

YOLOv3 is one of the most popular real-time object detectors in Computer Vision. In our previous post, we shared how to use YOLOv3 in an OpenCV application. It was very well received and many readers asked us to write a post on how to train YOLOv3 for new objects (i.e. In this step-by-step tutorial, we start with a simple case of how to train a 1-class object detector using YOLOv3. The tutorial is written with beginners in mind. Continuing with the spirit of the holidays, we will build our own snowman detector.

For many people, speaking off the cuff to a large audience does not come easily. But without professional feedback, rehearsing speeches and presentations can be a tough process. A psychologist, a management scientist and an IT specialist have developed an online training tool that uses artificial intelligence to evaluate users' speaking skills and personal characteristics. The team has now established the start-up Retorio at the Technical University of Munich (TUM) to launch the software on the market. It's a scenario many people can relate to – standing all alone in front of an audience, clutching a microphone with clammy hands and finding one's mouth has gone dry. Whether it's a job interview or a wedding speech: for many people, the idea of speaking in public is associated with anxiety and uncertainty.

We propose a multimodal approach for detection of students' behavioral engagement states (i.e., On-Task vs. Off-Task), based on three unobtrusive modalities: Appearance, Context-Performance, and Mouse. Final behavioral engagement states are achieved by fusing modality-specific classifiers at the decision level. Various experiments were conducted on a student dataset collected in an authentic classroom.

This video is a part of a free online course that provides introduction to practical deep learning methods using MATLAB. In addition to short engaging videos, the course also contains interactive, in-browser MATLAB projects. For a 14-hour comprehensive course covering the theory and practice of deep learning using real-world image and sequence data, see: http://bit.ly/2DjaTdh

Struggling to get started with machine learning using Python? In this step-by-step, hands-on tutorial you will learn how to perform machine learning using Python on numerical data and image data. By the time you are finished reading this post, you will be able to get your start in machine learning. To launch your machine learning in Python education, just keep reading! Inside this tutorial, you will learn how to perform machine learning in Python on numerical data and image data. Using this technique you will be able to get your start with machine learning and Python! Along the way, you'll discover popular machine learning algorithms that you can use in your own projects as well, including: This hands-on experience will give you the knowledge (and confidence) you need to apply machine learning in Python to your own projects. Before we can get started with this tutorial you first need to make sure your system is configured for machine learning. Today's code requires the following libraries: In order to help you gain experience performing machine learning in Python, we'll be working with two separate datasets. The first one, the Iris dataset, is the machine learning practitioner's equivalent of "Hello, World!" (likely one of the first pieces of software you wrote when learning how to program). The second dataset, 3-scenes, is an example image dataset I put together -- this dataset will help you gain experience working with image data, and most importantly, learn what techniques work best for numerical/categorical datasets vs. image datasets. Let's go ahead and get a more intimate look at these datasets.

The Stanford CS 224N course - Natural Language Processing with Deep Learning is known to be one of the best courses around for as evident by the title. I recently started doing the course as I wanted to do NLP for a long time now and considering that the 2019 course is going to be publicly available (http://web.stanford.edu/class/cs224n/), I thought that it might be a good idea to write a blog as I go through the assignments. In a series of stories, I'll be releasing the solutions as I go on about the course. Feel free to comment if you find a mistake or a better solution to mine.

Just enter code fccstevens into the promotional discount code box at checkout at manning.com. In part one, we learned about PyTorch and its component parts, now let's take a closer look and see what it can do. In this article, we explore some of PyTorch's capabilities by playing with pre-trained networks. Computer vision -- a field that deals with making computers to gain high-level understanding from digital images or videos -- is certainly one of the fields most impacted by the advent of deep learning, for a variety of reasons. The need for classifying or interpreting the content of natural images was there, huge datasets became available and new constructs, such as convolutional layers, came about and started to run quickly on GPUs with unprecedented accuracies.

Google.org is still looking for for organizations around the world to submit ideas for solving societal problems with AI. If your idea is selected, you will receive Google.org grant funding from a $25M pool, support and consulting with Google's AI and cloud experts, and more resources to help your idea become a reality. The deadline to apply is January 22. Need some help getting started? Get artificial intelligence training with these resources and online training modules, then submit your ideas by January 22 – good luck!

Learn how to build and train Neural Networks using the most popular machine learning framework for JavaScript, TensorFlow.js. This is a practical workshop where you'll learn "hands-on" by building 5 different applications from scratch using TensorFlow.js. By the end you'll know: – The *essential* mathematics. If you have ever been interested in Machine Learning, if you want to get a taste for what this exciting field has to offer, if you want to be able to talk to other Machine Learning/AI specialists in a language they understand, then this workshop is for you.