This post contains a list of the AI-related seminars that are scheduled to take place between 11 April 2022 and 31 May 2022. All events detailed here are free and open for anyone to attend virtually. Accelerating various AI algorithms on the edge: from software to hardware challenges Speaker: Martin Andraud Organised by: Finnish Centre for AI Zoom link is here. Title to be confirmed Speaker: Naomi Saphra (NYU) Organised by: New York University Please contact the organisers here if you are interested in attending the virtual seminar. EPFL CIS – RIKEN AIP Seminar Series Speaker: Emtiyaz Khan Organised by: EPFL Zoom link will be provided here nearer the time.

Do you want to learn Computer Vision and looking for the best resources to learn Computer Vision?… If yes, you are in the right place. In this article, I have listed all the best resources to learn Computer Vision including Online Courses, Tutorials, Books, and YouTube Videos. So, give your few minutes and find out the best resources to learn Computer Vision. You can bookmark this article so that you can refer to this article later.

Evaluation metrics to analyze the performance of models. Different methods to deal with imbalanced data. Implementation of Content and Collaborative based filtering. Implementation of Different algorithms used for Time Series forecasting. Evaluation metrics to analyze the performance of models.

How to create a performance budget for a game and stick to it. How to optimise Unity API calls to use up less memory and CPU time. How to optimise art and sound assets to speed up their processing at runtime. How to tweak the Unity physics system to accelerate performance. How to create a performance budget for a game and stick to it.

Are you ready to master Artificial Intelligence skills? Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. It is the simulation of natural intelligence in machines that are programmed to learn and mimic the actions of humans. These machines are able to learn with experience and perform human-like tasks. Artificial intelligence (AI) is one of the top tech fields to be in right now! Financial institutions, legal institutions, media companies, and insurance companies are all figuring out ways to use artificial intelligence (ai) to their advantage.

Creating a Neural Network from Scratch in JAVA, we will also build our very own Matrix Library. Learn how to create and use neural networks in your Java programs. This course teaches you not only how to implement machine learning AI with your own artificial neural networks (ANNs), but also the principles of how artificial neural networks work -- to the point that you can implement your own. You'll need only a knowledge of Java programming and basic algebra; in this course you'll learn the relevant linear algebra, information theory and calculus, and together we'll build a fast and efficient neural network from scratch, able to recognise handwritten digits and easily adapted to other tasks. After taking the course, artificial neural networks won't be a mystery to you any more.

Artificial Intelligence (AI) is disrupting businesses and job roles in every industry, causing concerns about long-term job security for low-skill manual jobs and management roles alike. To prepare for this AI-driven economy, many experienced managers and seasoned executives are turning to MOOCs (Massive Open Online Courses) to upskill in foundational data analytics and AI. This trend is unlikely to slow down anytime soon: The global MOOC market is expected to grow from $3.9 billion in 2018 to $20.8 billion by 2023, a CAGR of 40.1 percent. Business and technology-related courses make up 40 percent of these online courses. Many universities have also joined the drive to fill the AI leadership gap by offering high-touch executive education programs.

In our last post on deploying a machine learning pipeline in the cloud, we demonstrated how to develop a machine learning pipeline in PyCaret, containerize it with Docker and serve as a web app using Microsoft Azure Web App Services. If you haven't heard about PyCaret before, please read this announcement to learn more. In this tutorial, we will use the same machine learning pipeline and Flask app that we built and deployed previously. This time we will demonstrate how to containerize and deploy a machine learning pipeline on Google Kubernetes Engine. Previously we demonstrated how to deploy a ML pipeline on Heroku PaaS and how to deploy a ML pipeline on Azure Web Services with a Docker container.

The University of Tennessee, Knoxville, is among the 14 member universities of the Southeastern Conference forming a new artificial intelligence consortium. The consortium is designed to grow opportunities in the fast-changing fields of AI and data science, which are expected to be foundational for the future of industry, education, and research. It is believed to be the first athletics conference collaboration to focus on AI for workforce development. "Artificial intelligence and data science have universal applications, and we are proud to join with SEC universities to lead education and research in these emerging fields," said Provost and Senior Vice Chancellor John Zomchick. "The consortium will create exciting opportunities for our faculty, staff, and students to explore AI and data science and shape their future."

Books and courses where two main resources that I used to get to where I am right now. Among all the resources in this section, Kaggle stands out as one of the most amazing platforms on the internet for practicing and learning machine learning! Kaggle hosts lots of data science and deep learning competitions every year with participants taking part from all over the world. There are also loads of invaluable datasets on Kaggle that you can use to train your own models, all for free. The other amazing part of this platform is where people can share their code and solution to the competitions, all in a simple Jupyter notebook which makes experimenting with the code really easy.