ChatGPT has taken the internet by storm. People have been using it to compose music, understand complex topics, make jokes, write movie scripts, and even debug computer codes. Such is the bot's popularity; it took only five days to score its first million users. This detailed tutorial explains precisely how to use ChatGPT. But before we delve into the details, let's first consider what ChatGPT is and what's causing the huge buzz surrounding the latest AI tool.

Automatic medical image classification is a very important field where the use of AI has the potential to have a real social impact. However, there are still many challenges that act as obstacles to making practically effective solutions. One of those is the fact that most of the medical imaging datasets have a class imbalance problem. This leads to the fact that existing AI techniques, particularly neural network-based deep-learning methodologies, often perform poorly in such scenarios. Thus this makes this area an interesting and active research focus for researchers. In this study, we propose a novel loss function to train neural network models to mitigate this critical issue in this important field. Through rigorous experiments on three independently collected datasets of three different medical imaging domains, we empirically show that our proposed loss function consistently performs well with an improvement between 2%-10% macro f1 when compared to the baseline models. We hope that our work will precipitate new research toward a more generalized approach to medical image classification.

Want to make sense of the volumes of data you have collected? Need to incorporate data-driven decisions into your process? This course provides an overview of machine learning techniques to explore, analyze, and leverage data. You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems. At the end of the course, you will be able to: • Design an approach to leverage data using the steps in the machine learning process.

This course is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field. We will cover health data analysis, different types of neural networks, as well as training and application of neural networks applied on real-world medical scenarios. We cover deep learning (DL) methods, healthcare data and applications using DL methods. The courses include activities such as video lectures, self guided programming labs, homework assignments (both written and programming), and a large project. The first phase of the course will include video lectures on different DL and health applications topics, self-guided labs and multiple homework assignments.

Data integrity is one of the biggest concerns for companies and engineers in the latest period. The amount of data we have to process and understand only gets more significant, and manually looking at millions of samples is not sustainable. Thus, we need tools that can help us navigate our datasets. This tutorial will present how to clean, visualize and understand Computer Vision datasets, such as videos or images. We will be working on a video of the most precious thing in my house, my cat.

Here is my story: I recently gave a university tutoring class to MSc students on deep learning. Specifically, it was about training their first multi-layer perceptron (MLP) in Pytorch. I was literally stunned from their questions as beginners in the field. At the same time, I resonated with their struggles and reflected back to being a beginner myself. That's what this blogpost is all about. If you are used to numpy, tensorflow or if you want to deepen your understanding in deep learning, with a hands-on coding tutorial, hop in.

Throughout this Nano-degree program, you will learn different data structures for storing data, different methods to manipulate these data structures and examine the efficiency, searching and sorting on different data structures, and more advanced algorithms such as brute-force greedy algorithms, graph algorithms, and dynamic programming.

We are about to wrap up 2022, a year that brought plenty of Machine Learning projects, events, education opportunities, and many groundbreaking Machine Learning applications developed by ML practitioners around the world. The challenges and business needs of our customers continue to fuel our passion to bring to life the robust and innovative Machine Learning solutions they deserve. In this blog post, we put together the highlights of 2022 covering Machine Learning's lasting impact on a vast number of industries and businesses, BigML's new additions and enhancements to our pioneering Machine Learning software platform, our live and virtual events, education initiative updates, and much more! None of the numbers listed above and the activities described on this blog post would be possible without our customers, partners, followers, and certified practitioners. That's why this blog post is dedicated to all of you.

ChatGPT by OpenAI has quickly taken the world by storm. ChatGPT can write poetry, short stories, haikus, character biographies, computer code, and much, much more. In this course, we get you started with ChatGPT, getting you signed up for your own free account. From there, we randomly select topics to learn more about ChatGPT. We use ChatGPT to see how deep and wide this machine language tools learning base actually is.

MobiSpaces delivers an end-to-end mobility-aware and mobility optimised data governance platform, that concerns the offerings of data acquisition, in-situ processing and all the security- and privacy-related operations. MobiSpaces envisions a set of toolboxes, suites, and tools that implement the MobiSpaces concept. MobiSpaces identifies the AI-based Data Operations Toolbox, including an additional list of tools, namely the Declarative querying, Decentralized Data Management, and Online Data Aggregator. In addition MobiSpaces utilises the Edge Analytics Suite, including a further list of tools, namely the XAI Prediction Modelling, Edge-driven Federated Learning, and Visual Analytics. MobiSpaces currently has five use cases utilising these tools including; iRoute, SmartSense, MarineTrafficTracker, Vessel Edge, and CrowdSeaMapping. Join us in our first introductory webinar " Meet the MobiSpaces Use Cases: Innovations for Urban and Maritime Domains " on 31 January 2023, 11:00-12:15 CEST where you can hear directly from the project.