Over these past couple of months, ChatGPT has been all over the news. Many businesses are already leveraging the technology to get ahead of the competition. The Complete ChatGPT Artificial Intelligence OpenAI Training Bundle helps you follow suit, with four courses that showcase the hidden power of this AI platform. The training is worth a total of $800, but you can grab all four courses today for only $19.97 in a special price drop at TechRepublic Academy. Although ChatGPT has only just exploded onto the scene, the technology has had a massive impact.

The US Federal Trade Commission is paying close attention to developments in artificial intelligence to ensure the field isn't dominated by the major tech platforms, Chair Lina Khan said Monday. "As you have machine learning that depends on huge amounts of data and also a huge amount of storage, we need to be very vigilant to make sure that this is not just another site for big companies to become bigger," Khan said at an event hosted by the Justice Department in Washington.

As companies plunge into the world of data, skilled individuals who can extract valuable insights from an ocean of information are in high demand. Join the data revolution and secure a competitive edge for businesses vying for supremacy. Data Scientists and Analysts use various tools such as machine learning algorithms, statistical modeling, natural language processing (NLP), and predictive analytics to identify trends, uncover opportunities for improvement, and make better decisions. With the right combination of technical know-how, communication skills, problem solving abilities, and creative thinking – these professionals can help organizations gain a competitive advantage by leveraging data effectively. Data science and data analysis have rapidly emerged as flourishing and versatile career paths, encompassing a wide range of industries and applications.

Abstract: We provide a comprehensive reply to the comment written by Stefan Boettcher [arXiv:2210.00623] Conversely, we highlight the broader algorithmic development underlying our original work, and (within our original framework) provide additional numerical results showing sizable improvements over our original data, thereby refuting the comment's original performance statements. Furthermore, it has already been shown that physics-inspired graph neural networks (PI-GNNs) can outperform greedy algorithms, in particular on hard, dense instances. We also argue that the internal (parallel) anatomy of graph neural networks is very different from the (sequential) nature of greedy algorithms, and (based on their usage at the scale of real-world social networks) point out that graph neural networks have demonstrated their potential for superior scalability compared to existing heuristics such as extremal optimization. Finally, we conclude highlighting the conceptual novelty of our work and outline some potential extensions.

In my previous article, we learned about Autoencoders, now let's continue to talk about Generative AI. By now everyone is talking about it and everyone is excited about the practical applications that have been developed. But we continue to see the foundations of these AIs step by step. There are several Machine Learning models that allow us to build generative AI, to name a few we have Variational Autoencoders (VAE), autoregressive models and even normalizing flow models. In this article, however, we will focus on GANs.

Abstract: Deep Learning (DL) has developed to become a corner-stone in many everyday applications that we are now relying on. However, making sure that the DL model uses the underlying hardware efficiently takes a lot of effort. Knowledge about inference characteristics can help to find the right match so that enough resources are given to the model, but not too much. We have developed a DL Inference Performance Predictive Model (DIPPM) that predicts the inference latency, energy, and memory usage of a given input DL model on the NVIDIA A100 GPU. We also devised an algorithm to suggest the appropriate A100 Multi-Instance GPU profile from the output of DIPPM.

Come to talk digital health with friends . We are bringing together company leaders & investors in digtial health. Stay for a short visit or as long as you like. The theme of the evening is "Artificial intelligence will do 80% of what doctors do by 2030." Steven Wardell is the author of The Future of Digital Health, a growth & fundraising consultant to digital health companies with Wardell Advisors, & a former Wall Street equity research analyst covering digital health & therapeutics.

This is a column series that focuses on data quality for data science. This constitutes the first piece and focuses on Imbalanced Data, Underrepresented Data, and Overlapped Data. That is the curse of learning from data. In this piece, I'll go over the importance of feeding high-quality data to your machine learning models and introduce you to killer data quality issues that, if left unchecked, may utterly compromise your data science projects. From social to medical applications, machine learning has become deeply entangled in our daily lives.

In this article, I will present you the deep learning project that I wanted to perform, then I'll present the techniques and approach that I used to tacle this. And I will end up that article with some meaningful reflections, that I hope would help some of you. I wanted to build a smartphone app which can recognize flower from taken picture. Basically the app is splitted into two parts, the front-end part which is basically the mobile development. I wanted to build from scratch a deep learning model without deep learning framework, to help me understand the inner working process of image classification (I know it sounds crazy).

Machine learning is a rapidly evolving field that has shown incredible promise in revolutionizing various industries, from healthcare to finance and beyond. However, conducting machine learning experiments is a complex and iterative process that involves numerous experiments with different datasets, models, and hyperparameters. This process can be time-consuming, and it's often challenging to keep track of all the experiments and their outcomes. Machine learning experiment tracking is a crucial tool that enables researchers to streamline the experimentation process, improve model performance, and ensure reproducibility. By tracking experiments, researchers can analyze the results obtained from different configurations systematically, select the best datasets and hyperparameters for their models, and collaborate with others in the field. In this article, we provide a comprehensive introduction to machine learning experiment tracking, covering the essential concepts, best practices, and tools available for implementing an effective experiment-tracking system.