This course is intended to give learners and introduction to Natural Language Processing (NLP) and give them the skills they need to enter a Kaggle competition focusing on NLP. The learners will be introduced to the Natural Language Tool Kit (NLTK), Spacy, and the sklearn machine learning library. The course is broken down into three sections, being an introduction to NLP, practice projects, and lastly the chance to enter a Kaggle competition.

Welcome to Data Science: Transformers for Natural Language Processing. Ever since Transformers arrived on the scene, deep learning hasn't been the same. We've reached new state-of-the-art performance in many NLP tasks, such as machine translation, question-answering, entailment, named entity recognition, and more We've created multi-modal (text and image) models that can generate amazing art using only a text prompt We've solved a longstanding problem in molecular biology known as "protein structure prediction" In this course, you will learn very practical skills for applying transformers, and if you want, detailed theory behind how transformers and attention work. This is different from most other resources, which only cover the former. In this section, you will learn how to use transformers which were trained for you.

Hawkes processes have recently gained increasing attention from the machine learning community for their versatility in modeling event sequence data. While they have a rich history going back decades, some of their properties, such as sample complexity for learning the parameters and releasing differentially private versions, are yet to be thoroughly analyzed. In this work, we study standard Hawkes processes with background intensity $\mu$ and excitation function $\alpha e^{-\beta t}$. We provide both non-private and differentially private estimators of $\mu$ and $\alpha$, and obtain sample complexity results in both settings to quantify the cost of privacy. Our analysis exploits the strong mixing property of Hawkes processes and classical central limit theorem results for weakly dependent random variables. We validate our theoretical findings on both synthetic and real datasets.

You're looking for a complete Convolutional Neural Network (CNN) course that teaches you everything you need to create an Image Recognition model in R, right? You've found the right Convolutional Neural Networks course! How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Convolutional Neural networks course. If you are an Analyst or an ML scientist, or a student who wants to learn and apply Deep learning in Real world image recognition problems, this course will give you a solid base for that by teaching you some of the most advanced concepts of Deep Learning and their implementation in R without getting too Mathematical.

You're looking for a complete Linear Regression and Logistic Regression course that teaches you everything you need to create a Linear or Logistic Regression model in R Studio, right? You've found the right Linear Regression course! A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course. How this course will help you? Why should you choose this course?

Three Carnegie Mellon University researchers in the School of Computer Science recently earned Faculty Early Career Development Program (CAREER) awards from the National Science Foundation. The awards are the foundation's most prestigious for young faculty researchers. An assistant professor in the Computer Science and Electrical and Computer Engineering departments, Weina Wang received $500,000 to develop algorithms that guarantee ultra-low latency in edge computing, which supports emerging applications such as autonomous driving, augmented reality and automated mobile robots. This work will establish algorithms to optimize the time it takes for data to travel from one point to another and for the corresponding computation to be done without lag, even with a high volume of users in those systems. In addition to this research, Wang will also use the grant to continue expanding STEM outreach activities for K-12 students -- mentoring students from underrepresented groups, promoting the visibility of researchers from underrepresented groups and initiating online outreach seminars for the general public.

The big news: DALL-E 2 is now in beta! OpenAI just announced the release of DALL-E 2 to 1 million people, ten times more than the pre-beta model. You can no longer spam generations to have funny memes for free -- it is now nearly $300 for the same amount of free generations you had pre-beta. We had some terrific publications this past week like NUWA, BigColor, and Mega Portraits, all advancing the image generation field with fantastic approaches and results -- as well as the ICML 2022 event that released its outstanding papers that are worth the read. Last but not least, listen to this podcast hosted by one of our community members in this iteration!

Abstract: Scientific machine learning (SciML) is the burgeoning field combining scientific knowledge with machine learning for data-efficient predictive modeling. We will introduce SciML as the key to effective learning in many engineering applications, such as improving the fidelity of climate models to accelerating clinical trials. This will lead us to the question on the frontier of SciML: what about stiffness? Stiffness is a pervasive quality throughout engineering systems and the most common cause of numerical difficulties in simulation. We will see that handling stiffness in learning, and thus real-world models, requires new training techniques.

The 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence (IJACI-ECAI 2022) is underway in Vienna. So far, participants have been treated to some excellent invited talks and a varied programme of workshops and tutorials. Find out what people have been up to in this round-up from the Twitter-sphere. Really enjoyed attending the workshop for AI Evaluation Beyond Metrics at #IJCAI2022 this weekend, in particular, @adinamwilliams presentation on "No Escape from Qualitative Evaluation" pic.twitter.com/aYvnPrUprU At the Workshop on Complex Data Challenges in Earth Observation.

It is widely acknowledged that Ada Lovelace, born in the 19th century, was the first computer programmer but her contributions and that of many other brilliant women in science and technology have been erased and attributed to men. Even today, diverse voices in the male-dominated tech industry are overlooked and non-traditional backgrounds dismissed as "not techy enough". During a recent podcast series in collaboration with IBM, I had the opportunity to meet amazing women from multi-disciplinary backgrounds in key AI and technology roles at the tech giant. Their non-typical backgrounds give them an unique ability to identify opportunities as well as ethical gaps in AI that would be otherwise missed. Here are some highlights from our wide-ranging conversations that remind us of the critical importance of non-traditional backgrounds in technology.