Two Minute Papers takes a look at the paper “First Order Motion Model for Image Animation” and its source code are available here:
Computer have wide applications across industries for quality control.
For instance, the majority of all medical data is image-based: The assessment of X-rays and scans is crucial for the right diagnosis and, thus, for the right treatment.
Public health depends on the accurate interpretation of every single image, and many physicians are obliged to choose between longer working hours or doing less detailed and precise medical image analysis.
Right now, medical staffers around the world are stretched thin, this is where AI can come into play.
Artificial intelligence in healthcare speeds up the process of medical image analysis and makes it more accurate and stress-free for medical personnel. Using artificial intelligence, it is possible to detect rare diseases, such as Noonan syndrome, or identify viruses and bacteria by analyzing Petri dish images. Computer vision and machine learning for medical image analysis are becoming as vital as an experienced lab worker with modern equipment.
GraphQL is a query language that is rapidly gaining wide adoption across the community.
It combines type validation with a query and filtering syntax that makes it easy to get up-and-running with a powerful web API in almost no time.
Features like running parallel queries or update-all become much easier because they are first-class citizens of GraphQL. Add to that a vibrant community that keeps creating excellent tooling and documentation, it’s clear why GraphQL has become so popular with developers
Every abstraction has a cost, and GraphQL is no exception. The added complexity and a new schema format to parse and execute mean new performance bottlenecks. In addition to performance issues, the wrong use of GraphQL can lead to architectural bottlenecks.
Instead of viewing this as a problem, NearForm took this as a challenge.
More details: https://community.nearform.com/wfh-conf
MIT Introduction to Deep Learning 6.S191: Lecture 6 with Ava Soleimany.
Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!
- 0:00 – Introduction
- 0:58 – Course logistics
- 3:59 – Upcoming guest lectures
- 5:35 – Deep learning and expressivity of NNs
- 10:02 – Generalization of deep models
- 14:14 – Adversarial attacks
- 17:00 – Limitations summary
- 18:18 – Structure in deep learning
- 22:53 – Uncertainty & bayesian deep learning
- 28:09 – Deep evidential regression
- 33:08 – AutoML
- 36:43 – Conclusion
Two Minute Papers showcases the paper “Unsupervised Learning of Depth and Ego-Motion from Video” here in this YouTube post.
Ben Sparks explains (and codes) the so-called SIR Model being used to predict the spread of cornavirus (COVID-19) in this Numberphile video.
3Blue1Brown walks through the mathematics of epidemics through simulations.
The source code for this video is visible here:
Jon Wood shows us how to how to produce n-grams from text data in ML.NET.
N-Gram article – https://blog.xrds.acm.org/2017/10/introduction-n-grams-need/
Leila Gharani shows you how you can create MULTIPLE dependent data validation lists in Excel on the SAME sheet. In this video I’ll show you a solution that requires ONE single formula. This formula also excludes blank cells on the bottom of the data validation list.
Ryan Cunningham, the master of (all) Power Apps, has an in depth discussion on what is at the heart of solving customer problems and how Power Apps, together with all the technologies within the Power Platform, achieves this.
Ryan is the Product Lead for Power Apps and has been around since the beginning of the product. He has also been a champion in the community and at the center of it’s growth over these past three years, supporting and elevating those within the community to achieve more.
Learn more about Ryan here https://www.linkedin.com/in/rycu/
To learn more, visit: https://aka.ms/LessCodeMorePowerDocs