Sometimes, vocations and avocations need a champion, and students in Massachusetts looking to further their knowledge of science, technology and robotics have one in state Rep. Danillo Sena. A House member representing the 37th Middlesex District, Sena filed a bill on Feb. 4 titled "An Act establishing an elementary and secondary school robotics grant program," meant to create a grant program that provides public and charter schools the necessary funding to increase robotics and STEM participation during and after school. STEM stands for science, technology, engineering and mathematics, a branch of education designed to help students to become better problem-solvers. "Money should not be a barrier between students and access to fun and engaging STEM education programs that foster creativity and have lasting positive effects on student achievement like these robotics teams," the Acton Democrat stated in a release. The bill was created in collaboration with Olivia Oestreicher, a member of Team 4905 Andromeda One Robotics at Ayer Shirley Regional High School and a Rep. Sena intern.
Machine learning has now entered its business heyday. Almost half of CIOs were predicted to have implemented AI by 2020, a number that is expected to grow significantly in the next five years. Because creating a machine learning model and putting it into operation in an enterprise environment are two very different things. The biggest challenge for companies looking to use AI is operationalizing machine learning, the same way DevOps operationalized software development in the 2000's. Simplifying the data science workflow by providing necessary architecture and automating feature serving with feature stores are two of the most important ways to make machine learning easy, accurate, and fast at scale.
A couple of days ago I started thinking if I had to start learning machine learning and data science all over again where would I start? The funny thing was that the path that I imagined was completely different from that one that I actually did when I was starting. I'm aware that we all learn in different ways. Some prefer videos, others are ok with just books and a lot of people need to pay for a course to feel more pressure. And that's ok, the important thing is to learn and enjoy it. So, talking from my own perspective and knowing how I learn better I designed this path if I had to start learning Data Science again.
Currency notes have identifiers that allow the visually impaired to identify them easily. This is a learned skill. On the other hand, classifying them using images is an easier solution to help the visually impaired identify the currency they are dealing with. Here, we use pictures of different versions of the currency notes taken from different angles, with different backgrounds and covering different proportions. The dataset contains 195 images of 7 categories of Indian Currency Notes -- Tennote, Fiftynote, Twentynote, 2Thousandnote, 2Hundrednote, Hundrednote, 1Hundrednote.
I trained a classifier on images of animals and gave it an image of myself, it's 98% confident I'm a dog. This is an exploration of a possible Bayesian fix. I trained a multi-class classifier on images of cats, dogs and wild animals and passed an image of myself, it's 98% confident I'm a dog. The problem isn't that I passed an inappropriate image because models in the real world are passed all sorts of garbage. It's that the model is overconfident about an image far away from the training data.
Artificial intelligence researchers at Facebook claim they have developed software that can predict the likelihood of a Covid patient deteriorating or needing oxygen based on their chest X-rays. Facebook, which worked with academics at NYU Langone Health's predictive analytics unit and department of radiology on the research, says that the software could help doctors avoid sending at-risk patients home too early, while also helping hospitals plan for oxygen demand. The 10 researchers involved in the study -- five from Facebook AI Research and five from the NYU School of Medicine -- said they have developed three machine-learning "models" in total, that are all slightly different. One tries to predict patient deterioration based on a single chest X-ray, another does the same with a sequence of X-rays, and a third uses a single X-ray to predict how much supplemental oxygen (if any) a patient might need. "Our model using sequential chest X-rays can predict up to four days (96 hours) in advance if a patient may need more intensive care solutions, generally outperforming predictions by human experts," the authors said in a blog post published Friday.
If you're a programmer, you want to explore deep learning, and need a platform to help you do it – this tutorial is exactly for you. Google Colab is a great platform for deep learning enthusiasts, and it can also be used to test basic machine learning models, gain experience, and develop an intuition about deep learning aspects such as hyperparameter tuning, preprocessing data, model complexity, overfitting and more. Colaboratory by Google (Google Colab in short) is a Jupyter notebook based runtime environment which allows you to run code entirely on the cloud. This is necessary because it means that you can train large scale ML and DL models even if you don't have access to a powerful machine or a high speed internet access. Google Colab supports both GPU and TPU instances, which makes it a perfect tool for deep learning and data analytics enthusiasts because of computational limitations on local machines.
USING A computer used to mean bashing away at a keyboard. Then it meant tapping on a touchscreen. Increasingly, it means simply speaking. Over 100m devices powered by Alexa, Amazon's voice assistant, rest on the world's shelves. Apple's offering, Siri, processes 25bn requests a month. By 2025 the market for such technology could be worth more than $27bn.