In 2015, Elon Musk guessed that the industry should expect fully autonomous vehicles by 2018--but that never happened. In 2014, Nissan promised multiple, commercially viable driverless vehicles on the market by 2020. While the COVID-19 pandemic did not help the situation, this is another unmet promise. Why do auto manufacturers have to keep moving the goalposts on driverless vehicles? According to a research paper recently published in Nature Communications by the Center for Connected and Automated Transportation (CCAT), one of the obstacles that has hindered the development of autonomous vehicles comes down to a severe inefficiency in the way autonomous vehicle testing and evaluation is performed.

Many times AI has been put on a pedestal as the future of x y & z, however, many seem to agree that education is a sector in particular which will see stark changes in both admin, teaching styles, personalisation and more. I had the pleasure of speaking to three individuals working in the field, including, Vinod Bakthavachalam, Senior Data Scientist at Coursera, Kian Katanforoosh, Lecturer at Stanford University & Sergey Karayev, Co-Founder and CTO of Gradescope. We began by having Sergey of Gradescope walk us through his product, which has been recently acquired by turnitin. The concept, it seemed was formed from the simple and widespread issue of both lack of consistency, lack of insight through time constraint and delayed feedback on academic work. Sergey found that scanning the papers onto an online interface when paired with a rubric can allow for accurate marking in seconds across several papers.

Although nothing really changes but the date, a new year fills everyone with the hope of starting things afresh. If you add in a bit of planning, some well-envisioned goals, and a learning roadmap, you'll have a great recipe for a year full of growth. This post intends to strengthen your plan by providing you with a learning framework, resources, and project ideas to help you build a solid portfolio of work showcasing expertise in data science. Just a note: I've prepared this roadmap based on my personal experience in data science. This is not the be-all and end-all learning plan.

Hong Kong (CNN Business)Before the pandemic, Ka Tim Chu, teacher and vice principal of Hong Kong's True Light College, looked at his students' faces to gauge how they were responding to classwork. Now, with most of his lessons online, technology is helping Chu to read the room. An AI-powered learning platform monitors his students' emotions as they study at home. The software, 4 Little Trees, was created by Hong Kong-based startup Find Solution AI. While the use of emotion recognition AI in schools and other settings has caused concern, founder Viola Lam says it can make the virtual classroom as good as -- or better than -- the real thing.

There are a growing number of works out there addressing how to approach machine learning problems, many of them quite good. But how many of them are written by a 4x Kaggle Grandmaster? Abhishek Thakur, the 4x Grandmaster in question -- who now works on NLP at Hugging Face -- wrote and released his book Approaching (Almost) Any Machine Learning Problem (AAAMLP) last year. The book can be purchased through Amazon for a very reasonable price, much more so than most other books of similar content. Additionally, however, Abhishek has recently released the entirety of the book online for free, available in PDF via its Github repo.

If you are inclined to make AI/DataScience your career, look no further. A clear-cut go-to path for the job that you aspire for. How to read a job description? How and where do you fit in? What are the courses (free / paid) that you should undertake to be successful.

This course will guide you through how to use Google's latest TensorFlow 2 framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow 2 framework in a way that is easy to understand. We'll focus on understanding the latest updates to TensorFlow and leveraging the Keras API (TensorFlow 2.0's official API) to quickly and easily build models. In this course we will build models to forecast future price homes, classify medical images, predict future sales data, generate complete new text artificially and much more! This course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes.

Both Data Science and machine learning are very inter-related. It's hard to distinguish them at least at the Masters level. So, don't bother to differentiate them. Between data science and data analytics, it all depends on your existing skills and learning objectives. The course pattern, structure, syllabus everything is quite similar and is interrelated.

This article introduces the concept of Manifold Learning. A large number of machine learning datasets involve thousands and sometimes millions of features. These features can make training very slow. In addition, there is plenty of space in high dimensions making the high-dimensional datasets very sparse, as most of the training instances are quite likely to be far from each other. This increases the risk of overfitting since the predictions will be based on much larger extrapolations as compared to those on low-dimensional data.

In order to do machine learning engineering, a model must first be deployed, in most cases as a prediction API. In order to make this API work in production, model serving infrastructure must first be built. This includes load balancing, scaling, monitoring, updating, and much more. At first glance, all of this work seems familiar. Web developers and DevOps engineers have been automating microservice infrastructure for years now.