I started out wanting to learn AI Object Detection in Computer Vision... Now even though I have a masters degree in electronic engineering (M.Eng). It was still challenging for me to figure out. I had a lot of questions like... If Ubuntu, what version 16.04, 18.04, What kernel do I need? If I am training, what format does my dataset need to be in?

I remember beginning my first online course in machine learning and realising that I already knew most of it. I'm going to preface this article by saying that I'm a trained and journal published econometrician -- I'm biased. Do you know who is also biased? Joshua Angrist -- a 2021 Nobel Prize winner whose video I discovered saying the same thing while researching for this video. If you're reading this, I'm assuming you have some interest in data science- there's a lot you can learn from Econometrics, so buckle up and listen in.

The Future of ID in an AI World Artificial intelligence is rapidly changing the ways we live, work, and play. This post by Markus Bernhardt and Clark Quinn looks at how A.I. is evolving, and what it means to the future of instructional design. How I Create My Weekly Newsletter (and personal curation tips you should steal) Curation is a great skill for learning and development professionals to develop. This post by Mike Taylor – one of the best curators in the L&D space – explains his process for curating his weekly newsletter. It's a great post to understand curation in practice. Create a Google Cloud Account to Share Courses for Free While uploading elearning courses to a learning management system is common practice, there are times you need an alternative for hosting your projects (such as showcasing your personal portfolio). This post by Tom Kuhlmann explores how to use Google Cloud as a free alternative for sharing your courses. The Need for Diversity, Equity and Inclusion Training in the Hybrid Workplace Supporting diversity, equity, and inclusion is an important goal for L&D departments. This post examines what new challenges arise in supporting DEI efforts in an increasingly remote and hybrid work environment.

Algorithmic Trading A-Z with Python, Machine Learning & AWS - Build your own truly Data-driven Day Trading Bot Learn how to create, test, implement & automate unique Strategies.

Supervised machine learning is a technique that maps a series of inputs (X) to some known outputs (y) without being explicitly programmed. Training a machine learning model refers to the process where a machine learns a mapping between X and y. Once trained the model can be used to make predictions on new inputs where the output is unknown. The training of a machine learning model is only one element of the end to end machine learning lifecycle. For a model to be truly useful this mapping needs to be stored and deployed for use.

This post contains a list of the AI-related seminars that are scheduled to take place between 10 March 2022 and 30 April 2022. All events detailed here are free and open for anyone to attend virtually. Scaling Multilingual Machine Translation to Thousands of Language Directions Speaker: Shruti Bhosale Organised by: Stanford MLSys Join the email list to get notified of the speaker and livestream link each week. Brain Connectivity and Behaviour Speaker: Michel Thiebaut de Schotten (University of Bordeaux) Organised by: Cornell Machine Learning in Medicine Find out how to sign up here. Title to be confirmed Speaker: Emtiyaz Khan (Tokyo RIKEN) Organised by: UCL ELLIS Zoom link is here.

A pilot study of the use of artificial intelligence to detect the needs of students in remote learning has concluded that the data obtained can be used by teachers to offer help to the students who most need it. The research, carried out in conjunction with the Universitat Oberta de Catalunya (UOC), the Eurecat technological center and the Universidad Autónoma de Madrid, will help solve one of the biggest problems faced by off-site education, which has become more widespread during the pandemic: how to obtain information concerning the progress of students in order to provide them with the necessary support before it is too late. Laia Subirats, a course instructor at the UOC's Faculty of Computer Science, Multimedia and Telecommunications and a researcher at Eurecat, said: "We were able to carry out a continuous assessment in pre-pandemic years, then during lockdown and later in the second wave of the pandemic." She added: "Our objective is to develop a method to improve remote learning which will allow teachers to identify students who are at risk of failing, so that they, as well as the students themselves, can reinforce their learning process." The study, published in the open-access scientific journal Applied Sciences, drew on information gathered from 396 university students between the 2016/2017 and 2020/2021 academic years. Before the final exam, students were given the chance to take tests featuring various questions adapted to their individual level.

Understanding how important representation, role models, and mentoring had been to my own career journey, I started a network to support other Amazon employees looking to pursue a career in machine learning (ML) and artificial intelligence (AI). Open to anyone working at Amazon, the global Women in ML/AI group hosts regular networking events and organizes panel discussions with industry experts on career development. To discuss learnings from our professional journey, I sat down with fellow board members, including senior documentation manager Michelle Luna, senior software development manager Anna Khabibullina and general manager and product lead Shubha Pant. Here are some of the advice we found invaluable when launching and building a career in the field. Luna, Khabibullina, Pant and I are all proof that there are many paths into ML and AI -- from the traditional and linear, to the more unconventional.

Continual learning, also known as lifelong learning, is an emerging research topic that has been attracting increasing interest in the field of machine learning. With human activity recognition (HAR) playing a key role in enabling numerous real-world applications, an essential step towards the long-term deployment of such recognition systems is to extend the activity model to dynamically adapt to changes in people's everyday behavior. Current research in continual learning applied to HAR domain is still under-explored with researchers exploring existing methods developed for computer vision in HAR. Moreover, analysis has so far focused on task-incremental or class-incremental learning paradigms where task boundaries are known. This impedes the applicability of such methods for real-world systems since data is presented in a randomly streaming fashion. To push this field forward, we build on recent advances in the area of continual machine learning and design a lifelong adaptive learning framework using Prototypical Networks, LAPNet-HAR, that processes sensor-based data streams in a task-free data-incremental fashion and mitigates catastrophic forgetting using experience replay and continual prototype adaptation. Online learning is further facilitated using contrastive loss to enforce inter-class separation. LAPNet-HAR is evaluated on 5 publicly available activity datasets in terms of the framework's ability to acquire new information while preserving previous knowledge. Our extensive empirical results demonstrate the effectiveness of LAPNet-HAR in task-free continual learning and uncover useful insights for future challenges.

Solve real data science problems and add value quickly without needing to learn how to code. Do you want to super charge your career by learning the most in demand skills? Are you interested in data science but intimidated from learning by the need to learn a programming language? I can teach you how to solve real data science business problems that clients have paid hundreds of thousands of dollars to solve. I'm not going to turn you into a data scientist; no 2 hour, or even 40 hour online course is able to do that.