The whole backdrop of Artificial intelligence and deep learning is to imitate the human brain, and one of the most notable feature of our brain is it's inherent ability to transfer knowledge across tasks. Which in simple terms means using what you have learnt in kindergarten, adding 2 numbers, to solving matrix addition in high school mathematics. The field of machine learning also makes use of such a concept where a well trained model trained with lots and lots of data can add to the accuracy of our model. Here is my code for the transfer learning project I have implemented. I have made use of open cv to capture real time images of the face and use them as training and test datasets.

Each Fourth of July for the past five years I've written about AI with the potential to positively impact democratic societies. I return to this question with the hope of shining a light on technology that can strengthen communities, protect privacy and freedoms, or otherwise support the public good. This series is grounded in the principle that artificial intelligence can is capable of not just value extraction, but individual and societal empowerment. While AI solutions often propagate bias, they can also be used to detect that bias. As Dr. Safiya Noble has pointed out, artificial intelligence is one of the critical human rights issues of our lifetimes.

My aim, as always, was to keep the projects as diverse as possible so you can pick the ones that fit into your data science journey. If you're a beginner, I would suggest starting with the PalmerPenguins dataset as most folks aren't even aware of it right now. A great chance to get a head start. I would love to hear your thoughts on which open source project you found the most useful. Or let me know if you want me to feature any other data science projects here or in next month's edition.

The coronavirus lockdown and the school closures that have resulted have had a huge impact on the education of 1.2 billion children across 186 countries. Teachers have been scrambling to keep in touch with their classes, while parents have been trying to keep bored children engaged in learning while cut off from school and fellow students. As a result, virtual classrooms, language apps, online tutoring, and online education software (and new hardware) have seen a surge popularity, with some reports suggesting the market could hit $350 billion by 2025. But can the digital revolution in education, long been promised but rarely achieved, take a step forward as a result of these changes? Speaking at the CogX 2020 conference, Rose Luckin, professor of Learner Centered Design at University College London's Knowledge Lab, argued that the only way for the industry to evolve was to build on what it has learnt recently.

As any data scientist is probably familiar, there exists a vast world of predictive modelling competitions on the internet; the most well-known competitions likely being those from Kaggle. Some of these competitions are incentivized financially, others just by curiosity. The basic structure for nearly all of the financially incentivized competitions is that all participants (or teams of participants) train and develop models on a training dataset and send in their submissions as predictions on a test dataset of which the labels are hidden from the competitors. Subsequently, the'best' models are awarded their respective chunk of the financial reward based on some predetermined accuracy metric. Most opinions regarding these predictive modelling competitions are that competition based on financial incentive creates demand for brilliant people to discover solutions to hard problems.

Watching this playlist is an outstanding start to learn the fundamental concepts of deep learning and artificial neural networks. The lectures are deep dive into deep learning models for image classification. The lectures also explain training deep learning models. This specialization contains 5 courses to understand deep learning foundations and apply them (you can audit the courses for free). Deep learning is getting attention from the researchers.

Among the other questions being asked as a result of the current pandemic is, "What will the rise of artificial intelligence mean for K-12 education?" It would seem safe to assume that the rush to online learning and the adoption of new technologies will inevitably lead educators to embrace tools powered by artificial intelligence. But according to Robert F. Murphy, that more optimistic vision for AI will probably be tempered for now by budget shortfalls that "may seriously delay" school districts from making those types of investments anytime soon. Murphy is an independent education consultant with over two decades of research experience, including as a senior policy researcher for the international think tank RAND Corporation and as the director for evaluation research at SRI International, a scientific research center. In a paper authored last year for RAND, Murphy addressed the more fundamental issues of AI that need to be considered, regarding its further adoption.

The term artificial intelligence (AI) was coined 64 years ago at a scholarly conference. The AI field hasn't remained the theoretical province of computer scientists and mathematicians; it now is a pervasive part of everyday life. With a technology this powerful, it is critical to include the perspectives of all women, including those from underrepresented communities. AI applications -- based on algorithms -- are found in robotics, machine learning, natural language processing, machine vision, speech recognition and more. These applications are found in homes, vehicles and myriad other aspects of daily life.

Amid a growing backlash over AI's racial and gender biases, numerous tech giants are launching their own ethics initiatives -- of dubious intent. The schemes are billed as altruistic efforts to make tech serve humanity. But critics argue their main concern is evading regulation and scrutiny through "ethics washing." At least we can rely on universities to teach the next generation of computer scientists to make. Only 15% of instructors and professors said they're teaching AI ethics, and just 18% of students indicated they're learning about the subject.

In 2017, I built this deep learning machine as a solution to my growing AWS bill. In 2020, I struggled to get this machine to run Kubeflow locally. Here are the instructions that worked for me. Some of these instructions are based on this Ubuntu.com MicroK8s is a small version of Kubernetes that will work well locally.