A new school season is upon us – cue the rolling eyes, students – and so you might be in the market for a new computer. Whether you're back in the classroom or continuing to learn online, it's simply the most important piece of tech to help you stay on your game. Problem is, how do you decide what kind of computer is for you? Not only are there varying prices, competing operating systems and countless brands to choose from, but the student – or the parent(s) footing the bill – must decide on an ideal form factor (or type of computer), such as a laptop, desktop, 2-in-1 or all-in-one. And you might think you need a degree in computer science just to understand today's specifications ("specs").

Kids can make wonders; many surveys and stats prove that the age from 5-15 is the best time for anyone in their entire lifetime to store as much data into their brain as possible. But the very idea of introducing them to such complex topics like AI and ML can be extremely terrifying. But worry not, we are here to help you take your kids into the world of ML. Machine learning is definitely not as easy as teaching rhymes but we can make it fun. "Rome wasn't built in a day" and hence, make sure you don't dig deep into the topics like robots or automation.

Deep Learning is a subset of machine learning that encompasses neural networks that can learn from raw or unstructured data, much like humans. It's used for speech recognition, machine translation, computer vision and natural language processing. Deep Learning is at the centre of exciting innovation possibilities like Self Driven Cars, Image recognition, virtual assistants, speech recognition, machine translation, computer vision and natural language processing. Deep learning models are transforming businesses by providing learning techniques and real-world solutions based on large data sets. It is good to have skills for today's machine learning job market.

I finished my Business degree on 2014, I decided to study the A.I.. I started machine learning and deep learning in 2014. I got it myself in my school journal. Anyway, I was doing my best to study whatever it looks cool. Because it was only two years and it is not good enough to achieve the good knowledge.

As the IT (Information Technology) and AV (Audio Visual) industries further develop their usage of artificial intelligence (AI), there is going to be an incredible amount of change that goes with it. AI has already transformed how we use computers, but has lasting impacts on the future of several industries. This is especially true for sectors that rely heavily on technology. Something to consider is how AI is affecting these two industries. Information Technology, for example, seems to be more focused towards commercial clients, but AV tends to trend more towards residential clients (although there are plenty of business needs as well).

I met the most rational person I know during my freshman year of college. Greg (not his real name) had a tech-support job in the same computer lab where I worked, and we became friends. I planned to be a creative-writing major; Greg told me that he was deciding between physics and economics. He'd choose physics if he was smart enough, and economics if he wasn't--he thought he'd know within a few months, based on his grades. We roomed together, and often had differences of opinion.

Advancements in artificial intelligence have paved the way for researchers and scientists to train machines to interpret images, sounds, and patterns in videos. Deep learning algorithms have given a new meaning to artistic explorations, which have stunned the world. AI is gradually advancing to become the next medium for art, and several artists worldwide relate to this seamless development. In a world that is evolving amid codes and hi-tech machines, Generative Adversarial Networks (GANs) are a specific class of algorithms being used to generate AI artworks. From pitch recognition to natural image synthesis and generating human faces that never existed-- machines are likely to match human senses.

We study the performance of federated learning algorithms and their variants in an asymptotic framework. Our starting point is the formulation of federated learning as a multi-criterion objective, where the goal is to minimize each client's loss using information from all of the clients. We propose a linear regression model, where, for a given client, we theoretically compare the performance of various algorithms in the high-dimensional asymptotic limit. This asymptotic multi-criterion approach naturally models the high-dimensional, many-device nature of federated learning and suggests that personalization is central to federated learning. Our theory suggests that Fine-tuned Federated Averaging (FTFA), i.e., Federated Averaging followed by local training, and the ridge regularized variant Ridge-tuned Federated Averaging (RTFA) are competitive with more sophisticated meta-learning and proximal-regularized approaches. In addition to being conceptually simpler, FTFA and RTFA are computationally more efficient than its competitors. We corroborate our theoretical claims with extensive experiments on federated versions of the EMNIST, CIFAR-100, Shakespeare, and Stack Overflow datasets.

Computerized adaptive testing (CAT) refers to a form of tests that are personalized to every student/test taker. CAT methods adaptively select the next most informative question/item for each student given their responses to previous questions, effectively reducing test length. Existing CAT methods use item response theory (IRT) models to relate student ability to their responses to questions and static question selection algorithms designed to reduce the ability estimation error as quickly as possible; therefore, these algorithms cannot improve by learning from large-scale student response data. In this paper, we propose BOBCAT, a Bilevel Optimization-Based framework for CAT to directly learn a data-driven question selection algorithm from training data. BOBCAT is agnostic to the underlying student response model and is computationally efficient during the adaptive testing process. Through extensive experiments on five real-world student response datasets, we show that BOBCAT outperforms existing CAT methods (sometimes significantly) at reducing test length.

Real-world recommender system needs to be regularly retrained to keep with the new data. In this work, we consider how to efficiently retrain graph convolution network (GCN) based recommender models, which are state-of-the-art techniques for collaborative recommendation. To pursue high efficiency, we set the target as using only new data for model updating, meanwhile not sacrificing the recommendation accuracy compared with full model retraining. This is non-trivial to achieve, since the interaction data participates in both the graph structure for model construction and the loss function for model learning, whereas the old graph structure is not allowed to use in model updating. Towards the goal, we propose a \textit{Causal Incremental Graph Convolution} approach, which consists of two new operators named \textit{Incremental Graph Convolution} (IGC) and \textit{Colliding Effect Distillation} (CED) to estimate the output of full graph convolution. In particular, we devise simple and effective modules for IGC to ingeniously combine the old representations and the incremental graph and effectively fuse the long-term and short-term preference signals. CED aims to avoid the out-of-date issue of inactive nodes that are not in the incremental graph, which connects the new data with inactive nodes through causal inference. In particular, CED estimates the causal effect of new data on the representation of inactive nodes through the control of their collider. Extensive experiments on three real-world datasets demonstrate both accuracy gains and significant speed-ups over the existing retraining mechanism.