It is undoubtedly a given that Artificial Intelligence (AI) is the next evolutionary step and the future of businesses. However, many do not realize that the future is already here or much closer than foreseen. AI incorporates machine learning as well as decision making abilities that were once exclusive for human minds only. However, now technology has advanced to the point that even computers can do and think the unthinkable with the help of a group of algorithms that can automate repetitive tasks and produce usable output data. A company has many projects from software to logistics to finances and every one of them require planning, managing as well as monitoring.

Minecraft has sold 176 million copies across all platforms, making it the best-selling video game of all time. I first learned of it in 2013, when its popularity was peaking, just before it was acquired by Microsoft when it bought the indie studio Mojang for $2.5 billion. While surviving against monsters, starvation, other players, and environmental conditions can be part of the game, the main object is to build things. Players extract resources from the environment and combine them to make construction materials, armor, weapons, and more. All the nerdiest boys in my seventh-grade class were obsessed with it; they all wore T-shirts and backpacks with the game's pixelated characters, and I would overhear them making plans to meet up in the game after class.

Recurrent neural networks (RNNs) have gained a great deal of attention in solving sequential learning problems. The learning of long-term dependencies, however, remains challenging due to the problem of a vanishing or exploding hidden states gradient. By exploring further the recently established connections between RNNs and dynamical systems we propose a novel RNN architecture, which we call a Hamiltonian recurrent neural network (Hamiltonian RNN), based on a symplectic discretization of an appropriately chosen Hamiltonian system. The key benefit of this approach is that the corresponding RNN inherits the favorable long time properties of the Hamiltonian system, which in turn allows us to control the hidden states gradient with a hyperparameter of the Hamiltonian RNN architecture. This enables us to handle sequential learning problems with arbitrary sequence lengths, since for a range of values of this hyperparameter the gradient neither vanishes nor explodes. Additionally, we provide a heuristic for the optimal choice of the hyperparameter, which we use in our numerical simulations to illustrate that the Hamiltonian RNN is able to outperform other state-of-the-art RNNs without the need of computationally intensive hyperparameter optimization.

We consider online learning problems where the aim is to achieve regret which is efficient in the sense that it is the same order as the lowest regret amongst K experts. This is a substantially stronger requirement that achieving $O(\sqrt{n})$ or $O(\log n)$ regret with respect to the best expert and standard algorithms are insufficient, even in easy cases where the regrets of the available actions are very different from one another. We show that a particular lazy form of the online subgradient algorithm can be used to achieve minimal regret in a number of "easy" regimes while retaining an $O(\sqrt{n})$ worst-case regret guarantee. We also show that for certain classes of problem minimal regret strategies exist for some of the remaining "hard" regimes.

We present a simple self-training method that achieves 87.4% top-1 accuracy on ImageNet, which is 1.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. On robustness test sets, it improves ImageNet-A top-1 accuracy from 16.6% to 74.2%, reduces ImageNet-C mean corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from 27.8 to 16.1. To achieve this result, we first train an EfficientNet model on labeled ImageNet images and use it as a teacher to generate pseudo labels on 300M unlabeled images. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We iterate this process by putting back the student as the teacher. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as good as possible. But during the learning of the student, we inject noise such as data augmentation, dropout, stochastic depth to the student so that the noised student is forced to learn harder from the pseudo labels.

Time series modeling has attracted extensive research efforts; however, achieving both reliable efficiency and interpretability from a unified model still remains a challenging problem. Among the literature, shapelets offer interpretable and explanatory insights in the classification tasks, while most existing works ignore the differing representative power at different time slices, as well as (more importantly) the evolution pattern of shapelets. In this paper, we propose to extract time-aware shapelets by designing a two-level timing factor. Moreover, we define and construct the shapelet evolution graph, which captures how shapelets evolve over time and can be incorporated into the time series embeddings by graph embedding algorithms. To validate whether the representations obtained in this way can be applied effectively in various scenarios, we conduct experiments based on three public time series datasets, and two real-world datasets from different domains. Experimental results clearly show the improvements achieved by our approach compared with 17 state-of-the-art baselines.

The Elements of AI is a series of free online courses created by Reaktor and the University of Helsinki. We want to encourage as broad a group of people as possible to learn what AI is, what can (and can't) be done with AI, and how to start creating AI methods. The courses combine theory with practical exercises and can be completed at your own pace.

Learning new skills to enhance your abilities to do a task effectively can be a hectic schedule especially if you are an employee. It's hard to chase coaching or learning centers after spending 8-10 hours in the office per day. And when it comes to becoming technology-efficient specifically in the field of data science, you need to have the best qualification, handy experiences to get better job opportunities in this high in-demand profession. To ease out people's hectic schedules without compromising with the quality of the education, online platforms like Coursera, Udemy, eDX and many more have a collection of data science certification and courses. Adding a touch of extra bonanza, these courses are free of cost.

WHAT YOU WILL LEARN Use PyCharm and run Python files and programs on the interface Understand and use machine learning and neural networks with core concepts and examples Learn to use the Keras API and Syntax Explore the CIFAR-10 image dataset The Deep Learning Masterclass: Make a Keras Image Classifier Welcome to this epic masterclass on Keras (and so much more) with our #1 data scientist and app developer Nimish Narang, creator of over 20 Mammoth Interactive courses and a top-seller on Eduonix This course was funded by a wildly successful Kickstarter Anyone can take this course. If you already have experience using PyCharm and running Python files and programs on the interface, you can simply skip ahead to whatever section best suits your needs. Or, you can follow the progression of this meticulously curated course especially designed to take any absolute beginner off the street and make them a data modeler. This course is divided into days, but of course you can learn at your own pace. In Day 2 we teach you all the fundamentals of the Python programming language.

To improve teaching abilities of teachers in engineering colleges, AICTE is ready to set up AICTE Training and Learning (ATAL) academies in 11 more States. AICTE Chairman Anil D Sahasrabudhe said the council had set up four academies on its own, and 11 States, including Andhra Pradesh and Telangana, came forward to have them. The council would set up them if the respective State governments provided infrastructure. The four academies were started by AICTE at Jaipur, Baroda, Thiruvananthapuram and Guwahati. The idea is to improve the knowledge of teachers who had started their career long time back so that they can, in turn, teach students.