Python is increasingly being used as a scientific language. Matrix and vector manipulations are extremely important for scientific computations. Both NumPy and Pandas have emerged to be essential libraries for any scientific computation in python due to their intuitive syntax and high-performance matrix computation capabilities. In this post, we will provide an overview of the common functionalities of NumPy and Pandas. This similarity and added flexibility have resulted in wide acceptance of python in the scientific community lately. This post is an excerpt from a live hands-on training conducted by CloudxLab on 25th Nov 2017.

Pablo Gervás's talk, How Creative Can Reuse Be? pointed up CBR as a favored The main conference program comprised 31 contributions between presentations and posters from 144 authors on technical and applied CBR papers. The origins of the Conference on Case-Based Reasoning The accepted papers were of very high quality, and date from the first European workshop on provided many new insights across a wide range of CBR (EWCBR) held in Kaiserslautern, Germany, in CBR issues. Topics in recent CBR research included in 1993. Since then many European and international the presentations and discussions at ICCBR 2016 conferences on CBR have been held in different parts included novel approaches to similarity and retrieval; of the world. The European conference on CBR advances in adaptation strategies; case generation; representation and knowledge discovery; CBR as a (ECCBR) and the International Conference on CBR cognitive approach to big data; AI with large-scale (ICCBR) were held in alternating years.

It is becoming very hard to stay up to date with recent advancements happening in deep learning. Hardly a day goes by without a new innovation or a new application of deep learning coming by. To keep ourselves updated, we have created a small reading group to share our learnings internally at Analytics Vidhya. One such learning I would like to share with the community is a a survey of advanced architectures which have been developed by the research community. This article contains some of the recent advancements in Deep Learning along with codes for implementation in keras library.

Semi-supervised machine learning is getting ready for primetime. In this article we review a number of common semi-supervised algorithms, capped by a presentation of our own Mean Teacher [arxiv, github], presented at NIPS 2017. Deep learning models have delivered superhuman performance for many years. However, training with standard supervised techniques requires huge amounts of correctly labeled data. Being able to use unlabeled data would open doors to many new applications in e.g.

Cognitive technologies are increasingly being used to solve business problems; indeed, many executives believe that AI will substantially transform their companies within three years. But many of the most ambitious AI projects encounter setbacks or fail. A survey of 250 executives familiar with their companies' use of cognitive technology and a study of 152 projects show that companies do better by taking an incremental rather than a transformative approach to developing and implementing AI, and by focusing on augmenting rather than replacing human capabilities. Broadly speaking, AI can support three important business needs: automating business processes (typically back-office administrative and financial activities), gaining insight through data analysis, and engaging with customers and employees. To get the most out of AI, firms must understand which technologies perform what types of tasks, create a prioritized portfolio of projects based on business needs, and develop plans to scale up across the company.

SingularityNET, is an ambitions project to create a decentralized marketplace for AI, has raised a lot of money in its token sale. In around 60 seconds after opening the sale to the public, it sold out of the whole amount of available tokens (the AGI token), bringing the total to $36 million. However, a startup raising a lot of money in a token sale is not really of interest to me. This is part and parcel of this crazy unregulated, crypto word these days. But was IS interesting to me is what SingularityNET actually is.

If there is anything that chip giant Intel has learned over the past two decades as it has gradually climbed to dominance in processing in the datacenter, it is ironically that one size most definitely does not fit all. As the tight co-design of hardware and software continues in all parts of the IT industry, we can expect fine-grained customization for very precise – and lucrative – workloads, like data analytics and machine learning, just to name two of the hottest areas today. Software will run most efficiently on hardware that is tuned for it, although we are used to thinking of that process in a mirror image, where programmers tweak their code to take advantage of the forward-looking features a chip maker conceives of four or five years before they are etched into its transistors and delivered as a product. The competition is fierce these days, and Intel has to move fast if it is to keep its compute hegemony in the datacenter. That is why at the Intel Developer Forum in San Francisco the company put a new path on the Knights family of many-core processors that will see the company deliver a version of this chip specifically tuned for machine learning workloads.

This chapter is based on lectures from the 2016 Park City Mathematics Institute summer school on The Mathematics of Data, and it appears as a chapter [1] in the edited volume of lectures from that summer school.

New details regarding Apple's efforts in autonomous car technology were revealed in a patent published this week, spotted by Autoblog. The patent, called "Autonomous Navigation System," was filed by Apple in 2015, about a year after the company reportedly started working on self-driving technology. The paperwork filed with the U.S. Patent and Trademark Office details a navigation system with sensors installed in the vehicle that provides "updates to a virtual characterization" of a route drivers have traveled on. The patent also mentions a " database of characterizations," where information on traveled roads can be stored in. "Some embodiments provide an autonomous navigation system which enables autonomous navigation of a vehicle along one or more portions of a driving route based on monitoring, at the vehicle, various features of the route as the vehicle is manually navigated along the route to develop a characterization of the route."

In recent years, many tech giants (Google, Microsoft Azure, IBM) invested heavily in the general-use of Machine Learning and Deep Learning. In 2018, more SME businesses will learn how to use their solutions and full service platforms. They have managed to optimize Computer Vision and Natural Language Processing in such a way that it will most likely outperform any other (smaller) player in this field. With help of API's they will take over (market share up to 85%) the general-use machine learning industry in 2018. In 2017 there has been an exponential use of so called'click – drag and drop' tools.