IntelliVision, a pioneer and leader in AI/Deep Learning video analytics software for smart cameras, announced today that it has been named the 2017 Entrepreneurial Company of the Year for Security Intelligence and Video Analytics by leading analyst firm Frost & Sullivan. "Video analytics and intelligence functions will remain the most in-demand security technology segment for customers looking to modernize their security operations and increase overall efficiency," said Danielle VanZandt, security industry analyst with Frost & Sullivan. "IntelliVision's advanced analytics and intelligence technologies, coupled with its ability to deploy inside the camera, on-premise servers, or on the cloud put it well ahead of its competition in this field." "We are honored to receive this award from Frost & Sullivan," said Vaidhi Nathan, IntelliVision's CEO. "It is a vindication of our many years of research, development and customer service in the growing field of AI-based video analytics for smart cameras."
An excellent lineup of financial practitioners and academics presented research and insights on big data and machine learning in finance. Access a copy of the presentations and watch all available sessions: https://goo.gl/5pAMqx RavenPack's prestigious annual event has experienced growing interest, with attendance exceeding 260 buy-side professionals. Word on the street is RavenPack's research symposium is a "must attend event" for quantitative investors and financial professionals that are serious about Big Data.
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Pandas is hands down one of the best libraries of python. It supports reading and writing excel spreadsheets, CVS's and a whole lot of manipulation. It is more like a mandatory library you need to know if you're dealing with datasets from excel files and CSV files. This is part one of Pandas tutorial. I'm not going to cover everything possible with pandas, however, I want to give you a taste of what it is and how you can get started with it.
If you're looking for AI-first companies based out of India to work or collaborate with, you've come to the right place. We've collated all the top-funded startups from the country that are harnessing AI in their respective verticals from enterprise to consumer-focused startups, from robotics to computer vision, from speech to predictive analytics, and other sectors. AI is a horizontal technology that's being harnessed across sectors -- which we've tried to map below, with data from startup tracker Tracxn and LinkedIn. Do note that there will be some overlap in the categorisation below. Another disclaimer: this is not a comprehensive list of every AI startup that exists in the country.
Numpy is a math library for python. It enables us to do computation efficiently and effectively. It is better than regular python because of it's amazing capabilities. In this article I'm just going to introduce you to the basics of what is mostly required for machine learning and datascience. I'm not going to cover everything that's possible with numpy library.
After a few applied machine learning problems, you usually develop a pattern or process for quickly getting started and achieving good results. Once you have this process it is trivial to use it again and again on project after project. The more developed your process, the faster you can get to results! Let me give you a head start and teach you a 5-step systematic process that I developed while becoming a machine learning engineer. This step is all about learning more about the problem at hand.
It's not a secret anymore that machine learning is changing the world from face recognition on Facebook and Snapchat all the way to medicine and healthcare. Big data as we used to know got a whole different size if back in 2005 we thought that 1GB a day is big data today we have to handle much bigger numbers. The issue is not with storage as it with the complex queries we need execute in a very short time. Back in 2005 waiting a couple of seconds to page to load was fine, but not anymore, today we're expected to a serve a page in milliseconds and also try to recommend you the right product, song or ad all this need to happen faster with bigger data collection. Like any other job you'll need your tools and as a data scientist you'll need to install the right languages and IDE, the most common language is R (you can use any other language but R offer a wide variety of tools when handling with data).
Telstra has used open source machine learning technology to answer the age-old question that plagues every marketer: how effective is my ad spend? The telco wields one of the biggest marketing budgets in Australia, but that doesn't stop Telstra from wanting to track the performance of every dollar spent. The company previously faced a six-month lag to get visibility into the effectiveness of its marketing spend; that is now down to five weeks using new marketing mix modelling developed in partnership with Accenture, Deakin University and Servian. The telco previously used a traditional econometric model to assess the performance of its marketing spend, pulling together 800 variables – which took two-and-a-half months to assemble – and then modelling this using regression techniques. "Six months after the marketing period had ended I could tell the CMO [chief marketing officer] and the marketers how effective their marketing was... six months ago," Telstra's director of research, insights & analytics Liz Moore told the recent Big Data & Analytics Innovation Summit in Sydney.
Starting with the Google DeepMind paper, there has been a lot of new attention around training models to play video games. You, the data scientist/engineer/enthusiast, may not work in reinforcement learning but probably are interested in teaching neural networks to play video games. With that in mind, here's a list of nuances that should jumpstart your own implementation. The lessons below were gleaned from working on my own implementation of the Nature paper. The lessons are aimed at people who work with data but may run into some issues with some of the non-standard approaches used in the reinforcement learning community when compared with typical supervised learning use cases.