In statistical modelling the biggest threat is concept drift which makes the model gradually showing deteriorating performance over time. There are state of the art methodologies to detect the impact of concept drift, however general strategy considered to overcome the issue in performance is to rebuild or re-calibrate the model periodically as the variable patterns for the model changes significantly due to market change or consumer behavior change etc. Quantitative research is the most widely spread application of data science in Marketing or financial domain where applicability of state of the art reinforcement learning for auto-learning is less explored paradigm. Reinforcement learning is heavily dependent on having a simulated environment which is majorly available for gaming or online systems, to learn from the live feedback. However, there are some research happened on the area of online advertisement, pricing etc where due to the nature of the online learning environment scope of reinforcement learning is explored. Our proposed solution is a reinforcement learning based, true self-learning algorithm which can adapt to the data change or concept drift and auto learn and self-calibrate for the new patterns of the data solving the problem of concept drift. Index Terms-- Reinforcement learning, Genetic Algorithm, Q-learning, Classification modelling, CMA-ES, NES, Multi objective optimization, Concept drift, Population stability index, Incremental learning, F1-measure, Predictive Modelling, Self-learning, MCTS, AlphaGo, AlphaZero 1. Introduction Concept drift is well known challenge for sustainability of any machine learning predictive model over time. Machine learning offers diverse techniques to understand the underlying pattern of the data and associate the same with prediction objective. Any predictive modelling activity in either Marketing, Finance, Management are heavily dependent on the assumption that the training data represents the pattern of target population under specific study such as Fraud Identification, Customer churn prediction, Marketing mix modelling, Target customer identification for specific type of promotion etc. However due to social & economic development, customer behavior changes combined with other external factors making past learned pattern, irrelevant for current predictions.

Deep learning is a subset of machine learning, and chances are you've already used it whether you know it or not. While you most likely have heard the terms deep learning, machine learning, and AI, you might not be as familiar with real-world deep learning solutions. As you might suspect, the terms are interrelated. If it isn't already, deep learning should be on your radar. It is a subset of machine learning, and both fall under the umbrella of artificial intelligence, which refers to systems built to carry out tasks that normally require human input.

Deep learning process for the NLP market confirms that increasing applicability in customer-centric organizations is one of the key factors that can positively impact market growth. In-depth study covering high data volume, high computing performance, improved data storage and efficient recognition of various aspects, especially in speech recognition and pattern recognition. Organizations are implementing this process to improve their product portfolio. This in-depth learning improves some of the NLP's features, such as emotional analysis, which allows companies to gain insight into their emotions, provide improved services to their customers, and predict customer behavior. Global Deep Learning Courses For NLP Market is expected to grow at a Compound Annual Growth Rate (CAGR) of 5.4%.

Artificial intelligence is advancing at a rocket's pace, and every year the field looks fundamentally different than the year before. It's often difficult to keep up with all the news and exciting results. The best way I've found is to follow the machine learning community on Twitter. Keeping track of advancements in AI is not only fun but will also help in interviews by demonstrating to hiring managers your investment in the field. To get you started following the machine learning community, here's a fairly extensive list of AI researchers and pioneers I'm following.

The SA Innovation Summit as an annual flagship event on the South African Innovation Calendar, is a platform for nurturing, developing and showcasing African innovation, as well as facilitating innovation thought-leadership. Created to support and promote innovation and facilitate collaboration within its own eco-system, the initiative brings together corporates, thought leaders, inventors, entrepreneurs, academia and policy makers to amplify South Africa's renowned competitive edge and to inspire sustained economic growth across the continent of Africa. The outcomes achieved by the Summit, is a powerful platform to bring together thought leaders and accelerate innovation in South Africa, and into the African continent as whole. MIIA ill also be represented at the South African Innovation Summit and invitethe MIIA community to also join the 48-hour hackathon being held in Cape Town Stadium from 5 - 7 September 2017.

Video Artificial Intelligence (AI) and Deep Learning is now being used to help schools across the U.S. rapidly and cost-effectively enhance safety and security measures in order to help prevent school shootings and other safety issues facing students on a daily basis. The program was announced recently by Deep North – a pioneer in AI and Deep Learning. A select number of schools will have the opportunity to deploy and field-test this video AI platform in a novel way, leveraging Deep North's advanced object and facial recognition technology to detect and prevent a variety of threats to student safety. The demand for sensible and cost-effective safety measures in schools continues to grow exponentially. The company, therefore, plans to expand into the education sector as a whole in the future.

As other elementary schools across the country were preparing for the new school year by cleaning classrooms and training teachers, Hermosa Elementary, in Artesia, New Mexico was also installing a network of wireless microphones that could pick up the specific concussive audio signature of gunfire. Placed high in classrooms and hallways, the golf-ball-sized devices can alert authorities to the sound and location of gunshots, reportedly within 20 seconds of firing. They can also identify make and model of guns, and automatically lock doors and sound alarms throughout the campus. They are a technological balm for a terrifying problem: In the wake of the Parkland shooting, and Sandy Hook before that, school districts across the nation are spending hundreds of thousands to outfit campuses with high-tech surveillance, crisis response, and police technologies. Playgrounds are cordoned off by biometric locks requiring face and iris scans, parking lots are scanned and license plates are recorded, gunshot-detection devices are embedded in cafeterias, human police wear body cameras, and autonomous robots patrol hallways to detect weapons.

Have you ever done things that were fun and easy instead of a really hard and really important thing that you really had to do? You know, like sorting through 10-year-old pictures and reordering them in new files with improved -- and more creative -- names, instead of doing that major spring cleaning of the house that you have planned for two months in advance. According to TED speaker and procrastinator expert Tim Urban from Whait But Why (a website that I've stumbled upon during a procrastination session), all people are procrastinators! You and everyone you know are procrastinators. Some are more pro than others, though.

According to the report, AI is now the number one strategic enterprise IT investment priority in 2018. Applitools developed the first and only AI-powered image comparison technology that mimics the human eye and brain to streamline testing of web and mobile applications. The Visual AI engine only reports differences that are perceptible to users and reliably ignore invisible rendering, size and position differences. The algorithms can instantly validate entire application pages, detect layout issues, and process the most complex and dynamic pages. With tens of thousands of users across more than 300 customers, test automation engineers and developers have used Applitools Eyes to perform more than 100 million visual comparisons and one billion component level validations.

Much of our curriculum is based on feedback from corporate and government partners about the technologies they are using and learning. In addition to their feedback we wanted to develop a data-driven approach for determining what we should be teaching in our data science corporate training and our free fellowship for masters and PhDs looking to enter data science careers in industry. Below is a ranking of 23 open-source deep learning libraries that are useful for Data Science, based on Github and Stack Overflow activity, as well as Google search results. The table shows standardized scores, where a value of 1 means one standard deviation above average (average score of 0). For example, Caffe is one standard deviation above average in Github activity, while deeplearning4j is close to average.