Spaced repetition learning system. Ideal for studying. The application works on the principle of flashcards.
|CI/CD||Status (on master)|
|Build and Deploy Flutter|
|Build and Deploy Firebase|
|Run Flutter Driver tests|
|Flutter code coverage|
Source Code: https://github.com/futureware-tech/delern
#flutter #dart #mobile-apps
In SSMS, we many of may noticed System Databases under the Database Folder. But how many of us knows its purpose?. In this article lets discuss about the System Databases in SQL Server.
Fig. 1 System Databases
There are five system databases, these databases are created while installing SQL Server.
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Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.
The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.
#developers corner #learn transfer learning #machine learning #transfer learning #transfer learning methods #transfer learning resources
**Abstract. **We present a Reinforcement Learning (RL) based approach to implement Recommender Systems. The results are based on a real-life Wellness app that is able to provide personalized health / activity related content to users in an interactive fashion. Unfortunately, current recommender systems are unable to adapt to continuously evolving features, e.g. user sentiment, and scenarios where the RL reward needs to computed based on multiple and unreliable feedback channels (e.g., sensors, wearables). To overcome this, we propose three constructs: (i) weighted feedback channels, (ii) delayed rewards, and (iii) reward boosting, which we believe are essential for RL to be used in Recommender Systems.
This paper appears in the proceedings of AAI4H — Advances in Artificial Intelligence for Healthcare Workshop, co-located with the 24th European Conference on Artificial Intelligence (ECAI 2020), Sep 2020 (paper pdf) (ppt)
Health / Wellness apps have historically suffered from low adoption rates. Personalized recommendations have the potential of improving adoption, by making increasingly relevant and timely recommendations to users. While recommendation engines (and consequently, the apps based on them) have grown in maturity, they still suffer from the ‘cold start’ problem and the fact that it is basically a push-based mechanism lacking the level of interactivity needed to make such apps appealing to millennials.
We present a Wellness app case-study where we applied a combination of Reinforcement Learning (RL) and Natural Language Processing (NLP) / Chatbots to provide a highly personalized and interactive experience to users. We focus on the interactive aspect of the app, where the app is able to profile and converse with users in real-time, providing relevant content adapted to the current sentiment and past preferences of the user.
The core of such chatbots is an intent recognition Natural Language Understanding (NLU) engine, which is trained with hard-coded examples of question variations. When no intent is matched with a confidence level above 30%, the chatbot returns a fallback answer. The user sentiment is computed based on both the (explicit) user response and (implicit) environmental aspects, e.g. location (home, office, market, …), temperature, lighting, time of the day, weather, other family members present in the vicinity, and so on; to further adapt the chatbot response.
RL refers to a branch of Artificial Intelligence (AI), which is able to achieve complex goals by maximizing a reward function in real-time. The reward function works similar to incentivizing a child with candy and spankings, such that the algorithm is penalized when it takes a wrong decision and rewarded when it takes a right one — this is reinforcement. The reinforcement aspect also allows it to adapt faster to real-time changes in the user sentiment. For a detailed introduction to RL frameworks, the interested reader is referred to .
Previous works have explored RL in the context of Recommender Systems [2, 3, 4, 5], and enterprise adoption also seems to be gaining momentum with the recent availability of Cloud APIs (e.g. Azure Personalizer [6, 7]) and Google’s RecSim . However, they still work like a typical Recommender System. Given a user profile and categorized recommendations, the system makes a recommendation based on popularity, interests, demographics, frequency and other features. The main novelty of these systems is that they are able to identify the features (or combination of features) of recommendations getting higher rewards for a specific user; which can then be customized for that user to provide better recommendations .
Unfortunately, this is still inefficient for real-life systems which need to adapt to continuously evolving features, e.g. user sentiment, and where the reward needs to computed based on multiple and unreliable feedback channels (e.g., sensors, wearables).
The rest of the paper is organized as follows: Section 2 outlines the problem scenario and formulates it as an RL problem. In Section 3, we propose
three RL constructs needed to overcome the above limitations: (i) weighted feedback channels, (ii) delayed rewards, and (iii) reward boosting, which we believe are essential constructs for RL to be used in Recommender Systems.
‘Delayed Rewards’ in this context is different from the notion of Delayed RL , where rewards in the distant future are not considered as valuable as immediate rewards. This is very different from our notion of ‘Delayed Rewards’ where a received reward is only applied after its consistency has been validated by a subsequent action. Section 4 concludes the paper and provides directions for future research.
#recommendation-system #data-science #reinforcement-learning #machine-learning #chatbots #reinforcement learning based recommender systems
Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.
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1 August 2020, Sunny Sky solarannounced you to launch a residential solar power system in Queensland, Australia. There are different sizes of houses with different energy requirements so one solar power system cannot fulfill every type of electricity need.
Whether energy need is low or higher they have announced a wide range of solar power system in Brisbane that includes 5KW solar panel system, 6Kw solar panel system, 10Kw solar panel system, and many more so that everyone can enjoy the benefits of solar energy.
Residential Solar Power System needs to be flexible because of the changing requirement of energy. As we know our energy needs hikes up in the summers more than winters because we use air conditioners, refrigerators (also used in winters but less than summers), fans. In winter we drop down these usages so the energy needs to go up and down according to the weather changing.
Some households have a high energy need, some have low, and mostly have the normal or average of high and low. Sunny Sky Solar offers expert’s advice to all the customers on call or personally because it is important to analyze the energy need, budget, location, and many other things before buying a solar power system for your home sweet home.
Their professionals analyze all these things and suggest you the best residential solar power system in Brisbane to reduce the energy costs and clean the environment as solar energy is green & clean energy.
At this time of announcing the residential solar panel system, the representative of Sunny Sky Solar has talked about some advantages of a residential solar power system. He said “get update yourself by the time is important because the latest technology will save you lots of money and time. The solar power system is the best technology in this era that can give you lots of benefits. Don’t get upset with the initial cost because after installing a solar power system at your house it will repay you the initial cost in two to three years. So, you are going to invest in a great deal if you are purchasing a solar panel system in Brisbane.”
He also added “Residential solar power system can save your pocket from getting loose every month for heavy electricity bills. You will earn money by producing solar energy and feeding your power supply grid as government, and mostly all the power suppliers give benefits to producing solar energy. You can easily earn money by feeding the power grid with your excess produced solar energy. You will use solar energy and save the excess by feeding the power grid this way.”
Sunny Sky Solar offering an efficient range of residential and commercial solar power system that includes 5KW solar panel system, 6.6Kw solar panel system, 10Kw solar panel system, and there are many more that you can select according to your energy needs and budget.
They provide expert assistance that will help you in choosing the best solar system for your house. Their experienced professionals work under the guidance of experts who ensures the perfections and safety at the time of installing and after the installation.
Installing a solar power system at your place will be more convenient with them because they work under the expert’s supervision that makes them perfect and faster. They ensure safety first at the time of installing because at that time family members are around the installing site and accidents can happen.
They also ensure the quality of products they used in installing and other solar products. If the products will be durable and efficient, the system will produce more electricity with higher efficiency for a longer period.
The main thing that matters while installing a solar power system at a residence is the roof situation, Sunny Sky Solar doesn’t work for doing business only. They first check the place or analyze from your information that your location is safe for installing a solar power system or not. If the find any problem they will suggest repairing it first because if you will put the solar power system at a less secure place and the solar system’s weight can damage it then repairing that place first should your main priority.
This shows their loyalty and caring behavior towards the customers.
#solar panel system #solar panel system in brisbane #5kw solar panel system #5kw solar panel system #10kw solar panel system #10kw solar panel system in brisbane