1603263600

Take a moment to locate the nearest major city around you.

If you were to travel there now, which mode of transportation would you use? You may take a car, a bus, or a train. Perhaps you’ll ride a bike, or even purchase an airplane ticket.

Regardless of how you get to the location, you incorporate _probabilit_y into the decision-making process. Perhaps there’s a 70 percent chance of rain or a car crash, which can cause traffic jams. If your bike tire is old, it may pop - this is certainly a large probabilistic factor.

On the other hand, there are *deterministic* costs — for instance, the cost of gas or an airplane ticket — as well as deterministic rewards — like substantially faster travel times taking an airplane (depending on the location) or perhaps a free Uber/Lyft ride.

These types of problems - in which an agent must balance probabilistic and deterministic rewards and costs - appear commonly in decision-making. Markov Decision Processes are used to model these types of optimization problems and can be applied furthermore to more complex tasks in Reinforcement Learning.

To illustrate a Markov Decision process, consider a dice game:

- Each round, you can either
**continue**or**quit**. - If you
**quit**, you receive $5 and the game ends. - If you
**continue**, you receive $3 and roll a 6-sided die. If the die comes up as 1 or 2, the game ends. Otherwise, the game continues onto the next round.

Clearly, there is a trade-off here. For one, we can trade a deterministic gain of $2 for the chance to roll a dice and continue onto the next round.

Let’s establish some vocabulary, which we will use to create an MDP to model this game:

- A _state _is a status the agent (decision-maker) can hold. In the dice game, the agent can either be
*in the game*or*out of the game*. - An
*action*is a movement the agent can choose. These move the agent between states, with certain penalties or rewards. *Transition probabilities*describe the probability of ending up in a state s’ (s prime) given an action*a*. These will be often denoted as a function*P*(*s*,*a*,*s*’) that outputs the probability of ending up in*s’*given current state*s*and action*a*.- For example,
*P*(*s*=playing the game,*a*=choose to continue playing,*s’*=not playing the game) is ⅓, since there is a two-sixths (one-third) chance of losing the dice roll. *Rewards*are given depending on the action. The reward for continuing the game is 3, whereas the reward for quitting is $5. The ‘overall’ reward is to be optimized.

#data-science #programming #machine-learning #ai #artificial-intelligence

1603263600

Take a moment to locate the nearest major city around you.

If you were to travel there now, which mode of transportation would you use? You may take a car, a bus, or a train. Perhaps you’ll ride a bike, or even purchase an airplane ticket.

Regardless of how you get to the location, you incorporate _probabilit_y into the decision-making process. Perhaps there’s a 70 percent chance of rain or a car crash, which can cause traffic jams. If your bike tire is old, it may pop - this is certainly a large probabilistic factor.

On the other hand, there are *deterministic* costs — for instance, the cost of gas or an airplane ticket — as well as deterministic rewards — like substantially faster travel times taking an airplane (depending on the location) or perhaps a free Uber/Lyft ride.

These types of problems - in which an agent must balance probabilistic and deterministic rewards and costs - appear commonly in decision-making. Markov Decision Processes are used to model these types of optimization problems and can be applied furthermore to more complex tasks in Reinforcement Learning.

To illustrate a Markov Decision process, consider a dice game:

- Each round, you can either
**continue**or**quit**. - If you
**quit**, you receive $5 and the game ends. - If you
**continue**, you receive $3 and roll a 6-sided die. If the die comes up as 1 or 2, the game ends. Otherwise, the game continues onto the next round.

Clearly, there is a trade-off here. For one, we can trade a deterministic gain of $2 for the chance to roll a dice and continue onto the next round.

Let’s establish some vocabulary, which we will use to create an MDP to model this game:

- A _state _is a status the agent (decision-maker) can hold. In the dice game, the agent can either be
*in the game*or*out of the game*. - An
*action*is a movement the agent can choose. These move the agent between states, with certain penalties or rewards. *Transition probabilities*describe the probability of ending up in a state s’ (s prime) given an action*a*. These will be often denoted as a function*P*(*s*,*a*,*s*’) that outputs the probability of ending up in*s’*given current state*s*and action*a*.- For example,
*P*(*s*=playing the game,*a*=choose to continue playing,*s’*=not playing the game) is ⅓, since there is a two-sixths (one-third) chance of losing the dice roll. *Rewards*are given depending on the action. The reward for continuing the game is 3, whereas the reward for quitting is $5. The ‘overall’ reward is to be optimized.

#data-science #programming #machine-learning #ai #artificial-intelligence

1624395600

Learn the most popular NoSQL / document database: MongoDB. In this quickstart tutorial, you’ll be up and running with MongoDB and Python.

⭐️Course Contents⭐️

⌨️ (0:00:00) Welcome

⌨️ (0:04:33) Intro to MongoDB

⌨️ (0:07:49) How do document DBs work?

⌨️ (0:10:34) Who uses MongoDB

⌨️ (0:13:02) Data modeling

⌨️ (0:16:30) Modeling guidelines

⌨️ (0:22:11) Integration database

⌨️ (0:24:23) Getting demo code

⌨️ (0:30:07) How ODMs work?

⌨️ (0:32:55) Introduction to mongoengine

⌨️ (0:34:01) Demo: Registering connections with MongoEngine

⌨️ (0:37:20) Concept: Registering connections

⌨️ (0:39:14) Demo: Defining mongoengine entities (classes)

⌨️ (0:45:22) Concept: mongoengine entities

⌨️ (0:49:03) Demo: Create a new account

⌨️ (0:56:55) Demo: Robo 3T for viewing and managing data

⌨️ (0:58:18) Demo: Login

⌨️ (1:00:07) Demo: Register a cage

⌨️ (1:10:28) Demo: Add a bookable time as a host

⌨️ (1:16:13) Demo: Managing your snakes as a guest

⌨️ (1:19:18) Demo: Book a cage as a guest

⌨️ (1:33:41) Demo: View your bookings as guest

⌨️ (1:41:29) Demo: View bookings as host

⌨️ (1:46:18) Concept: Inserting documents

⌨️ (1:47:28) Concept: Queries

⌨️ (1:48:09) Concept: Querying subdocuments with mongoengine

⌨️ (1:49:37) Concept: Query using operators

⌨️ (1:50:24) Concept: Updating via whole documents

⌨️ (1:51:46) Concept: Updating via in-place operators

⌨️ (1:54:01) Conclusion

📺 The video in this post was made by freeCodeCamp.org

The origin of the article: https://www.youtube.com/watch?v=E-1xI85Zog8&list=PLWKjhJtqVAbnqBxcdjVGgT3uVR10bzTEB&index=10

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#mongodb #python #python crash course #mongodb with python crash course - tutorial for beginners #beginners #mongodb with python crash course

1600398733

In this article, I will introduce the concept of dynamic programming, developed by Richard Bellman in the 1950s, a powerful algorithm design technique to solve problems by breaking them down into smaller problems, storing their solutions, and combining these to get to the solution of the original problem.

The hardest problems asked in FAANG coding interviews usually fall under this category. It is likely that you will get tasked with solving one during your interviews, hence the importance of knowing this technique. I will explain what dynamic programming is, give you a recipe to tackle dynamic programming problems, and will take you through a few examples so that you can understand better when and how to apply it.

#dynamic-programming #programming-interviews #programming

1624531051

Alerts4Dynamics is productivity app for Dynamics 365 CRM that helps to create, schedule, manage and track alerts. It helps to notify and pass relevant information to target audience right within Dynamics 365 CRM. These notifications can be created and displayed indefinitely or for a defined scheduled period. Notification button is available on all entities and can be accessed from anywhere in the CRM.

Features

• Create Announcement and Rule Based/Record Based alerts.

• Alerts can be sent as Pop-ups, Form Notifications or Email to target Dynamics 365 CRM users.

• Categorize alerts as Information, Warning or Critical.

• Track log of read/dismissed alerts by users.

• Define process start date from when the notifications will start getting created and process end date when creation of new notifications will stop. Also, add the display end date for notification.

#dynamics 365 pop-up alert #dynamics 365 email alerts #dynamics 365 bulk alerts #dynamics crm pop-up alert #dynamics 365 notifications #dynamics crm alert

1625057189

Auto Tax Calculator enables users to calculate tax automatically within Dynamics 365 for Sales. With this productivity app, there is no further need to calculate tax manually which increases accuracy and efficiency of users. Your taxes can be applied correctly the first time in order thereby, saving your time and effort during tax season.

Auto Tax Calculator is available for Dynamics 365 8.2 & above and Power Apps. It supports Dynamics 365 On-Premises & Online.

Features

• Automatic Tax Calculation: Calculate tax automatically for OOB entities – Opportunity, Quote, Order and Invoice

• Tax Schedules & Details: Create specific Tax Schedules and Tax Details to calculate taxes automatically

• AvaTax Integration: Seamless integration with AvaTax from Avalara

• Geolocational Tax Calculation: Calculate taxes as per the rules and regulations stated for specific geographical locations using AvaTax

https://www.inogic.com/product/productivity-apps/automated-tax-calculation-processing-dynamics-365-crm

#automatic tax calculations dynamics 365 #avalara integration dynamics crm #avalara integration dynamics 365 #tax calculation dynamics 365 #tax calculation dynamics crm #tax processing application dynamics crm