Browse

Dimension Test :424 x 263
Google Images earlier offered a useful “search by size” option in advanced search to help you find logos, wallpapers and other images on the Internet by their exact size (or resolution).
For instance, you could limit your search for landscape photographs to image files that were at least 10 Megapixels in size. Or, if you are were using Google Image search to find wallpapers for the desktop, you could specify the image resolution as 1920x1080 pixels and Google would only return large images with those exact dimensions

How to Search Google Images by the Exact Size
ref:https://www.labnol.org/internet/google-image-size-search/26902/#:~:text=Here's%20how.,that%20match%20the%20specified%20size.The “exact size” search option is no longer available in Google Image Search but you can still limit your image searches to a particular size by using the secret imagesize search operator in the query itself.
Here’s how.
Go to images.google.com and enter the search terms as before. Then append imagesize:WIDTHxHEIGHT to your query and hit Enter. Google Images will remove the operator from the query but the results will only display images that match the specified size.
ref::https://www.img2go.com/result#j=076d9aae-9369-4903-81f9-1c3e58e31ff7 Dimension : 519x334

Ref test : https://www.google.com/search?rlz=1C5GCEA_enLK882LK883&ei=5S7rX7WrN8jw9QOh-4KYAQ&q=chemical+tests&oq=chemical+tests&gs_lcp=CgZwc3ktYWIQAzIFCAAQyQMyAggAMgIIADICCAAyAggAMgIIADICCAAyAggAMgIIADICCAA6CAgAEMkDEJECOgUIABCRAjoKCAAQsQMQgwEQQzoICAAQsQMQgwE6BQgAELEDOgIILjoECAAQQzoOCC4QsQMQgwEQxwEQrwE6BwguELEDEEM6BQguELEDOgoIABCxAxDJAxBDOgcIABCxAxBDOggILhDHARCvAToGCAAQFhAeOggIABAWEAoQHlDs-esXWIea7BdgrZvsF2gCcAF4AYABoQKIAeARkgEGMC4xMy4ymAEAoAEBqgEHZ3dzLXdperABAMABAQ&sclient=psy-ab&ved=0ahUKEwj1se7ipfPtAhVIeH0KHaG9ABMQ4dUDCA0&uact=5

Algebra Formulas | Maths Formulas
1. 𝑎2−𝑏2=(𝑎+𝑏)(𝑎−𝑏)
2. (𝑎+𝑏)2=𝑎2+2𝑎𝑏+𝑏2
3. 𝑎2+𝑏2=(𝑎−𝑏)2+2𝑎𝑏
4. (𝑎−𝑏)2=𝑎2−2𝑎𝑏+𝑏2
5. (𝑎+𝑏+𝑐)2=𝑎2+𝑏2+𝑐2+2𝑎𝑏+2𝑎𝑐+2𝑏𝑐
6. (𝑎−𝑏−𝑐)2=𝑎2+𝑏2+𝑐2−2𝑎𝑏−2𝑎𝑐+2𝑏𝑐
7. (𝑎+𝑏)3=𝑎3+3𝑎2𝑏+3𝑎𝑏2+𝑏3;(𝑎+𝑏)3=𝑎3+𝑏3+3𝑎𝑏(𝑎+𝑏)
8. (𝑎−𝑏)3=𝑎3−3𝑎2𝑏+3𝑎𝑏2−𝑏3
9. 𝑎3−𝑏3=(𝑎−𝑏)(𝑎2+𝑎𝑏+𝑏2)
10. 𝑎3+𝑏3=(𝑎+𝑏)(𝑎2−𝑎𝑏+𝑏2)
11. (𝑎+𝑏)4=𝑎4+4𝑎3𝑏+6𝑎2𝑏2+4𝑎𝑏3+𝑏4
12. (𝑎−𝑏)4=𝑎4−4𝑎3𝑏+6𝑎2𝑏2−4𝑎𝑏3+𝑏4
13. 𝑎4−𝑏4=(𝑎−𝑏)(𝑎+𝑏)(𝑎2+𝑏2)
14. 𝑎5−𝑏5=(𝑎−𝑏)(𝑎4+𝑎3𝑏+𝑎2𝑏2+𝑎𝑏3+𝑏4)
15. (𝑥+𝑦+𝑧)2=𝑥2+𝑦2+𝑧2+2𝑥𝑦+2𝑦𝑧+2𝑥𝑧
 
16. (𝑥+𝑦−𝑧)2=𝑥2+𝑦2+𝑧2+2𝑥𝑦−2𝑦𝑧−2𝑥𝑧
 
17. (𝑥−𝑦+𝑧)2=𝑥2+𝑦2+𝑧2−2𝑥𝑦−2𝑦𝑧+2𝑥𝑧
18. (𝑥−𝑦−𝑧)2=𝑥2+𝑦2+𝑧2−2𝑥𝑦+2𝑦𝑧−2𝑥𝑧
19. 𝑥3+𝑦3+𝑧3−3𝑥𝑦𝑧=(𝑥+𝑦+𝑧)(𝑥2+𝑦2+𝑧2−𝑥𝑦−𝑦𝑧−𝑥𝑧)
 
20. 𝑥2+𝑦2=12[(𝑥+𝑦)2+(𝑥−𝑦)2]

Reinforcement Learning is a feedback-based Machine learning technique in which an agent learns to behave in an environment by performing the actions and seeing the results of actions. For each good action, the agent gets positive feedback, and for each bad action, the agent gets negative feedback or penalty.

In Reinforcement Learning, the agent learns automatically using feedbacks without any labeled data, unlike supervised learning.
Since there is no labeled data, so the agent is bound to learn by its experience only.
RL solves a specific type of problem where decision making is sequential, and the goal is long-term, such as game-playing, robotics, etc.
The agent interacts with the environment and explores it by itself. The primary goal of an agent in reinforcement learning is to improve the performance by getting the maximum positive rewards. terms: 
Agent(): An entity that can perceive/explore the environment and act upon it.
Environment(): A situation in which an agent is present or surrounded by. In RL, we assume the stochastic environment, which means it is random in nature.
Action(): Actions are the moves taken by an agent within the environment.
State(): State is a situation returned by the environment after each action taken by the agent.
Reward(): A feedback returned to the agent from the environment to evaluate the action of the agent.
Policy(): Policy is a strategy applied by the agent for the next action based on the current state.
Value(): It is expected long-term retuned with the discount factor and opposite to the short-term reward.
Q-value(): It is mostly similar to the value, but it takes one additional parameter as a current action (a).

he educator plans a successful learning unit by applying the constructive alignment principle. This includes building relationships among learning outcomes, assessment, and learning activities.
Method Components
The learning unit is a learning experience that results in a learner being able to do something they could not do prior to having the learning experience. This new ability or competency is called Unit Learning Outcome. The effectiveness of a learning unit can be enhanced by using the philosophies of outcome-based education, which focuses on organizing everything around the achievement of the learning outcomes and related competencies (Spady, 1994). One of the key elements in designing this learning unit is to apply the constructive alignment principle. This micro-credential shows how to apply the constructive alignment principle when planning a learning unit.ref: credential: https://microcredentials.digitalpromise.org/explore/constructive-alignment-principle-application-for-l

What is communication (and what isn’t it)?
The P21 framework emphasizes effective use of oral, written, and nonverbal communication skills for multiple purposes (e.g., to inform, instruct, motivate, persuade, and share ideas). It also focuses on effective listening, using technology to communicate, and being able to evaluate the effectiveness of communication efforts—all within diverse contexts (adapted from P21). Note that working in partners is a great way to collaborate or build shared understanding but a critical part of communication is sharing with an authentic audience

The student could then chart her score using a computer programUse behavioral and environmental prompts to increase desired classroom behaviors. For example, pictorial prompts of students attending in class serve as a reminder of the teacher’s expectations for learning and behavior. Electronic visual aids such as interactive whiteboards and document cameras are helpful for capturing the attention of students with ADHD (Piffner, 2011).