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--- ip:ws2021:lets_plaiy:student-documentation:further-reading:start [2022/01/13 18:09] – [What's the story today?] pranay001
+++ ip:ws2021:lets_plaiy:student-documentation:further-reading:start [2022/01/13 18:26] (current) – pranay001
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-Adults like to use a lot of complicated words to say something. This could be because they want to show their impressive vocabulary. It gets worse in technical fields like Computer Science which have a lot of big technical words. These technical words are supposed to help differentiate between similar ideas. But most often it complicates simple ideas. In this article, I’ll try not to use these complicated words. This is so that you can understand the ideas clearly rather than memorize big words. So let’s get started with a bit of history.
+===== LET US GUESS ======
+Adults enjoy using a lot of sophisticated phrases to express themselves. This might be due to their desire to demonstrate their outstanding vocabulary. It's much worse in technical disciplines with a lot of huge technical terms, such as Computer Science. These technical terms are intended to assist distinguish between related concepts. However, it frequently confuses basic concepts. I'll try not to use any of these difficult terms in this essay. This is so that you can comprehend the concepts rather than memorizing large words. Let's begin with some background information.
 **Let’s take it from the top.**\\
-You might have seen computers all around you. From the smartwatch to phones, tablets, and laptops — they are all different forms of computers. These useful gadgets of today have always been “Dumb Machines”. They are good at obeying orders like an obedient puppy (or kitten for those who are weird like that ). If you tell it to Swipe Right, it swipes right. If you tell it to Swipe Left, it swipes left. If you tell it to open Instagram when someone clicks on the Instagram icon it will open it. You get the drift. It can’t think of anything on its own.
+You may have seen computers all over the place. Computers come in a variety of shapes and sizes, from smartwatches to phones, tablets, and laptops. Today's handy devices have traditionally been referred to as "Dumb Machines." They are as obedient as a dog when it comes to obeying directions (or kitten for those who are weird like that ). Swipe Right is what it does when you instruct it to. Swipe Left is what it does when you instruct it to. It will launch Instagram if you instruct it to when someone clicks on the Instagram symbol. I think you get the idea. It is incapable of thinking of anything on its own.
-Although these machines are dumb, over the last 50 years, we have made the computers do many things. From adding numbers and playing songs to defeating grandmasters at Chess. But for each of those things, someone had to tell the computer what to do and exactly how to do it.
+Despite the fact that these machines are dumb, we have taught them to perform a lot of things during the previous 50 years. From adding numbers to performing tunes to defeating Grandmasters at Chess, there's something for everyone. But someone has to teach the computer what to do and how to do it for each of those things.As a result, the capabilities of a computer were restricted. Computers could only do tasks that humans could demonstrate to them (step by step). As you can expect, the enchantment quickly lost its allure. There are always those situations in which :
-So the things that a computer could do for us was limited. Computers could only do things that we could show them how to do (step by step). As you could guess, that magic lost its charm pretty soon. There are always some things where :
+  * Unordered List Item Even humans have no idea how to achieve it
-  * Unordered List Item even humans don’t know how to do it (let alone explain to the computer)
+  * Ordered List Item certain duties for which we have no idea how to instruct the machine. (We can't express it well enough for a machine to grasp)
-  * Ordered List Item and some tasks where we don’t know how to tell the computer what to do. ( we can’t explain it clearly in a language that machine could understand)
-( A big word for telling computers what to do and how to do it is programming. But we wouldn’t use these big words because we don’t wanna be adults yet.)
 ====What's the story today?====
-For a very long time, computers couldn’t do anything without someone telling them how to do something. This was true until recently. Some very smart people* found a way to teach computers to learn by themselves. (Another big word adults use for this is Machine Learning, but psst. who needs that). Imagine you are in a classroom without a teacher. There you and all your classmates learn on your own without any teacher. Weird right? Imagine how weird it must be for a computer to learn on its own. Surprisingly the computer is very good at learning on its own. It can even learn complicated things that people can’t teach it.
+Computers couldn't accomplish anything without someone instructing them how to do it for a long time. Until recently, this was true. Some extremely clever people* devised a method for teaching computers to learn on their own. (Another popular term used by grownups is Machine Learning, but who needs that?) Consider being in a classroom with no teacher. You and your students study on your own without the help of an instructor. Isn't it strange? Imagine the strangeness of a computer learning on its own. Surprisingly, the computer is quite capable of self-learning. It can even learn complex things that humans are unable to teach it.
 ====What do you mean ‘Learning by itself’ ?====
-How we teach computers is by showing it many examples of questions and answers. Experts like to call the whole set of examples as the dataset. These examples contain a sample question(x) and its correct answer(y). An example could contain say ‘a picture of a cat’ and its answer ‘cat’.
+We educate computers by providing them with numerous instances of queries and solutions. The entire set of instances is referred to as the dataset by experts. A sample question(x) and its right answer are included in these instances (y). As an example, consider the phrase "a image of a cat" and the response "cat."This is similar to the practice quizzes that some wonderful instructors provide before an exam. This practice question will help you prepare for the test by giving you an idea of what to anticipate. You may not get the exact same questions on the exam, but it will help you prepare for questions of a similar nature.
-This is very like the sample quiz some nice teachers in school give before an exam. This sample quiz helps to get you prepared for what to expect in the exam. You might not get the exact same questions in the exam but it helps you prepare for similar types of questions.
+We offer computers samples of questions and answers without instructing them on how to get the proper answers. The machine tries to find out a way to estimate the correct answer using the samples. Even if it hasn't encountered this exact question in the examples we've shown it, it learns to make an accurate estimate. The machine looks for similar questions it's seen before and makes a prediction based on the right answer it's seen before. That's all the computer is doing: guessing based on prior experience.
-We show computers examples of questions and answers without telling it how the correct answers can be found. Using the examples, the computer tries to figure out a way to guess the correct answer. It learns to make a correct guess even if it has not seen this particular question in the examples we showed it. The computer looks for similar questions it has seen before and uses the previously seen correct answer to make a guess. That’s all the computer is doing — making a guess based on previously seen examples.
 ====“If the machine is correcting itself, What do we humans do in this?”====
-Machine Learning is not performed by computers on their own. Humans have a very important role in machine learning. Humans play the role of coaches or facilitators who control the whole game :
+Computers do not execute Machine Learning on their own. In machine learning, humans play a critical role. Humans take on the role of coaches or facilitators, overseeing the entire game:
-  - Ordered List Item	Humans provide examples that computers use to learn.
+  - Ordered List Item	Humans serve as models for computers to learn from.
-  - Ordered List ItemAnd humans tell computers how to detect their mistakes.
+  - Ordered List ItemHumans also instruct computers on how to recognize their errors.
-Basically the only thing a machine is doing is making guesses based on examples and correcting its guesses to the best of its ability. Everything else is guided by information and code provided by humans.
+A machine's sole purpose is to make educated assumptions based on examples and to correct those guesses to the best of its abilities. Everything else is governed by human-provided data and code.
 ===“Guessing you say, that's it? “Yes and No.====
-All that the computer is doing is making a guess based on what it knows about similar questions. (A fancy way of saying guessing is called Prediction). Well, it’s not guessing randomly. It tries to learn to make the best possible guess based on some fun math (we don’t need to get into that just yet). To reiterate, the learning in Machine Learning is all about figuring out how to make the best possible guess. The best possible guess is the one that is closest to the correct answer.\\ \\
+All the machine is doing is guessing based on previous experiences with similar queries. (Prediction is a fancy way of meaning guessing.) It's not like you're guessing at random. It tries to learn to make the best prediction possible based on some interesting arithmetic (which we won't go into right now). To summarize, Machine Learning is all about figuring out how to make the best estimate possible. The closest approach to the correct answer is the best potential guess.
-“So how does it guess correctly?”\\ \\
+===“So how does it guess correctly?”===
-Initially, the computer makes really crappy guesses (starting with random guesses). Then it compares its own guess to the correct answer we provided in the example. We ask the computer to reduce the mistakes it’s making in its guesses. It then goes on a wild goose chase to reduce the mistakes. As it tries to reduce its mistakes, it self corrects and thus slowly gets closer to the correct guess.
+At first, the machine makes some extremely bad estimates (starting with random guesses). The program then compares its own guess to the right answer given in the example. We instruct the computer to make fewer predictions and make them more accurate. It then travels on a merry-go-round in an attempt to cut down on errors. It self-corrects as it strives to lessen its errors, bringing it closer to the correct guess.
-The process by which the computer is correcting its own mistakes is called gradient descent by experts. No need to remember the name now. This could be a topic for another fun post in the future. For now, just remember that the computer corrects itself slowly by looking at examples of correct answers.
+Experts refer to the process through which the computer corrects its own errors as gradient descent. There's no need to memorize the name any more. This might be the subject of a future entertaining post. For the time being, just keep in mind that the computer steadily corrects itself by looking at instances of correct replies.