शैडोइंग अभ्यास: A Gentle Introduction to Machine Learning - वीडियो के साथ अंग्रेजी बोलना सीखें
अभ्यास मोड चुनें
हर मोड पूरी स्क्रीन का अभ्यास कक्ष खोलता है।
136 वाक्य
यदि वाक्य बहुत छोटे या लंबे हों, तो समायोजित करने के लिए Edit पर क्लिक करें।
1
Gonna start this StatQuest with a silly song.
2
But if you don't like silly songs, that's okay.
3
StatQuest.
4
Hello, I'm Josh Starmer and welcome to StatQuest.
5
Today we're going to do a gentle introduction to machine learning.
6
Note, this StatQuest was originally prepared for and presented at the Society for Scientific Advancement's annual conference.
7
One of the things that SOSA does is promote science and technology in Jamaica.
8
Let's start with a silly example.
9
Do you like silly songs?
10
If you like silly songs, are you interested in machine learning?
11
If you like silly songs and machine learning, then you'll love StatQuest.
12
If you like silly songs but not machine learning, are you interested in statistics?
13
If you like silly songs and statistics but not machine learning, then you'll still love StatQuest.
14
Otherwise, you might not like StatQuest.
15
If you don't like silly songs, are you interested in machine learning?
16
If you don't like silly songs but you like machine learning, then you'll love StatQuest.
17
If you don't like silly songs or machine learning, are you interested in statistics?
18
If you don't like silly songs or machine learning, but you're interested in statistics, then you will love StatQuest.
19
Otherwise, you might not like StatQuest.
20
Wah wah.
21
This is a silly example, but it illustrates a decision tree, a simple machine learning method.
22
The purpose of this particular decision tree is to predict whether or not someone will love StatQuest.
23
Alternatively, we could say that this decision tree classifies a person as either someone who loves StatQuest or someone who doesn't.
24
Since decision trees are a type of machine learning, then if you understand how we use this tree to predict or classify if someone would love StatQuest,
25
you are well on your way to understanding machine learning.
26
Bam!
27
Here's another silly example of machine learning.
28
Imagine we measured how quickly someone could run 100 meters.
29
And how much yam they ate.
30
This is me.
31
I'm not very fast, and I don't eat much yam.
32
These are some other people.
33
And this is Usain Bolt.
34
Usain Bolt is very fast, and he eats a lot of yam.
35
Given this pretend data, we see that the more yam someone eats, the faster they run the 100 meter dash.
36
We can fit a black line to the data to show the trend.
37
but we can also use the black line to make predictions.
38
For example, if someone told us they ate this much yam, then we could use the black line to predict how fast that person might run.
39
This is the predicted speed.
40
The black line is a type of machine learning because we can use it to make predictions.
41
In general, machine learning is all about making predictions and classifications.
42
Bam!
43
Now that we can make predictions and classifications, let's talk about some of the main ideas in machine learning.
44
First of all, in machine learning lingo, the original data is called training data.
45
So the black line is fit to training data.
46
Alternatively, we could have fit a green squiggle to the training data.
47
The green squiggle fits the training data better than the black line, but remember, the goal of machine learning is to make predictions.
48
So we need a way to decide if the green squiggle is better or worse than the black line at making predictions.
49
So we find a new person and measure how fast they run and how much yam they eat.
50
And then we find another, and another, and another.
51
All together, the blue dots represent testing data.
52
We use the testing data to compare the predictions made by the black line to the predictions made by the green squiggle.
53
Let's start by seeing how well the black line predicts the speed of each person in the testing data.
54
Here's the first person in the testing data.
55
They ate this much yam.
56
And they ran this fast.
57
However, the black line predicts that someone who ate this much yam should run a little slower.
58
So let's measure the distance between the actual speed and the predicted speed,
59
and save the distance on the right while we focus on the other people in the testing data.
60
Here's the second person in the testing data.
61
They ate this much yam, and they ran this fast.
62
but the black line predicts that they will run a little faster.
63
So we measure the distance between the actual speed and the predicted speed, and add it to the one we measured for the first person in the testing data.
64
Then we measure the distance between the real and the predicted speed for the third person in the testing data,
65
and add it to our running total of distances between the real and predicted speeds for the black line.
66
Then we do the same thing for the fourth person in the testing data.
67
And add that distance to our running total for the black line.
68
This is the sum of all the distances between the real and predicted speeds for the black line.
69
Now let's calculate the distances between the real and predicted speeds using the green squiggle.
70
Remember, the green squiggle did a great job fitting the training data.
71
But when we are doing machine learning, we are more interested in how well the green squiggle can make predictions with new data.
72
So, just like before, we determine this person's real speed and their predicted speed and measure the distance between them.
73
And just like we did for the black line, We'll keep track of the distances for the green squiggle over here.
74
Then we do the same thing for the second person in the testing data. And the third person. And the fourth person.
75
This is the sum of the distances between the real and predicted speeds for the green squiggle.
76
The sum of the distances is larger for the green squiggle than the black line.
77
In other words, even though the green squiggle fit the training data way better than the black line,
78
the black line did a better job predicting speeds with the testing data.
79
So if we had to choose between using the black line or the green squiggle to make predictions, we would choose the black line.
80
Bam!
81
This example teaches two main ideas about machine learning.
82
First, we use testing data to evaluate machine learning methods.
83
Second, don't be fooled by how well a machine learning method fits the training data.
84
Fitting the training data well but making poor predictions is called the bias-variance trade-off.
85
Oh no!
86
A shameless self-promotion!
87
If you want to learn more about the bias-variance trade-off, there's a stat quest that will walk you through it one step at a time.
88
Before we move on, you may be wondering why we used a simple black line
89
in a silly green squiggle instead of a Deep Learning Convolutional Neural Network.
90
Or, Insert newest, bestest, most fancy machine learning method here.
91
There are tons of fancy-sounding machine learning methods.
92
And each year, something new and exciting comes on the scene.
93
But regardless of what you use, the most important thing isn't how fancy it is, but how it performs with testing data.
94
Double bam!
95
Now let's go back to the decision tree that we started with.
96
Remember, we wanted to classify if someone loves StatQuest based on a few questions.
97
To create the decision tree, we collected data from people who love StatQuest, and from people who did not love StatQuest.
98
Altogether, this was the training data, and we used it to build the decision tree.
99
Then we got data from a few more people who love StatQuest, and a few more people who did not love StatQuest.
100
Altogether, this forms the testing data.
101
We can use the testing data to see how well our decision tree predicts if someone will love StatQuest.
102
The first person in the testing data did not like silly songs, so we go to the right side of the decision tree.
103
They didn't like machine learning, either.
104
So we just keep on going down the right side of the decision tree.
105
They didn't like statistics either.
106
So the decision tree predicts that this person will not love StatQuest.
107
However, this person loves StatQuest, so the decision tree made a mistake.
108
Wah wah.
109
The second person in the testing data liked silly songs.
110
And that takes us down the left side of the decision tree.
111
They were also interested in machine learning.
112
So we predict that that person loves StatQuest.
113
And since this person actually loves StatQuest, the decision tree did a good job.
114
Hooray!
115
Now we just run all of the other people in the testing data down the decision tree
116
and compare the predictions to reality.
117
Then we can compare this decision tree to the latest greatest machine learning method.
118
Ultimately, we pick the method that does the best job predicting if someone will love StatQuest or not.
119
Triple Bam!
120
In summary, machine learning is all about making predictions and classifications.
121
There are tons of fancy machine learning methods, but the most important thing to know about them isn't what makes them so fancy.
122
It's that we decide which method fits our needs the best by using testing data.
123
One last thing before we go.
124
You may be wondering how we decide which data go into the training set and which data go into the testing set.
125
Earlier, we just arbitrarily decided that these red dots were the training data.
126
But the blue dots could have, just as easily, been the training data.
127
The good news is that there are ways to determine
128
which samples should be used for training data and which samples should be used for testing data.
129
And if you're interested in learning more about this, check out the StatQuest.
130
And there are lots more StatQuests that walk you through machine learning concepts step by step, so check them out.
131
Hooray!
132
We've made it to the end of another exciting StatQuest.
133
If you like this StatQuest and want to see more, please subscribe.
134
And if you want to support StatQuest, well, consider buying one or two of my original songs, or getting a t-shirt or a hoodie or some other slick merchandise.
135
There's links on the screen and there's links in the description below.
136
Alright, until next time, quest on!
ऐप डाउनलोड करें
धाराप्रवाह बोलने के लिए ज़रूरी सब कुछ
AI उच्चारण स्कोरहर वाक्य का स्कोर
IPA अभ्यासहर ध्वनि में महारत
शब्दावलीअपना शब्द भंडार बनाएँ
शब्द गेमखेलते हुए सीखें
उसी चैनल से
C11
Ridge vs Lasso Regression, Visualized!!!
Daily ConversationUniversity Students
C17
Gaussian Naive Bayes, Clearly Explained!!!
Daily ConversationUniversity Students
C11
Naive Bayes, Clearly Explained!!!
Daily ConversationUniversity Students
C11
The Essential Main Ideas of Neural Networks
Business EnglishUniversity StudentsWorking Adults
C12
The GELU, SiLU and SwiGLU activation functions, clearly explained!!!
Daily ConversationUniversity Students
FOR YOU
अनुशंसित वीडियो
C11
165 वाक्य
My Method for Learning Languages from Scratch
Business EnglishUniversity StudentsWorking Adults
C13
43 वाक्य
P1 机器学习的应用【2024公认最好的 | 吴恩达机器学习 | 教程 | Machine Learning Specialization(超爽中英!)】
Business EnglishUniversity StudentsWorking Adults
C12
106 वाक्य
What is Engineering?: Crash Course Engineering #1
Business EnglishUniversity StudentsWorking Adults
C14
201 वाक्य
10 life lessons (for a healthy mind + body) ✨
Business EnglishUniversity StudentsWorking Adults
C12
224 वाक्य
What is Statistics? A Beginner's Guide to Statistics (Data Analytics)!
Business EnglishUniversity StudentsWorking Adults
C12
41 वाक्य
Mathematical Statistics and the Theory of Probability in Casinos
Business EnglishUniversity StudentsWorking Adults
C12
190 वाक्य
Supervised Learning: Crash Course AI #2
Business EnglishUniversity StudentsWorking Adults
B23
194 वाक्य
People Are Mad They're Told to Learn
Business EnglishUniversity StudentsWorking Adults
C110
227 वाक्य
Psychology of Intelligence: Learn Anything So Fast It's Almost Unfair
Business EnglishUniversity StudentsWorking Adults
C111
102 वाक्य
World Models explained in 10min..
Business EnglishUniversity StudentsWorking Adults
C148
8 वाक्य
Neurosymbolic AI Explained
Business EnglishUniversity StudentsWorking Adults
इस पाठ के बारे में
इस पाठ में, आप मशीन लर्निंग के सरल परिचय के माध्यम से अपने अंग्रेजी बोलने का अभ्यास करेंगे। आप निर्णय वृक्ष और मशीन लर्निंग के अन्य मूल विचारों को समझेंगे। यह पाठ आपको न केवल तकनीकी शब्दावली को समझने में मदद करेगा, बल्कि आपको सामग्री की प्रवृत्ति और विभिन्न उदाहरणों का उपयोग करके अपने अंग्रेजी संचलन कौशल को विकसित करने की भी अनुमति देगा।
मुख्य शब्दावली और वाक्यांश
- मशीन लर्निंग: मशीनों के लिए डेटा से सीखने की प्रक्रिया।
- निर्णय वृक्ष: एक प्रकार का सरल मशीन लर्निंग विधि जो वर्गीकरण में मदद करता है।
- प्रशिक्षण डेटा: वह डेटा जिसका उपयोग मशीन लर्निंग मॉडल को प्रशिक्षित करने के लिए किया जाता है।
- प्रिडिक्शन: भविष्यवाणी करना।
- गति मापन: किसी व्यक्ति की गति का मापन।
- उपयोगिता: डेटा का सही उपयोग और व्याख्या करना।
- प्रवृत्ति: किसी डेटा सेट की सामान्य दिशा।
- छाया बोलना: मूल वक्ता की आवाज़ की नकल करना।
प्रैक्टिस टिप्स
इस पाठ में सम्मिलित सामग्री को समझने के लिए, आप इसे सुनते समय अंग्रेजी बोलने का अभ्यास कर सकते हैं। यहाँ कुछ सुझाव दिए गए हैं:
- छाया बोलने का अभ्यास करते समय, वीडियो की गति का ध्यान रखें। इस वीडियो की शैली हल्की और मजेदार है, जिससे आप इसे आसान पाएंगे।
- प्रत्येक उदाहरण को ध्यान से सुनें और कोशिश करें कि आप मूल वक्ता की शैली और उच्चारण का अनुसरण करें।
- मशीन लर्निंग से संबंधित शब्दों पर जोर दें। जैसे "निर्णय वृक्ष" और "प्रशिक्षण डेटा" आपके शब्दकोश में शामिल करना महत्वपूर्ण है।
- कभी-कभी वीडियो को धीमा करें ताकि आप छाया बोलावट को बेहतर ढंग से समझ सकें। यह आपको सही उच्चारण और स्वर में अभ्यास करने का अतिरिक्त समय देगा।
- वीडियो समाप्त होने के बाद, अपनी समझ को संक्षेप में लिखें। इससे आपकी लेखन और सोचने की क्षमता में सुधार होगा।
अंत में, शैडोइंग तकनीक का उपयोग करना न केवल आपके उच्चारण में सुधार करेगा बल्कि आपके मौखिक कौशल को भी विकसित करेगा।"
इस वीडियो का व्याकरण
वक्ता जिन संरचनाओं का सबसे अधिक प्रयोग करता है, वीडियो के असली शब्दों के साथ:
| संरचना | वीडियो में |
|---|---|
| Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है | is called · be fooled · should be used |
| Relative clauses who / which + उपवाक्य — व्यक्ति या वस्तु के बारे में अतिरिक्त जानकारी | someone who loves · someone who ate · people who love |
शैडोइंग तकनीक क्या है?
शैडोइंग (Shadowing) एक विज्ञान-समर्थित भाषा सीखने की तकनीक है जो मूल रूप से पेशेवर दुभाषिया प्रशिक्षण के लिए विकसित की गई थी। विधि सरल लेकिन शक्तिशाली है: आप मूल अंग्रेज़ी ऑडियो सुनते हैं और तुरंत इसे ज़ोर से दोहराते हैं — जैसे वक्ता की छाया 1-2 सेकंड की देरी से। शोध से पता चलता है कि यह उच्चारण सटीकता, स्वर, लय, जुड़ी हुई ध्वनियाँ, सुनने की समझ और बोलने की प्रवाहशीलता में काफ़ी सुधार करता है।