Forwarded from Machine Learning
100+ LLM Interview Questions and Answers (GitHub Repo)
Anyone preparing for #AI/#ML Interviews, it is mandatory to have good knowledge related to #LLM topics.
This# repo includes 100+ LLM interview questions (with answers) spanning over LLM topics like
LLM Inference
LLM Fine-Tuning
LLM Architectures
LLM Pretraining
Prompt Engineering
etc.
๐ Github Repo - https://github.com/KalyanKS-NLP/LLM-Interview-Questions-and-Answers-Hub
https://t.me/DataScienceMโ
Anyone preparing for #AI/#ML Interviews, it is mandatory to have good knowledge related to #LLM topics.
This# repo includes 100+ LLM interview questions (with answers) spanning over LLM topics like
LLM Inference
LLM Fine-Tuning
LLM Architectures
LLM Pretraining
Prompt Engineering
etc.
https://t.me/DataScienceM
Please open Telegram to view this post
VIEW IN TELEGRAM
โค6๐3
I'm happy to announce that freeCodeCamp has launched a new certification in #Python ๐
ยป Learning the basics of programming
ยป Project development
ยป Final exam
ยป Obtaining a certificate
Everything takes place directly in the browser, without installation. This is one of the six certificates in version 10 of the Full Stack Developer training program.
Full announcement with a detailed FAQ about the certificate, the course, and the exams
Link: https://www.freecodecamp.org/news/freecodecamps-new-python-certification-is-now-live/
๐ @codeprogrammer
ยป Learning the basics of programming
ยป Project development
ยป Final exam
ยป Obtaining a certificate
Everything takes place directly in the browser, without installation. This is one of the six certificates in version 10 of the Full Stack Developer training program.
Full announcement with a detailed FAQ about the certificate, the course, and the exams
Link: https://www.freecodecamp.org/news/freecodecamps-new-python-certification-is-now-live/
Please open Telegram to view this post
VIEW IN TELEGRAM
โค10
1. What will be the output of the following code?
A. [1] then [2]
B. [1] then [1, 2]
C. [] then []
D. Raises TypeError
Correct answer: A.
2. What is printed by this code?
A. 10
B. 5
C. None
D. UnboundLocalError
Correct answer: D.
3. What is the result of executing this code?
A. [1, 2, 3, 4]
B. [4]
C. [1, 2, 3]
D. []
Correct answer: C.
4. What does the following expression evaluate to?
A. False
B. True
C. Raises ValueError
D. None
Correct answer: B.
5. What will be the output?
A. <class 'list'>
B. <class 'set'>
C. <class 'dict'>
D. <class 'tuple'>
Correct answer: C.
6. What is printed by this code?
A. (1, 2, [3])
B. (1, 2, [3, 4])
C. TypeError
D. AttributeError
Correct answer: C.
7. What does this code output?
A. [0, 1, 2]
B. [1, 2]
C. [0]
D. []
Correct answer: B.
8. What will be printed?
A. None
B. KeyError
C. 2
D. "b"
Correct answer: C.
9. What is the output?
A. True True
B. True False
C. False True
D. False False
Correct answer: A.
10. What does this code produce?
A. 0 1
B. 1 2
C. 0 0
D. StopIteration
Correct answer: A.
11. What is printed?
A. {0, 1}
B. {0: 0, 1: 1}
C. [(0,0),(1,1)]
D. Error
Correct answer: B.
12. What is the result of this comparison?
A. True True
B. False False
C. True False
D. False True
Correct answer: C.
13. What will be printed?
A. A
B. B
C. B then A
D. A then B
Correct answer: C.
14. What does this code output?
A. [1, 2, 3]
B. [3]
C. [1, 2]
D. Error
Correct answer: C.
15. What is printed?
A. <class 'list'>
B. <class 'tuple'>
C. <class 'generator'>
D. <class 'range'>
Correct answer: C.
def add_item(item, lst=None):
if lst is None:
lst = []
lst.append(item)
return lst
print(add_item(1))
print(add_item(2))
A. [1] then [2]
B. [1] then [1, 2]
C. [] then []
D. Raises TypeError
Correct answer: A.
2. What is printed by this code?
x = 10
def func():
print(x)
x = 5
func()
A. 10
B. 5
C. None
D. UnboundLocalError
Correct answer: D.
3. What is the result of executing this code?
a = [1, 2, 3]
b = a[:]
a.append(4)
print(b)
A. [1, 2, 3, 4]
B. [4]
C. [1, 2, 3]
D. []
Correct answer: C.
4. What does the following expression evaluate to?
bool("False")A. False
B. True
C. Raises ValueError
D. None
Correct answer: B.
5. What will be the output?
print(type({}))A. <class 'list'>
B. <class 'set'>
C. <class 'dict'>
D. <class 'tuple'>
Correct answer: C.
6. What is printed by this code?
x = (1, 2, [3])
x[2] += [4]
print(x)
A. (1, 2, [3])
B. (1, 2, [3, 4])
C. TypeError
D. AttributeError
Correct answer: C.
7. What does this code output?
print([i for i in range(3) if i])
A. [0, 1, 2]
B. [1, 2]
C. [0]
D. []
Correct answer: B.
8. What will be printed?
d = {"a": 1}
print(d.get("b", 2))A. None
B. KeyError
C. 2
D. "b"
Correct answer: C.
9. What is the output?
print(1 in [1, 2], 1 is 1)
A. True True
B. True False
C. False True
D. False False
Correct answer: A.
10. What does this code produce?
def gen():
for i in range(2):
yield i
g = gen()
print(next(g), next(g))
A. 0 1
B. 1 2
C. 0 0
D. StopIteration
Correct answer: A.
11. What is printed?
print({x: x*x for x in range(2)})A. {0, 1}
B. {0: 0, 1: 1}
C. [(0,0),(1,1)]
D. Error
Correct answer: B.
12. What is the result of this comparison?
print([] == [], [] is [])
A. True True
B. False False
C. True False
D. False True
Correct answer: C.
13. What will be printed?
def f():
try:
return "A"
finally:
print("B")
print(f())
A. A
B. B
C. B then A
D. A then B
Correct answer: C.
14. What does this code output?
x = [1, 2]
y = x
x = x + [3]
print(y)
A. [1, 2, 3]
B. [3]
C. [1, 2]
D. Error
Correct answer: C.
15. What is printed?
print(type(i for i in range(3)))
A. <class 'list'>
B. <class 'tuple'>
C. <class 'generator'>
D. <class 'range'>
Correct answer: C.
โค11๐1
๐ฅ NEW YEAR 2026 โ PREMIUM SCIENTIFIC PAPER WRITING OFFER ๐ฅ
Q1-Ready | Journal-Targeted | Publication-Focused
Serious researchers, PhD & MSc students, postdocs, universities, and funded startups only.
To start 2026 strong, weโre offering a limited New Year scientific writing package designed for fast-track publication, not academic busywork.
๐ฏ What We Offer (End-of-Year Special):
โ๏ธ Full Research Paper Writing โ $400
(Q1 / Q2 journalโready)
Includes:
โ Journal-targeted manuscript (Elsevier / Springer / Wiley / IEEE / MDPI)
โ IMRAD structure (IntroductionโMethodsโResultsโDiscussion)
โ Strong problem formulation & novelty framing
โ Methodology written to reviewer standards
โ Professional academic English (native-level)
โ Plagiarism-free (Turnitin <10%)
โ Ready for immediate submission
๐ Available Paper Types:
Original Research Articles
Review & Systematic Review
AI / Machine Learning Papers
Engineering & Medical Research
Health AI & Clinical Data Studies
Interdisciplinary & Applied Research
๐ง Optional Add-ons (if needed):
Journal selection & scope matching
Cover letter to editor
Reviewer response (after review)
Statistical validation & result polishing
Figure & table redesign (publication quality)
๐ Why This Is Different
We donโt โwrite generic papers.โ
We engineer publishable research.
โ๏ธ Real novelty positioning
โ๏ธ Reviewer-proof logic
โ๏ธ Data-driven arguments
โ๏ธ Aligned with current 2025โ2026 journal expectations
Many of our papers are built on real-world datasets and are already aligned with Q1 journal standards.
โณ New Year Offer โ Limited Time
Regular price: $1,500 โ $3,000
New Year 2026 price: $400
Limited slots (quality > quantity)
๐ Priority given to:
PhD / MSc students
Active researchers
Funded startups
Universities & labs
๐ฉ DM for details, samples & timelines
Contact:
@Omidyzd62
Start 2026 with a submitted paperโnot just a plan
Q1-Ready | Journal-Targeted | Publication-Focused
Serious researchers, PhD & MSc students, postdocs, universities, and funded startups only.
To start 2026 strong, weโre offering a limited New Year scientific writing package designed for fast-track publication, not academic busywork.
๐ฏ What We Offer (End-of-Year Special):
โ๏ธ Full Research Paper Writing โ $400
(Q1 / Q2 journalโready)
Includes:
โ Journal-targeted manuscript (Elsevier / Springer / Wiley / IEEE / MDPI)
โ IMRAD structure (IntroductionโMethodsโResultsโDiscussion)
โ Strong problem formulation & novelty framing
โ Methodology written to reviewer standards
โ Professional academic English (native-level)
โ Plagiarism-free (Turnitin <10%)
โ Ready for immediate submission
๐ Available Paper Types:
Original Research Articles
Review & Systematic Review
AI / Machine Learning Papers
Engineering & Medical Research
Health AI & Clinical Data Studies
Interdisciplinary & Applied Research
๐ง Optional Add-ons (if needed):
Journal selection & scope matching
Cover letter to editor
Reviewer response (after review)
Statistical validation & result polishing
Figure & table redesign (publication quality)
๐ Why This Is Different
We donโt โwrite generic papers.โ
We engineer publishable research.
โ๏ธ Real novelty positioning
โ๏ธ Reviewer-proof logic
โ๏ธ Data-driven arguments
โ๏ธ Aligned with current 2025โ2026 journal expectations
Many of our papers are built on real-world datasets and are already aligned with Q1 journal standards.
โณ New Year Offer โ Limited Time
Regular price: $1,500 โ $3,000
New Year 2026 price: $400
Limited slots (quality > quantity)
๐ Priority given to:
PhD / MSc students
Active researchers
Funded startups
Universities & labs
๐ฉ DM for details, samples & timelines
Contact:
@Omidyzd62
Start 2026 with a submitted paperโnot just a plan
โค9๐ฅ3
Machine Learning with Python pinned ยซ๐ฅ NEW YEAR 2026 โ PREMIUM SCIENTIFIC PAPER WRITING OFFER ๐ฅ Q1-Ready | Journal-Targeted | Publication-Focused Serious researchers, PhD & MSc students, postdocs, universities, and funded startups only. To start 2026 strong, weโre offering a limited New Yearโฆยป
Forwarded from Machine Learning with Python
๐Stanford just completed a must-watch for anyone serious about AI:
๐ โ๐๐ ๐ ๐ฎ๐ต๐ฑ: ๐ง๐ฟ๐ฎ๐ป๐๐ณ๐ผ๐ฟ๐บ๐ฒ๐ฟ๐ & ๐๐ฎ๐ฟ๐ด๐ฒ ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ ๐ ๐ผ๐ฑ๐ฒ๐น๐โ is now live entirely on YouTube and itโs pure gold.
If youโre building your AI career, stop scrolling.
This isnโt another surface-level overview. Itโs the clearest, most structured intro to LLMs you could follow, straight from the Stanford Autumn 2025 curriculum.
๐ ๐ง๐ผ๐ฝ๐ถ๐ฐ๐ ๐ฐ๐ผ๐๐ฒ๐ฟ๐ฒ๐ฑ ๐ถ๐ป๐ฐ๐น๐๐ฑ๐ฒ:
โข How Transformers actually work (tokenization, attention, embeddings)
โข Decoding strategies & MoEs
โข LLM finetuning (LoRA, RLHF, supervised)
โข Evaluation techniques (LLM-as-a-judge)
โข Optimization tricks (RoPE, quantization, approximations)
โข Reasoning & scaling
โข Agentic workflows (RAG, tool calling)
๐ง My workflow: I usually take the transcripts, feed them into NotebookLM, and once Iโve done the lectures, I replay them during walks or commutes. That combo works wonders for retention.
๐ฅ Watch these now:
- Lecture 1: https://lnkd.in/dDER-qyp
- Lecture 2: https://lnkd.in/dk-tGUDm
- Lecture 3: https://lnkd.in/drAPdjJY
- Lecture 4: https://lnkd.in/e_RSgMz7
- Lecture 5: https://lnkd.in/eivMA9pe
- Lecture 6: https://lnkd.in/eYwwwMXn
- Lecture 7: https://lnkd.in/eKwkEDXV
- Lecture 8: https://lnkd.in/eEWvyfyK
- Lecture 9: https://lnkd.in/euiKRGaQ
๐ Do yourself a favor for this 2026: block 2-3 hours per week / llectue and go through them.
If youโre in AI โ whether building infra, agents, or apps โ this is the foundational course you donโt want to miss.
Letโs level up.
https://t.me/CodeProgrammer๐
๐ โ๐๐ ๐ ๐ฎ๐ต๐ฑ: ๐ง๐ฟ๐ฎ๐ป๐๐ณ๐ผ๐ฟ๐บ๐ฒ๐ฟ๐ & ๐๐ฎ๐ฟ๐ด๐ฒ ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ ๐ ๐ผ๐ฑ๐ฒ๐น๐โ is now live entirely on YouTube and itโs pure gold.
If youโre building your AI career, stop scrolling.
This isnโt another surface-level overview. Itโs the clearest, most structured intro to LLMs you could follow, straight from the Stanford Autumn 2025 curriculum.
๐ ๐ง๐ผ๐ฝ๐ถ๐ฐ๐ ๐ฐ๐ผ๐๐ฒ๐ฟ๐ฒ๐ฑ ๐ถ๐ป๐ฐ๐น๐๐ฑ๐ฒ:
โข How Transformers actually work (tokenization, attention, embeddings)
โข Decoding strategies & MoEs
โข LLM finetuning (LoRA, RLHF, supervised)
โข Evaluation techniques (LLM-as-a-judge)
โข Optimization tricks (RoPE, quantization, approximations)
โข Reasoning & scaling
โข Agentic workflows (RAG, tool calling)
๐ง My workflow: I usually take the transcripts, feed them into NotebookLM, and once Iโve done the lectures, I replay them during walks or commutes. That combo works wonders for retention.
๐ฅ Watch these now:
- Lecture 1: https://lnkd.in/dDER-qyp
- Lecture 2: https://lnkd.in/dk-tGUDm
- Lecture 3: https://lnkd.in/drAPdjJY
- Lecture 4: https://lnkd.in/e_RSgMz7
- Lecture 5: https://lnkd.in/eivMA9pe
- Lecture 6: https://lnkd.in/eYwwwMXn
- Lecture 7: https://lnkd.in/eKwkEDXV
- Lecture 8: https://lnkd.in/eEWvyfyK
- Lecture 9: https://lnkd.in/euiKRGaQ
๐ Do yourself a favor for this 2026: block 2-3 hours per week / llectue and go through them.
If youโre in AI โ whether building infra, agents, or apps โ this is the foundational course you donโt want to miss.
Letโs level up.
https://t.me/CodeProgrammer
Please open Telegram to view this post
VIEW IN TELEGRAM
โค8๐1
Forwarded from Code With Python
Automatic translator in Python!
We translate a text in a few lines using
Install the library:
Example of use:
Mass translation of a list:
๐ฅ We get a mini-Google Translate right in Python: you can embed it in a chatbot, use it in notes, or automate work with the API.
๐ช @DataScience4
We translate a text in a few lines using
deep-translator. It supports dozens of languages: from English and Russian to Japanese and Arabic.Install the library:
pip install deep-translator
Example of use:
from deep_translator import GoogleTranslator
text = "Hello, how are you?"
result = GoogleTranslator(source="ru", target="en").translate(text)
print("Original:", text)
print("Translation:", result)
Mass translation of a list:
texts = ["Hello", "What's your name?", "See you later"]
for t in texts:
print("โ", GoogleTranslator(source="ru", target="es").translate(t))
๐ฅ We get a mini-Google Translate right in Python: you can embed it in a chatbot, use it in notes, or automate work with the API.
Please open Telegram to view this post
VIEW IN TELEGRAM
โค14๐1๐ฅ1
In scientific work, the most time is spent on reading articles, data, and reports.
On GitHub, there is a collection called Awesome AI for Science -ยปยปยป a catalog of AI tools for all stages of research.
Inside:
-ยป working with literature
-ยป data analysis
-ยป turning articles into posters
-ยป automating experiments
-ยป tools for biology, chemistry, physics, and other fields
GitHub: http://github.com/ai-boost/awesome-ai-for-science
The list includes Paper2Poster, MinerU, The AI Scientist, as well as articles, datasets, and frameworks.
In fact, this is a complete set of tools for AI support in scientific research.
๐ https://t.me/CodeProgrammer
On GitHub, there is a collection called Awesome AI for Science -ยปยปยป a catalog of AI tools for all stages of research.
Inside:
-ยป working with literature
-ยป data analysis
-ยป turning articles into posters
-ยป automating experiments
-ยป tools for biology, chemistry, physics, and other fields
GitHub: http://github.com/ai-boost/awesome-ai-for-science
The list includes Paper2Poster, MinerU, The AI Scientist, as well as articles, datasets, and frameworks.
In fact, this is a complete set of tools for AI support in scientific research.
Please open Telegram to view this post
VIEW IN TELEGRAM
โค6๐1๐1
AI-ML Roadmap from Scratch
๐ https://github.com/aadi1011/AI-ML-Roadmap-from-scratch?tab=readme-ov-file
https://t.me/CodeProgrammer๐
Like and Share
https://t.me/CodeProgrammer
Like and Share
Please open Telegram to view this post
VIEW IN TELEGRAM
โค10๐4
This GitHub repository is not a dump of tutorials.
Inside, there are 28 production-ready AI projects that can be used.
What's there:
Machine learning projects
โ Airbnb price forecasting
โ Air ticket cost calculator
โ Student performance tracker
AI for medicine
โ Chest disease detection
โ Heart disease prediction
โ Diabetes risk analysis
Generative AI applications
โ Live chatbot on Gemini
โ Medical assistant tool
โ Document analysis tool
Computer vision projects
โ Hand tracking system
โ Drug recognition app
โ OpenCV implementations
Data analysis dashboards
โ E-commerce analytics
โ Restaurant analytics
โ Cricket statistics tracker
And 10 more advanced projects coming soon:
โ Deepfake detection
โ Brain tumor classification
โ Driver drowsiness alert system
This is not just a collection of code files.
These are end-to-end working applications.
View the repository๐ฒ
https://github.com/KalyanM45/AI-Project-Gallery
๐ @codeprogrammer
Like and Share
Inside, there are 28 production-ready AI projects that can be used.
What's there:
Machine learning projects
โ Airbnb price forecasting
โ Air ticket cost calculator
โ Student performance tracker
AI for medicine
โ Chest disease detection
โ Heart disease prediction
โ Diabetes risk analysis
Generative AI applications
โ Live chatbot on Gemini
โ Medical assistant tool
โ Document analysis tool
Computer vision projects
โ Hand tracking system
โ Drug recognition app
โ OpenCV implementations
Data analysis dashboards
โ E-commerce analytics
โ Restaurant analytics
โ Cricket statistics tracker
And 10 more advanced projects coming soon:
โ Deepfake detection
โ Brain tumor classification
โ Driver drowsiness alert system
This is not just a collection of code files.
These are end-to-end working applications.
View the repository
https://github.com/KalyanM45/AI-Project-Gallery
Like and Share
Please open Telegram to view this post
VIEW IN TELEGRAM
โค11๐2
transformer Q&A.pdf
1.3 MB
๐๐๐ซ๐โ๐ฌ ๐ ๐ช๐ฎ๐ข๐๐ค ๐๐ซ๐๐๐ค๐๐จ๐ฐ๐ง ๐๐ซ๐จ๐ฆ ๐ญ๐ก๐ ๐ญ๐จ๐ฉ ๐๐ซ๐๐ง๐ฌ๐๐จ๐ซ๐ฆ๐๐ซ๐ฌ ๐๐ง๐ญ๐๐ซ๐ฏ๐ข๐๐ฐ ๐๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง๐ฌ ๐ฅ๐โฃโฃ
โฃโฃ
โ ๐๐ฉ๐ข๐ต ๐ช๐ด ๐ข ๐๐ณ๐ข๐ฏ๐ด๐ง๐ฐ๐ณ๐ฎ๐ฆ๐ณ ๐ข๐ฏ๐ฅ ๐ธ๐ฉ๐บ ๐ธ๐ข๐ด ๐ช๐ต ๐ช๐ฏ๐ต๐ณ๐ฐ๐ฅ๐ถ๐ค๐ฆ๐ฅ?โฃโฃ
๐๐ต ๐ด๐ฐ๐ญ๐ท๐ฆ๐ฅ ๐ต๐ฉ๐ฆ ๐ญ๐ช๐ฎ๐ช๐ต๐ข๐ต๐ช๐ฐ๐ฏ๐ด ๐ฐ๐ง ๐๐๐๐ด & ๐๐๐๐๐ด ๐ฃ๐บ ๐ถ๐ด๐ช๐ฏ๐จ ๐ด๐ฆ๐ญ๐ง-๐ข๐ต๐ต๐ฆ๐ฏ๐ต๐ช๐ฐ๐ฏ, ๐ฆ๐ฏ๐ข๐ฃ๐ญ๐ช๐ฏ๐จ ๐ฑ๐ข๐ณ๐ข๐ญ๐ญ๐ฆ๐ญ ๐ฑ๐ณ๐ฐ๐ค๐ฆ๐ด๐ด๐ช๐ฏ๐จ ๐ข๐ฏ๐ฅ ๐ค๐ข๐ฑ๐ต๐ถ๐ณ๐ช๐ฏ๐จ ๐ญ๐ฐ๐ฏ๐จ-๐ณ๐ข๐ฏ๐จ๐ฆ ๐ฅ๐ฆ๐ฑ๐ฆ๐ฏ๐ฅ๐ฆ๐ฏ๐ค๐ช๐ฆ๐ด ๐ญ๐ช๐ฌ๐ฆ ๐ฏ๐ฆ๐ท๐ฆ๐ณ ๐ฃ๐ฆ๐ง๐ฐ๐ณ๐ฆ!โฃโฃ
โฃโฃ
โ ๐๐ฆ๐ญ๐ง-๐๐ต๐ต๐ฆ๐ฏ๐ต๐ช๐ฐ๐ฏ โ ๐๐ฉ๐ฆ ๐ฎ๐ข๐จ๐ช๐ค ๐ฃ๐ฆ๐ฉ๐ช๐ฏ๐ฅ ๐ช๐ตโฃโฃ
๐๐ท๐ฆ๐ณ๐บ ๐ธ๐ฐ๐ณ๐ฅ ๐ถ๐ฏ๐ฅ๐ฆ๐ณ๐ด๐ต๐ข๐ฏ๐ฅ๐ด ๐ช๐ต๐ด ๐ค๐ฐ๐ฏ๐ต๐ฆ๐น๐ต ๐ช๐ฏ ๐ณ๐ฆ๐ญ๐ข๐ต๐ช๐ฐ๐ฏ ๐ต๐ฐ ๐ฐ๐ต๐ฉ๐ฆ๐ณ๐ดโ๐ฎ๐ข๐ฌ๐ช๐ฏ๐จ ๐ฆ๐ฎ๐ฃ๐ฆ๐ฅ๐ฅ๐ช๐ฏ๐จ๐ด ๐ด๐ฎ๐ข๐ณ๐ต๐ฆ๐ณ ๐ข๐ฏ๐ฅ ๐ฎ๐ฐ๐ฅ๐ฆ๐ญ๐ด ๐ฎ๐ฐ๐ณ๐ฆ ๐ค๐ฐ๐ฏ๐ต๐ฆ๐น๐ต-๐ข๐ธ๐ข๐ณ๐ฆ.โฃโฃ
โฃโฃ
โ ๐๐ถ๐ญ๐ต๐ช-๐๐ฆ๐ข๐ฅ ๐๐ต๐ต๐ฆ๐ฏ๐ต๐ช๐ฐ๐ฏ โ ๐๐ฆ๐ฆ๐ช๐ฏ๐จ ๐ง๐ณ๐ฐ๐ฎ ๐ฎ๐ถ๐ญ๐ต๐ช๐ฑ๐ญ๐ฆ ๐ข๐ฏ๐จ๐ญ๐ฆ๐ดโฃโฃ
๐๐ช๐ง๐ง๐ฆ๐ณ๐ฆ๐ฏ๐ต ๐ข๐ต๐ต๐ฆ๐ฏ๐ต๐ช๐ฐ๐ฏ ๐ฉ๐ฆ๐ข๐ฅ๐ด ๐ง๐ฐ๐ค๐ถ๐ด ๐ฐ๐ฏ ๐ฅ๐ช๐ง๐ง๐ฆ๐ณ๐ฆ๐ฏ๐ต ๐ณ๐ฆ๐ญ๐ข๐ต๐ช๐ฐ๐ฏ๐ด๐ฉ๐ช๐ฑ๐ด ๐ช๐ฏ ๐ต๐ฉ๐ฆ ๐ฅ๐ข๐ต๐ข. ๐๐ตโ๐ด ๐ญ๐ช๐ฌ๐ฆ ๐ฉ๐ข๐ท๐ช๐ฏ๐จ ๐ฎ๐ถ๐ญ๐ต๐ช๐ฑ๐ญ๐ฆ ๐ฆ๐น๐ฑ๐ฆ๐ณ๐ต๐ด ๐ข๐ฏ๐ข๐ญ๐บ๐ป๐ฆ ๐ต๐ฉ๐ฆ ๐ด๐ข๐ฎ๐ฆ ๐ช๐ฏ๐ง๐ฐ๐ณ๐ฎ๐ข๐ต๐ช๐ฐ๐ฏ!โฃโฃ
โฃโฃ
โ ๐๐ฐ๐ด๐ช๐ต๐ช๐ฐ๐ฏ๐ข๐ญ ๐๐ฏ๐ค๐ฐ๐ฅ๐ช๐ฏ๐จ โ ๐๐ฆ๐ข๐ค๐ฉ๐ช๐ฏ๐จ ๐ต๐ฉ๐ฆ ๐ฎ๐ฐ๐ฅ๐ฆ๐ญ ๐ฐ๐ณ๐ฅ๐ฆ๐ณ ๐ฎ๐ข๐ต๐ต๐ฆ๐ณ๐ดโฃโฃ
๐๐ช๐ฏ๐ค๐ฆ ๐๐ณ๐ข๐ฏ๐ด๐ง๐ฐ๐ณ๐ฎ๐ฆ๐ณ๐ด ๐ฅ๐ฐ๐ฏโ๐ต ๐ฑ๐ณ๐ฐ๐ค๐ฆ๐ด๐ด ๐ฅ๐ข๐ต๐ข ๐ด๐ฆ๐ฒ๐ถ๐ฆ๐ฏ๐ต๐ช๐ข๐ญ๐ญ๐บ, ๐ต๐ฉ๐ช๐ด ๐ต๐ณ๐ช๐ค๐ฌ ๐ฆ๐ฏ๐ด๐ถ๐ณ๐ฆ๐ด ๐ต๐ฉ๐ฆ๐บ โ๐ฌ๐ฏ๐ฐ๐ธโ ๐ต๐ฉ๐ฆ ๐ฑ๐ฐ๐ด๐ช๐ต๐ช๐ฐ๐ฏ ๐ฐ๐ง ๐ฆ๐ข๐ค๐ฉ ๐ต๐ฐ๐ฌ๐ฆ๐ฏ.โฃโฃ
โฃโฃ
โ ๐๐ข๐บ๐ฆ๐ณ ๐๐ฐ๐ณ๐ฎ๐ข๐ญ๐ช๐ป๐ข๐ต๐ช๐ฐ๐ฏ โ ๐๐ต๐ข๐ฃ๐ช๐ญ๐ช๐ป๐ช๐ฏ๐จ ๐ต๐ฉ๐ฆ ๐ญ๐ฆ๐ข๐ณ๐ฏ๐ช๐ฏ๐จ ๐ฑ๐ณ๐ฐ๐ค๐ฆ๐ด๐ดโฃโฃ
๐๐ต ๐ด๐ฑ๐ฆ๐ฆ๐ฅ๐ด ๐ถ๐ฑ ๐ต๐ณ๐ข๐ช๐ฏ๐ช๐ฏ๐จ ๐ข๐ฏ๐ฅ ๐ข๐ท๐ฐ๐ช๐ฅ๐ด ๐ท๐ข๐ฏ๐ช๐ด๐ฉ๐ช๐ฏ๐จ ๐จ๐ณ๐ข๐ฅ๐ช๐ฆ๐ฏ๐ต๐ด, ๐ญ๐ฆ๐ต๐ต๐ช๐ฏ๐จ ๐ฎ๐ฐ๐ฅ๐ฆ๐ญ๐ด ๐จ๐ฐ ๐ฅ๐ฆ๐ฆ๐ฑ๐ฆ๐ณ ๐ข๐ฏ๐ฅ ๐ญ๐ฆ๐ข๐ณ๐ฏ ๐ฃ๐ฆ๐ต๐ต๐ฆ๐ณ.โฃโฃ
๐ @codeprogrammer
Like and Share๐
โฃโฃ
โ ๐๐ฉ๐ข๐ต ๐ช๐ด ๐ข ๐๐ณ๐ข๐ฏ๐ด๐ง๐ฐ๐ณ๐ฎ๐ฆ๐ณ ๐ข๐ฏ๐ฅ ๐ธ๐ฉ๐บ ๐ธ๐ข๐ด ๐ช๐ต ๐ช๐ฏ๐ต๐ณ๐ฐ๐ฅ๐ถ๐ค๐ฆ๐ฅ?โฃโฃ
๐๐ต ๐ด๐ฐ๐ญ๐ท๐ฆ๐ฅ ๐ต๐ฉ๐ฆ ๐ญ๐ช๐ฎ๐ช๐ต๐ข๐ต๐ช๐ฐ๐ฏ๐ด ๐ฐ๐ง ๐๐๐๐ด & ๐๐๐๐๐ด ๐ฃ๐บ ๐ถ๐ด๐ช๐ฏ๐จ ๐ด๐ฆ๐ญ๐ง-๐ข๐ต๐ต๐ฆ๐ฏ๐ต๐ช๐ฐ๐ฏ, ๐ฆ๐ฏ๐ข๐ฃ๐ญ๐ช๐ฏ๐จ ๐ฑ๐ข๐ณ๐ข๐ญ๐ญ๐ฆ๐ญ ๐ฑ๐ณ๐ฐ๐ค๐ฆ๐ด๐ด๐ช๐ฏ๐จ ๐ข๐ฏ๐ฅ ๐ค๐ข๐ฑ๐ต๐ถ๐ณ๐ช๐ฏ๐จ ๐ญ๐ฐ๐ฏ๐จ-๐ณ๐ข๐ฏ๐จ๐ฆ ๐ฅ๐ฆ๐ฑ๐ฆ๐ฏ๐ฅ๐ฆ๐ฏ๐ค๐ช๐ฆ๐ด ๐ญ๐ช๐ฌ๐ฆ ๐ฏ๐ฆ๐ท๐ฆ๐ณ ๐ฃ๐ฆ๐ง๐ฐ๐ณ๐ฆ!โฃโฃ
โฃโฃ
โ ๐๐ฆ๐ญ๐ง-๐๐ต๐ต๐ฆ๐ฏ๐ต๐ช๐ฐ๐ฏ โ ๐๐ฉ๐ฆ ๐ฎ๐ข๐จ๐ช๐ค ๐ฃ๐ฆ๐ฉ๐ช๐ฏ๐ฅ ๐ช๐ตโฃโฃ
๐๐ท๐ฆ๐ณ๐บ ๐ธ๐ฐ๐ณ๐ฅ ๐ถ๐ฏ๐ฅ๐ฆ๐ณ๐ด๐ต๐ข๐ฏ๐ฅ๐ด ๐ช๐ต๐ด ๐ค๐ฐ๐ฏ๐ต๐ฆ๐น๐ต ๐ช๐ฏ ๐ณ๐ฆ๐ญ๐ข๐ต๐ช๐ฐ๐ฏ ๐ต๐ฐ ๐ฐ๐ต๐ฉ๐ฆ๐ณ๐ดโ๐ฎ๐ข๐ฌ๐ช๐ฏ๐จ ๐ฆ๐ฎ๐ฃ๐ฆ๐ฅ๐ฅ๐ช๐ฏ๐จ๐ด ๐ด๐ฎ๐ข๐ณ๐ต๐ฆ๐ณ ๐ข๐ฏ๐ฅ ๐ฎ๐ฐ๐ฅ๐ฆ๐ญ๐ด ๐ฎ๐ฐ๐ณ๐ฆ ๐ค๐ฐ๐ฏ๐ต๐ฆ๐น๐ต-๐ข๐ธ๐ข๐ณ๐ฆ.โฃโฃ
โฃโฃ
โ ๐๐ถ๐ญ๐ต๐ช-๐๐ฆ๐ข๐ฅ ๐๐ต๐ต๐ฆ๐ฏ๐ต๐ช๐ฐ๐ฏ โ ๐๐ฆ๐ฆ๐ช๐ฏ๐จ ๐ง๐ณ๐ฐ๐ฎ ๐ฎ๐ถ๐ญ๐ต๐ช๐ฑ๐ญ๐ฆ ๐ข๐ฏ๐จ๐ญ๐ฆ๐ดโฃโฃ
๐๐ช๐ง๐ง๐ฆ๐ณ๐ฆ๐ฏ๐ต ๐ข๐ต๐ต๐ฆ๐ฏ๐ต๐ช๐ฐ๐ฏ ๐ฉ๐ฆ๐ข๐ฅ๐ด ๐ง๐ฐ๐ค๐ถ๐ด ๐ฐ๐ฏ ๐ฅ๐ช๐ง๐ง๐ฆ๐ณ๐ฆ๐ฏ๐ต ๐ณ๐ฆ๐ญ๐ข๐ต๐ช๐ฐ๐ฏ๐ด๐ฉ๐ช๐ฑ๐ด ๐ช๐ฏ ๐ต๐ฉ๐ฆ ๐ฅ๐ข๐ต๐ข. ๐๐ตโ๐ด ๐ญ๐ช๐ฌ๐ฆ ๐ฉ๐ข๐ท๐ช๐ฏ๐จ ๐ฎ๐ถ๐ญ๐ต๐ช๐ฑ๐ญ๐ฆ ๐ฆ๐น๐ฑ๐ฆ๐ณ๐ต๐ด ๐ข๐ฏ๐ข๐ญ๐บ๐ป๐ฆ ๐ต๐ฉ๐ฆ ๐ด๐ข๐ฎ๐ฆ ๐ช๐ฏ๐ง๐ฐ๐ณ๐ฎ๐ข๐ต๐ช๐ฐ๐ฏ!โฃโฃ
โฃโฃ
โ ๐๐ฐ๐ด๐ช๐ต๐ช๐ฐ๐ฏ๐ข๐ญ ๐๐ฏ๐ค๐ฐ๐ฅ๐ช๐ฏ๐จ โ ๐๐ฆ๐ข๐ค๐ฉ๐ช๐ฏ๐จ ๐ต๐ฉ๐ฆ ๐ฎ๐ฐ๐ฅ๐ฆ๐ญ ๐ฐ๐ณ๐ฅ๐ฆ๐ณ ๐ฎ๐ข๐ต๐ต๐ฆ๐ณ๐ดโฃโฃ
๐๐ช๐ฏ๐ค๐ฆ ๐๐ณ๐ข๐ฏ๐ด๐ง๐ฐ๐ณ๐ฎ๐ฆ๐ณ๐ด ๐ฅ๐ฐ๐ฏโ๐ต ๐ฑ๐ณ๐ฐ๐ค๐ฆ๐ด๐ด ๐ฅ๐ข๐ต๐ข ๐ด๐ฆ๐ฒ๐ถ๐ฆ๐ฏ๐ต๐ช๐ข๐ญ๐ญ๐บ, ๐ต๐ฉ๐ช๐ด ๐ต๐ณ๐ช๐ค๐ฌ ๐ฆ๐ฏ๐ด๐ถ๐ณ๐ฆ๐ด ๐ต๐ฉ๐ฆ๐บ โ๐ฌ๐ฏ๐ฐ๐ธโ ๐ต๐ฉ๐ฆ ๐ฑ๐ฐ๐ด๐ช๐ต๐ช๐ฐ๐ฏ ๐ฐ๐ง ๐ฆ๐ข๐ค๐ฉ ๐ต๐ฐ๐ฌ๐ฆ๐ฏ.โฃโฃ
โฃโฃ
โ ๐๐ข๐บ๐ฆ๐ณ ๐๐ฐ๐ณ๐ฎ๐ข๐ญ๐ช๐ป๐ข๐ต๐ช๐ฐ๐ฏ โ ๐๐ต๐ข๐ฃ๐ช๐ญ๐ช๐ป๐ช๐ฏ๐จ ๐ต๐ฉ๐ฆ ๐ญ๐ฆ๐ข๐ณ๐ฏ๐ช๐ฏ๐จ ๐ฑ๐ณ๐ฐ๐ค๐ฆ๐ด๐ดโฃโฃ
๐๐ต ๐ด๐ฑ๐ฆ๐ฆ๐ฅ๐ด ๐ถ๐ฑ ๐ต๐ณ๐ข๐ช๐ฏ๐ช๐ฏ๐จ ๐ข๐ฏ๐ฅ ๐ข๐ท๐ฐ๐ช๐ฅ๐ด ๐ท๐ข๐ฏ๐ช๐ด๐ฉ๐ช๐ฏ๐จ ๐จ๐ณ๐ข๐ฅ๐ช๐ฆ๐ฏ๐ต๐ด, ๐ญ๐ฆ๐ต๐ต๐ช๐ฏ๐จ ๐ฎ๐ฐ๐ฅ๐ฆ๐ญ๐ด ๐จ๐ฐ ๐ฅ๐ฆ๐ฆ๐ฑ๐ฆ๐ณ ๐ข๐ฏ๐ฅ ๐ญ๐ฆ๐ข๐ณ๐ฏ ๐ฃ๐ฆ๐ต๐ต๐ฆ๐ณ.โฃโฃ
Like and Share
Please open Telegram to view this post
VIEW IN TELEGRAM
โค6๐3๐1๐1
Forwarded from Code With Python
A cheat sheet about functions and techniques in Python: shows useful built-in functions, working with iterators, strings, and collections, as well as popular tricks with unpacking, zip, enumerate, map, filter, and dictionaries
@DataScience4
@DataScience4
โค4
Forwarded from ADMINOTEKA
This media is not supported in your browser
VIEW IN TELEGRAM
ะ ะะพะฒัะน ะณะพะด ะฒัะต ะผะตััั ะดะพะปะถะฝั ัะฑัะฒะฐัััั! ๐
ะั ะฒะพั, ะฝะฐะฟัะธะผะตั, ะผะตััะฐะตะผ ะฟะพะปััะธัั ะผะฝะพะณะพ ะผะฝะพะณะพ ะฑัััะพะฒ ะฝะฐ ะฝะฐั ะปัะฑะธะผัะน ะบะฐะฝะฐะป
ะ ัั ะพ ััะผ ะผะตััะฐะตัั? ะะฐะน-ะบะฐ ัะณะฐะดะฐัโฆ ะกะบะพัะตะต ะฒัะตะณะพ ะพ $20 ัะตะบะปะฐะผะฝะพะณะพ ะฑัะดะถะตัะฐ ะฒ ะะดะผะธะฝะพัะตะบะต!
ะัะธะฝััั ััะฐััะธะต ะฒ ัะพะทัะณัััะต ะพัะตะฝั ะฟัะพััะพ:
โ๏ธ ะะพะดะฟะธัะฐัััั ะฝะฐ ADMINOTEKA
โ๏ธ ะะฐะฑัััะธัั ะฝะฐั ะบะฐะฝะฐะป
ะ ะฐะฝะดะพะผะฝะพ ะฒัะฑะตัะตะผ 20 ะฟะพะฑะตะดะธัะตะปะตะน ััะตะดะธ ะฑัััะตัะพะฒ, ะบะพัะพััะต ะฟะพะปััะฐั $20 ะฝะฐ ัะฒะพะน ะฑะฐะปะฐะฝั
ะ ะตะทัะปััะฐัั 6 ัะฝะฒะฐัั ะฒ 13:00 ะฟะพ ะผะพัะบะพะฒัะบะพะผั ะฒัะตะผะตะฝะธ. ะฃะดะฐัะธ!๐พ
ะั ะฒะพั, ะฝะฐะฟัะธะผะตั, ะผะตััะฐะตะผ ะฟะพะปััะธัั ะผะฝะพะณะพ ะผะฝะพะณะพ ะฑัััะพะฒ ะฝะฐ ะฝะฐั ะปัะฑะธะผัะน ะบะฐะฝะฐะป
ะ ัั ะพ ััะผ ะผะตััะฐะตัั? ะะฐะน-ะบะฐ ัะณะฐะดะฐัโฆ ะกะบะพัะตะต ะฒัะตะณะพ ะพ $20 ัะตะบะปะฐะผะฝะพะณะพ ะฑัะดะถะตัะฐ ะฒ ะะดะผะธะฝะพัะตะบะต!
ะัะธะฝััั ััะฐััะธะต ะฒ ัะพะทัะณัััะต ะพัะตะฝั ะฟัะพััะพ:
ะ ะฐะฝะดะพะผะฝะพ ะฒัะฑะตัะตะผ 20 ะฟะพะฑะตะดะธัะตะปะตะน ััะตะดะธ ะฑัััะตัะพะฒ, ะบะพัะพััะต ะฟะพะปััะฐั $20 ะฝะฐ ัะฒะพะน ะฑะฐะปะฐะฝั
ะ ะตะทัะปััะฐัั 6 ัะฝะฒะฐัั ะฒ 13:00 ะฟะพ ะผะพัะบะพะฒัะบะพะผั ะฒัะตะผะตะฝะธ. ะฃะดะฐัะธ!
Please open Telegram to view this post
VIEW IN TELEGRAM
โค4๐ฅ1