Which method is used to add a key-value pair to a dictionary?
Anonymous Quiz
11%
A) add()
18%
B) append()
7%
C) insert()
64%
D) dict[key] = value
😱4🔥1
Which of the following is a valid list comprehension?*
Anonymous Quiz
38%
A) [x for x in range(5) if x%2==0]
24%
B) for x in range(5): if x%2==0
14%
C) x = [range(5) if x%2==0]
24%
D) list(x for x in range(5) if x%2==0)
🔥2❤1
Which structure is best for storing key-value pairs?
Anonymous Quiz
10%
A) List
12%
B) Tuple
11%
C) Set
68%
D) Dictionary
❤5🔥1😁1
What does the return keyword do in a function?
Anonymous Quiz
6%
A) Exits Python
10%
B) Prints the output
80%
C) Returns a value to the caller
5%
D) Skips execution
❤7🔥1🥰1
What is the output of this code?*
def add(x, y=2):
return x + y print(add(3))
def add(x, y=2):
return x + y print(add(3))
Anonymous Quiz
6%
2
19%
3
75%
5
❤5🔥1😁1
What is *args used for in a function?
Anonymous Quiz
8%
A) To pass a list
7%
B) To define a loop
71%
C) To pass variable number of positional arguments
15%
D) To return multiple values
❤5🔥1👌1
What does this function return?
def func(a, b):
print(a + b)
def func(a, b):
print(a + b)
Anonymous Quiz
49%
A) a + b
30%
B) None
8%
C) 0
13%
D) Error
❤2
Sometimes reality outpaces expectations in the most unexpected ways.
While global AI development seems increasingly fragmented, Sber just released Europe's largest open-source AI collection—full weights, code, and commercial rights included.
✅ No API paywalls.
✅ No usage restrictions.
✅ Just four complete model families ready to run in your private infrastructure, fine-tuned on your data, serving your specific needs.
What makes this release remarkable isn't merely the technical prowess, but the quiet confidence behind sharing it openly when others are building walls. Find out more in the article from the developers.
GigaChat Ultra Preview: 702B-parameter MoE model (36B active per token) with 128K context window. Trained from scratch, it outperforms DeepSeek V3.1 on specialized benchmarks while maintaining faster inference than previous flagships. Enterprise-ready with offline fine-tuning for secure environments.
GitHub | HuggingFace | GitVerse
GigaChat Lightning offers the opposite balance: compact yet powerful MoE architecture running on your laptop. It competes with Qwen3-4B in quality, matches the speed of Qwen3-1.7B, yet is significantly smarter and larger in parameter count.
Lightning holds its own against the best open-source models in its class, outperforms comparable models on different tasks, and delivers ultra-fast inference—making it ideal for scenarios where Ultra would be overkill and speed is critical. Plus, it features stable expert routing and a welcome bonus: 256K context support.
GitHub | Hugging Face | GitVerse
Kandinsky 5.0 brings a significant step forward in open generative models. The flagship Video Pro matches Veo 3 in visual quality and outperforms Wan 2.2-A14B, while Video Lite and Image Lite offer fast, lightweight alternatives for real-time use cases. The suite is powered by K-VAE 1.0, a high-efficiency open-source visual encoder that enables strong compression and serves as a solid base for training generative models. This stack balances performance, scalability, and practicality—whether you're building video pipelines or experimenting with multimodal generation.
GitHub | GitVerse | Hugging Face | Technical report
Audio gets its upgrade too: GigaAM-v3 delivers speech recognition model with 50% lower WER than Whisper-large-v3, trained on 700k hours of audio with punctuation/normalization for spontaneous speech.
GitHub | HuggingFace | GitVerse
Every model can be deployed on-premises, fine-tuned on your data, and used commercially. It's not just about catching up – it's about building sovereign AI infrastructure that belongs to everyone who needs it.
While global AI development seems increasingly fragmented, Sber just released Europe's largest open-source AI collection—full weights, code, and commercial rights included.
✅ No API paywalls.
✅ No usage restrictions.
✅ Just four complete model families ready to run in your private infrastructure, fine-tuned on your data, serving your specific needs.
What makes this release remarkable isn't merely the technical prowess, but the quiet confidence behind sharing it openly when others are building walls. Find out more in the article from the developers.
GigaChat Ultra Preview: 702B-parameter MoE model (36B active per token) with 128K context window. Trained from scratch, it outperforms DeepSeek V3.1 on specialized benchmarks while maintaining faster inference than previous flagships. Enterprise-ready with offline fine-tuning for secure environments.
GitHub | HuggingFace | GitVerse
GigaChat Lightning offers the opposite balance: compact yet powerful MoE architecture running on your laptop. It competes with Qwen3-4B in quality, matches the speed of Qwen3-1.7B, yet is significantly smarter and larger in parameter count.
Lightning holds its own against the best open-source models in its class, outperforms comparable models on different tasks, and delivers ultra-fast inference—making it ideal for scenarios where Ultra would be overkill and speed is critical. Plus, it features stable expert routing and a welcome bonus: 256K context support.
GitHub | Hugging Face | GitVerse
Kandinsky 5.0 brings a significant step forward in open generative models. The flagship Video Pro matches Veo 3 in visual quality and outperforms Wan 2.2-A14B, while Video Lite and Image Lite offer fast, lightweight alternatives for real-time use cases. The suite is powered by K-VAE 1.0, a high-efficiency open-source visual encoder that enables strong compression and serves as a solid base for training generative models. This stack balances performance, scalability, and practicality—whether you're building video pipelines or experimenting with multimodal generation.
GitHub | GitVerse | Hugging Face | Technical report
Audio gets its upgrade too: GigaAM-v3 delivers speech recognition model with 50% lower WER than Whisper-large-v3, trained on 700k hours of audio with punctuation/normalization for spontaneous speech.
GitHub | HuggingFace | GitVerse
Every model can be deployed on-premises, fine-tuned on your data, and used commercially. It's not just about catching up – it's about building sovereign AI infrastructure that belongs to everyone who needs it.
❤4👍4
✅ Top Python Libraries for Data Analytics & AI 🧠📊
If you're working in data science, machine learning, or AI, these Python libraries are essential. Each one plays a specific role — from handling data to building deep learning models.
🔹 1. NumPy
Core library for numerical computations.
⦁ Supports arrays, matrices, and high-performance math functions.
⦁ Foundation for most other data libraries.
import numpy as np
a = np.array()
🔹 2. Pandas
Used for data manipulation and analysis.
⦁ Works with tabular data (DataFrames).
⦁ Easily read/write CSV, Excel, SQL, etc.
import pandas as pd
df = pd.read_csv("data.csv")
🔹 3. Matplotlib & Seaborn
For data visualization.
⦁ Matplotlib: Custom plots (bar, line, scatter).
⦁ Seaborn: Statistical plots with better aesthetics.
import seaborn as sns
sns.histplot(df['age'])
🔹 4. Scikit-learn
Key ML library.
⦁ Algorithms: regression, classification, clustering.
⦁ Tools: model evaluation, pipelines.
from sklearn.linear_model import LogisticRegression
model = LogisticRegression().fit(X, y)
🔹 5. TensorFlow & Keras
For deep learning and neural networks.
⦁ TensorFlow: Low-level control, scalable.
⦁ Keras: High-level API built on TensorFlow.
from tensorflow import keras
model = keras.Sequential([...])
🔹 6. PyTorch
An alternative deep learning framework (popular in research).
⦁ Dynamic computation graphs
⦁ Easy debugging
import torch
x = torch.tensor([1.0, 2.0])
🔹 7. OpenCV
Computer vision tasks (image processing, face detection, etc).
import cv2
img = cv2.imread("image.jpg")
🔹 8. NLTK / spaCy / Transformers
For Natural Language Processing (NLP).
⦁ NLTK: Text preprocessing
⦁ spaCy: Fast NLP pipelines
⦁ HuggingFace Transformers: Use BERT, GPT, etc.
🔹 9. Statsmodels
For statistical modeling & hypothesis testing.
import statsmodels.api as sm
model = sm.OLS(y, X).fit()
🔹 10. Plotly / Bokeh
For interactive data visualizations on the web.
⦁ Great for dashboards
⦁ Export as HTML
💡 Tip:
Start with NumPy, Pandas, Matplotlib, and Scikit-learn. Master those first — they're used in 90% of analytics work.
💬 Double Tap ❤️ for more!
If you're working in data science, machine learning, or AI, these Python libraries are essential. Each one plays a specific role — from handling data to building deep learning models.
🔹 1. NumPy
Core library for numerical computations.
⦁ Supports arrays, matrices, and high-performance math functions.
⦁ Foundation for most other data libraries.
import numpy as np
a = np.array()
🔹 2. Pandas
Used for data manipulation and analysis.
⦁ Works with tabular data (DataFrames).
⦁ Easily read/write CSV, Excel, SQL, etc.
import pandas as pd
df = pd.read_csv("data.csv")
🔹 3. Matplotlib & Seaborn
For data visualization.
⦁ Matplotlib: Custom plots (bar, line, scatter).
⦁ Seaborn: Statistical plots with better aesthetics.
import seaborn as sns
sns.histplot(df['age'])
🔹 4. Scikit-learn
Key ML library.
⦁ Algorithms: regression, classification, clustering.
⦁ Tools: model evaluation, pipelines.
from sklearn.linear_model import LogisticRegression
model = LogisticRegression().fit(X, y)
🔹 5. TensorFlow & Keras
For deep learning and neural networks.
⦁ TensorFlow: Low-level control, scalable.
⦁ Keras: High-level API built on TensorFlow.
from tensorflow import keras
model = keras.Sequential([...])
🔹 6. PyTorch
An alternative deep learning framework (popular in research).
⦁ Dynamic computation graphs
⦁ Easy debugging
import torch
x = torch.tensor([1.0, 2.0])
🔹 7. OpenCV
Computer vision tasks (image processing, face detection, etc).
import cv2
img = cv2.imread("image.jpg")
🔹 8. NLTK / spaCy / Transformers
For Natural Language Processing (NLP).
⦁ NLTK: Text preprocessing
⦁ spaCy: Fast NLP pipelines
⦁ HuggingFace Transformers: Use BERT, GPT, etc.
🔹 9. Statsmodels
For statistical modeling & hypothesis testing.
import statsmodels.api as sm
model = sm.OLS(y, X).fit()
🔹 10. Plotly / Bokeh
For interactive data visualizations on the web.
⦁ Great for dashboards
⦁ Export as HTML
💡 Tip:
Start with NumPy, Pandas, Matplotlib, and Scikit-learn. Master those first — they're used in 90% of analytics work.
💬 Double Tap ❤️ for more!
❤18
What is self in Python classes?
Anonymous Quiz
19%
A. A keyword to define a class
15%
B. Refers to the class name
54%
C. Refers to the current instance of the class
11%
D. Used to create objects
❤5👍1
Which method is called when an object is created?
Anonymous Quiz
9%
A. _str_()
61%
B. _init_()
12%
C. _call_()
18%
D. _new_()
❤3👍1
What is Inheritance?
Anonymous Quiz
12%
A. Running multiple classes in parallel
5%
B. Creating objects
78%
C. Deriving a new class from an existing one
5%
D. Passing functions to classes
❤3👍2
What does Encapsulation do?
Anonymous Quiz
16%
A. Combines multiple noscripts
65%
B. Hides internal details of objects
10%
C. Displays class methods only
9%
D. Encrypts variables
❤2👍2
What is Polymorphism?
Anonymous Quiz
31%
A. One class inheriting many classes
47%
B. One function behaving differently based on input
17%
C. Multiple constructors in one class
5%
D. None of the above
❤6👍3
What does a Python generator return?
Anonymous Quiz
14%
A. List
13%
B. Tuple
37%
C. Iterator
36%
D. Function
❤5
What keyword is used to define a generator function?
Anonymous Quiz
17%
A. return
31%
B. yield
12%
C. async
40%
D. def
❤6
What is a decorator used for in Python?
Anonymous Quiz
9%
A. To repeat loops
14%
B. To handle exceptions
71%
C. To modify functions or methods
6%
D. To sort lists
❤3
Which method is used to open a file that automatically closes it?*
Anonymous Quiz
16%
A. open()
30%
B. with open()
34%
C. file.open()
20%
D. autoopen()
👍2❤1
What does _enter_ and _exit_ relate to?
Anonymous Quiz
28%
A. Error handling
23%
B. Class inheritance
32%
C. Context managers
18%
D. Abstract methods
❤4
To start with Machine Learning:
1. Learn Python
2. Practice using Google Colab
Take these free courses:
https://news.1rj.ru/str/datasciencefun/290
If you need a bit more time before diving deeper, finish the Kaggle tutorials.
At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle.
If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed.
From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit.
The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them:
https://news.1rj.ru/str/datasciencefree/259
Many different books will help you. The attached image will give you an idea of my favorite ones.
Finally, keep these three ideas in mind:
1. Start by working on solved problems so you can find help whenever you get stuck.
2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice.
3. Find a community on LinkedIn or 𝕏 and share your work. Ask questions, and help others.
During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right.
Here is the good news:
Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in space.
Focus on finding your path, and Write. More. Code.
That's how you win.✌️✌️
1. Learn Python
2. Practice using Google Colab
Take these free courses:
https://news.1rj.ru/str/datasciencefun/290
If you need a bit more time before diving deeper, finish the Kaggle tutorials.
At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle.
If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed.
From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit.
The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them:
https://news.1rj.ru/str/datasciencefree/259
Many different books will help you. The attached image will give you an idea of my favorite ones.
Finally, keep these three ideas in mind:
1. Start by working on solved problems so you can find help whenever you get stuck.
2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice.
3. Find a community on LinkedIn or 𝕏 and share your work. Ask questions, and help others.
During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right.
Here is the good news:
Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in space.
Focus on finding your path, and Write. More. Code.
That's how you win.✌️✌️
❤8👍4