What is the output of this code?
x = 5 y = "5" print(x + int(y))
x = 5 y = "5" print(x + int(y))
Anonymous Quiz
17%
A) 55
61%
B) 10
19%
C) TypeError
3%
D) 5
❤5🤔2
❤3
How do you get user input in Python?
Anonymous Quiz
13%
A) input.get()
12%
B) get.input()
73%
C) input()
2%
D) read()
❤6
What is the output of this code?
print(10 // 3)
print(10 // 3)
Anonymous Quiz
35%
A) 3.33
54%
B) 3
2%
C) 4
9%
D) Error
Which keyword is used to define a function in Python?
Anonymous Quiz
8%
A) func
7%
B) define
8%
C) function
77%
D) def
What is the result of this code?
a = [1, 2, 3]
a.append(4) print(a)
a = [1, 2, 3]
a.append(4) print(a)
Anonymous Quiz
9%
A) [1, 2, 3]
9%
B) [4, 1, 2, 3]
82%
C) [1, 2, 3, 4]
❤7
What is the output?
x = 10
if x > 5: print("Yes") else: print("No")
x = 10
if x > 5: print("Yes") else: print("No")
Anonymous Quiz
82%
A) Yes
11%
B) No
5%
C) Error
1%
D) Nothing
❤4
❤2
Which symbol is used for writing comments in Python?
Anonymous Quiz
25%
A) //
8%
B) <!-- -->
59%
C) #
8%
D) /* */
❤9
Which data structure does not allow duplicate values?
Anonymous Quiz
8%
A) List
33%
B) Tuple
42%
C) Set
16%
D) Dictionary
😴4🔥1
Which method is used to add a key-value pair to a dictionary?
Anonymous Quiz
10%
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
11%
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
14%
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