DS Interview.pdf
1.6 MB
Data Science Interview questions
#DeepLearning #AI #MachineLearning #NeuralNetworks #DataScience #DataAnalysis #LLM #InterviewQuestions
https://news.1rj.ru/str/CodeProgrammer
#DeepLearning #AI #MachineLearning #NeuralNetworks #DataScience #DataAnalysis #LLM #InterviewQuestions
https://news.1rj.ru/str/CodeProgrammer
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Do you see yourself as a programmer, researcher, or engineer?
Anonymous Poll
46%
Programmer
22%
Researcher
32%
Engineer
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by [@codeprogrammer]
---
🏛️ MIT OpenCourseWare – Machine Learning
---
#MachineLearning #LearnML #DataScience #AI
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Machine Learning | Google for Developers
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ML engineers, take note: structured ML reference guide
Link: https://ml-cheatsheet.readthedocs.io/en/latest/
There are no courses, no redundant theory, and no lengthy lectures here, but there are clear formulas, algorithms, the logic of ML pipelines, and a neatly structured knowledge base.
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Link: https://ml-cheatsheet.readthedocs.io/en/latest/
There are no courses, no redundant theory, and no lengthy lectures here, but there are clear formulas, algorithms, the logic of ML pipelines, and a neatly structured knowledge base.
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Trackers v2.1.0 has been released. In this release, support for ByteTrack has been added - a fast tracking-by-detection algorithm that maintains stable IDs even during occlusions.
Link: https://github.com/roboflow/trackers
Trackers allows you to combine normal multi-object tracking with your detection or segmentation model.
👉 @codeprogrammer
Link: https://github.com/roboflow/trackers
pip install trackers
Trackers allows you to combine normal multi-object tracking with your detection or segmentation model.
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And most of the content is #free: → https://learn.github.com
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Here's the full path I would recommend to build production-grade AI agents this year:
▪️ a foundation in Python and algorithms
▪️ mathematics and the basics of ML
▪️ transformers and LLMs
▪️ prompt engineering
▪️ memory and RAG
▪️ tools and integrations
▪️ frameworks like LangChain or CrewAI
▪️ multi-agent systems
▪️ testing, deployment, and security
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Want to turn any image into ASCII art? It's not magic, just simple brightness processing.
It's tedious and stupid to do it manually
img = [
[255, 0, 0],
[0, 255, 0]
]
# Now we need to pick a symbol for each pixel...
# What a hassle.Problem:
Manually selecting symbols by brightness is a pain. We need to automate the conversion of grayscale to symbols.
from PIL import Image
def image_to_ascii(path, width=100):
img = Image.open(path)
aspect = img.height / img.width
height = int(width * aspect * 0.55)
img = img.resize((width, height)).convert('L')
ascii_chars = '@%#*+=-:. '
pixels = img.getdata()
ascii_art = '\n'.join(
ascii_chars[pixel * (len(ascii_chars) - 1) // 255]
for pixel in pixels
)
lines = [ascii_art[i:i+width] for i in range(0, len(ascii_art), width)]
return '\n'.join(lines)
print(image_to_ascii('cat.jpg'))
How it works:
convert('L') converts the image to grayscale
Each pixel (0-255) is assigned a symbol from the set
The darker the pixel, the "denser" the symbol (e.g., '@'), the lighter - the "weaker" (space)
Let's write a converter with customizable palette:
class AsciiConverter:
PALETTES = {
'default': '@%#*+=-:. ',
'blocks': '█rayed ',
'detailed': '$@B%8&WM#*oahkbdpqwmZO0QLCJUYXzcvunxrjft/\\|()1{}[]?-_+~<>i!lI;:,"^`\'. '
}
def __init__(self, palette_name='default'):
if palette_name not in self.PALETTES:
raise ValueError(f'Нет такой палитры, идиот. Выбери из: {list(self.PALETTES.keys())}')
self.chars = self.PALETTES[palette_name]
def convert(self, image_path, width=80):
# ... code to convert using self.chars ...
return ascii_result
Try specifying a non-existent palette - you'll get a clear error.
Key parameters:
🔵 Width - determines the size of the final ASCII art🔵 Character palette - affects the detail and style🔵 Aspect ratio - important for correct display🔵 Inversion - you can invert the brightness for a dark background
Important:
ASCII art isn't just a fun thing. It's used to visualize data in the console, create creative logs, and even "hide" information in plain sight.
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Numpy_Cheat_Sheet.pdf
4.8 MB
NumPy Cheat Sheet: Data Analysis in Python
This #Python cheat sheet is a quick reference for #NumPy beginners.
Learn more:
https://www.datacamp.com/cheat-sheet/numpy-cheat-sheet-data-analysis-in-python
https://news.1rj.ru/str/DataAnalyticsX
This #Python cheat sheet is a quick reference for #NumPy beginners.
Learn more:
https://www.datacamp.com/cheat-sheet/numpy-cheat-sheet-data-analysis-in-python
https://news.1rj.ru/str/DataAnalyticsX
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