Machine Learning
39.7K subscribers
3.57K photos
25 videos
46 files
589 links
Real Machine Learning โ€” simple, practical, and built on experience.
Learn step by step with clear explanations and working code.

Admin: @HusseinSheikho || @Hussein_Sheikho
Download Telegram
20x faster KMeans with Faiss!!

#KMeans uses a slow, exhaustive search to find the nearest centroids.

#Faiss uses "Inverted Index"โ€”an optimized data structure to store and index data points for approximate neighbor search.

#MachineLearning #DeepLearning #BigData #Datascience #ML #HealthTech #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras

https://t.me/DataScienceM
๐Ÿ‘6โค2๐Ÿ”ฅ1
# Real-World Case Study: E-commerce Product Pipeline
import boto3
from PIL import Image
import io

def process_product_image(s3_bucket, s3_key):
# 1. Download from S3
s3 = boto3.client('s3')
response = s3.get_object(Bucket=s3_bucket, Key=s3_key)
img = Image.open(io.BytesIO(response['Body'].read()))

# 2. Standardize dimensions
img = img.convert("RGB")
img = img.resize((1200, 1200), Image.LANCZOS)

# 3. Remove background (simplified)
# In practice: use rembg or AWS Rekognition
img = remove_background(img)

# 4. Generate variants
variants = {
"web": img.resize((800, 800)),
"mobile": img.resize((400, 400)),
"thumbnail": img.resize((100, 100))
}

# 5. Upload to CDN
for name, variant in variants.items():
buffer = io.BytesIO()
variant.save(buffer, "JPEG", quality=95)
s3.upload_fileobj(
buffer,
"cdn-bucket",
f"products/{s3_key.split('/')[-1].split('.')[0]}_{name}.jpg",
ExtraArgs={'ContentType': 'image/jpeg', 'CacheControl': 'max-age=31536000'}
)

# 6. Generate WebP version
webp_buffer = io.BytesIO()
img.save(webp_buffer, "WEBP", quality=85)
s3.upload_fileobj(webp_buffer, "cdn-bucket", f"products/{s3_key.split('/')[-1].split('.')[0]}.webp")

process_product_image("user-uploads", "products/summer_dress.jpg")


By: @DataScienceM ๐Ÿ‘

#Python #ImageProcessing #ComputerVision #Pillow #OpenCV #MachineLearning #CodingInterview #DataScience #Programming #TechJobs #DeveloperTips #AI #DeepLearning #CloudComputing #Docker #BackendDevelopment #SoftwareEngineering #CareerGrowth #TechTips #Python3
โค1
๐Ÿ“Œ Ten Lessons of Building LLM Applications for Engineers

๐Ÿ—‚ Category: LLM APPLICATIONS

๐Ÿ•’ Date: 2025-11-25 | โฑ๏ธ Read time: 22 min read

Drawing from two years of hands-on experience, this article outlines ten essential lessons for engineers building applications with Large Language Models. Gain practical insights and field-tested advice on structuring projects, optimizing workflows, and implementing effective evaluation strategies to successfully navigate the complexities of LLM development. This guide is for engineers looking to move from theory to production-ready applications.

#LLM #AIdevelopment #SoftwareEngineering #MLOps
โค1
๐Ÿ‘ฃ Rust Interview Deep Dive ๐Ÿฆ€๐Ÿ”

A repository for systematic preparation for Rust interviews at the middle, senior, and staff levels. ๐Ÿ’ผ๐Ÿ“š

Inside 100 real questions from interviews in product and infrastructure companies, detailed analyses with code examples and scenarios of tasks that occur in production. ๐Ÿ’ป๐Ÿ—๏ธ Not "guess the program's output", but the mechanics on which real services are built. ๐Ÿ› ๏ธ๐Ÿš€

Here are lock-free structures, self-referential types in async, FFI with tensor libraries, correct Send on guards via await, memory ordering under loom, soundness of custom collections. ๐Ÿ”’โšก And it all starts with the basics. Ownership, borrowing, lifetimes. ๐Ÿงฑ๐Ÿ”„ Those who want can start from scratch or at the staff level. ๐Ÿšถโ€โ™‚๏ธ๐Ÿ‘จโ€๐Ÿ’ป

https://github.com/Develp10/rustinterviewquiestions ๐Ÿ”—

#Rust #Programming #InterviewPrep #SoftwareEngineering #SystemsProgramming #CareerGrowth
โค3