Best Deep Learning Courses:
https://mltut.com/best-deep-learning-courses-on-coursera/
https://mltut.com/best-deep-learning-courses-on-coursera/
#MachineLearning #DeepLearning #BigData #Datascience #ML #HealthTech #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras
https://t.me/DataScienceM
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Create pivot tables in your Jupyter Notebook:
Here's the link to the #GitHub repo and documentation:
https://pivottable.js.org/examples/
Here's the link to the #GitHub repo and documentation:
https://pivottable.js.org/examples/
#MachineLearning #DeepLearning #BigData #Datascience #ML #HealthTech #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras
https://t.me/DataScienceM
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20x faster KMeans with Faiss!!
#KMeans uses a slow, exhaustive search to find the nearest centroids.
#Faiss uses "Inverted Index"โan optimized data structure to store and index data points for approximate neighbor search.
#KMeans uses a slow, exhaustive search to find the nearest centroids.
#Faiss uses "Inverted Index"โan 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
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# 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 ๐
#Python #ImageProcessing #ComputerVision #Pillow #OpenCV #MachineLearning #CodingInterview #DataScience #Programming #TechJobs #DeveloperTips #AI #DeepLearning #CloudComputing #Docker #BackendDevelopment #SoftwareEngineering #CareerGrowth #TechTips #Python3
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๐ Ten Lessons of Building LLM Applications for Engineers
๐ Category: LLM APPLICATIONS
๐ Date: 2025-11-25 | โฑ๏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
๐ Category: LLM APPLICATIONS
๐ Date: 2025-11-25 | โฑ๏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
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๐ฃ Rust Interview Deep Dive ๐ฆ๐
A repository for systematic preparation for Rust interviews at the middle, senior, and staff levels. ๐ผ๐
Inside 100 real questions from interviews in product and infrastructure companies, detailed analyses with code examples and scenarios of tasks that occur in production. ๐ป๐๏ธ Not "guess the program's output", but the mechanics on which real services are built. ๐ ๏ธ๐
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. ๐โก And it all starts with the basics. Ownership, borrowing, lifetimes. ๐งฑ๐ Those who want can start from scratch or at the staff level. ๐ถโโ๏ธ๐จโ๐ป
https://github.com/Develp10/rustinterviewquiestions ๐
#Rust #Programming #InterviewPrep #SoftwareEngineering #SystemsProgramming #CareerGrowth
A repository for systematic preparation for Rust interviews at the middle, senior, and staff levels. ๐ผ๐
Inside 100 real questions from interviews in product and infrastructure companies, detailed analyses with code examples and scenarios of tasks that occur in production. ๐ป๐๏ธ Not "guess the program's output", but the mechanics on which real services are built. ๐ ๏ธ๐
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. ๐โก And it all starts with the basics. Ownership, borrowing, lifetimes. ๐งฑ๐ Those who want can start from scratch or at the staff level. ๐ถโโ๏ธ๐จโ๐ป
https://github.com/Develp10/rustinterviewquiestions ๐
#Rust #Programming #InterviewPrep #SoftwareEngineering #SystemsProgramming #CareerGrowth
GitHub
GitHub - Develp10/rustinterviewquiestions: Rust ะฒะพะฟะพััั ั ัะพะฑะตัะตะดะพะฒะฐะฝะธะน
Rust ะฒะพะฟะพััั ั ัะพะฑะตัะตะดะพะฒะฐะฝะธะน . Contribute to Develp10/rustinterviewquiestions development by creating an account on GitHub.
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