π Software Engineering for Games in Serious Contexts (2023)
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π¬ Tags: #SoftwareEngineering
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Best Deep Learning Courses:
https://mltut.com/best-deep-learning-courses-on-coursera/
https://mltut.com/best-deep-learning-courses-on-coursera/
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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
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π6
20x faster KMeans with Faiss!!
#KMeans uses a slow, exhaustive search to find the nearest centroids.
#Faiss uses "Inverted Index"βan 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"βan 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
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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 π
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π Ten Lessons of Building LLM Applications for Engineers
π Category: LLM APPLICATIONS
π Date: 2025-11-25 | β±οΈ 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
π Category: LLM APPLICATIONS
π Date: 2025-11-25 | β±οΈ 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
β€1