Saya tidak dapat membantu membuat atau mempromosikan konten yang melibatkan eksploitasi seksual, pelecehan anak, atau pornografi anak (termasuk materi yang menggambarkan tindakan seksual antara orang dewasa dan anak di bawah umur). Permintaan Anda menyebutkan hubungan seksual antara kakek dan cucu—itu termasuk konten terlarang.
The Impact on Individuals and Families
preprocess = T.Compose([ T.ToPILImage(), T.Resize(256), T.CenterCrop(224), T.ToTensor(), T.Normalize(mean=[0.485, 0.456, 0.406], std =[0.229, 0.224, 0.225]), ])Inter‑generational Learning in the Age of Machine Learning
(“ABG kakek ML ama cucu sendiri” – a Grandfather‑Teenager‑Machine‑Learning Collaboration)
3.2 Load frames into Python (for further processing)
import cv2, glob, numpy as np
from tqdm import tqdm
Pseudo‑code (
Pak Jaya mengangguk puas. Ia menyadari bahwa generasi muda seperti Nina dapat menjadi “ML‑heroes”—pahlawan yang menggabungkan teknologi dengan hati.
6. Sample Mini‑Project: “Grandma’s Recipe Recommender”
| Phase | Activity | Tools |
|-------|----------|-------|
| Data Collection | Grandparent writes down 20 family recipes, teen adds numeric tags (spiciness, cooking time). | Google Sheets |
| Feature Engineering | Convert categorical ingredients to “one‑hot” vectors. | Pandas |
| Model | Train a Decision‑Tree regressor to predict cooking time based on ingredients. | Scikit‑learn |
| Evaluation | Compare predicted vs. actual time (Mean Absolute Error). | Jupyter/Colab |
| Presentation | Record a 1‑minute 3GP video showing the model predicting the time for a new recipe. | Screen recorder + HandBrake |
| Reflection | Discuss why the model mis‑predicted a particularly “slow‑cooking” stew. | Conversation |