Generative AI
StyleGAN: Generating High-Resolution Images
How StyleGAN reaches 1024 by 1024 faces: the mapping network, per-layer styles, noise inputs, the truncation trick, and what StyleGAN2, ADA and StyleGAN3 changed.
Cloud · AI · Generative AI
Towards the cloud,
one idea at a time.
Complex tech, broken into fun, digestible bites. Practical guides to AWS, Google Cloud and Azure, and plain-English tours of machine learning and generative AI.
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- Quiz questions
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Three series that build on each other: start with the cloud, meet the machines that learn, then watch them create.
38 articles
Cloud computing
Fluffy Computing
Foundations of the cloud and hands-on tours of AWS, Google Cloud and Azure — from your first VM to multi-cloud strategy.
- Virtual Machines7
- Diving into Microsoft Azure7
- Exploring Google Cloud Platform (GCP)7
- Getting to Know AWS7
13 articles
AI & machine learning
Artificial Unintelligence
How machines learn: the history of AI, supervised and unsupervised learning, model training and the data behind it all.
- Introduction to Machine Learning and AI9
- Machine Learning Algorithms4
35 articles
Generative AI
Word Up! Bot
Transformers, GANs, VAEs and GPT — what generative AI is, how it works, and how the cloud providers put it to work.
- Introduction to Generative AI30
- Deep Dive into Generative Models5
Quick quiz
Think you know the cloud?
Three quizzes, 150 questions, instant explanations. Your best scores stay on your device.
The 'shared responsibility model' in cloud computing primarily defines the security obligations between:
The shared responsibility model outlines which security tasks are handled by the cloud provider (e.g., security OF the cloud infrastructure) and which are handled by the customer (e.g., security IN the cloud, like data configuration and access management).
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More from the archive
Cloud
Pricing Models: On-Demand, Reserved, Savings Plans, and Spot
How on-demand, reservations, savings plans, committed use discounts and Spot pricing work on AWS, GCP and Azure, and how to combine them safely.
Generative AI
Training GANs: Tips, Tricks, and Common Pitfalls
Why GAN training breaks, how to read the signs, and the fixes that work: a solid DCGAN baseline, loss choices, spectral norm, R1, EMA and a debugging checklist.
AI & ML
Logistic Regression: Understanding Probabilities
How logistic regression turns a weighted sum into a probability: the sigmoid, log loss, odds ratios, thresholds, imbalance and scikit-learn code.
Cloud
Instance Types and Families Explained
How to read and choose VM instance types on AWS, GCP and Azure: families, memory ratios, CPU architectures, burstable credits, local disks and rightsizing.
Generative AI
Implementing VAEs: A Step-by-Step Guide
Build a convolutional variational autoencoder in PyTorch: data, encoder, decoder, loss, KL warm-up, evaluation, sampling and checkpoints.
AI & ML
Linear Regression: Predicting Outcomes
How linear regression predicts numbers: the model, least squares, gradient descent, scikit-learn code, the assumptions that matter and when to regularize.