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.
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.
- Articles
- 80
- Series
- 11
- Quiz questions
- 150
Fresh from the blog
Latest reads
Learning paths
Pick a track
Three series that build on each other: start with the cloud, meet the machines that learn, then watch them create.
36 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.
- Diving into Microsoft Azure7
- Exploring Google Cloud Platform (GCP)7
- Getting to Know AWS7
- Understanding Cloud Computing7
11 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 Algorithms2
33 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 Models3
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).
Keep exploring
More from the archive
Cloud
Comparing VM Offerings Across AWS, GCP, and Azure
EC2, Compute Engine and Azure VMs side by side: naming, families, disks, maintenance, stop billing, Spot, discounts and SLAs, and how to choose.
Generative AI
Mathematical Foundations of VAEs
Where the VAE loss comes from: latent variable models, the evidence lower bound, the closed-form KL term and the reparameterization trick, with PyTorch code.
AI & ML
Key Concepts and Terminologies in ML
The machine learning vocabulary you need: features, labels, parameters, hyperparameters, splits, overfitting, metrics and leakage, with runnable examples.
Cloud
Understanding Azure Virtual Machines
How Azure VMs really work: size names decoded, VM families, managed disks, availability options, power states, pricing choices and the CLI basics.
Cloud
Modern E-Commerce Checkout Flow Architecture With Payments
A reference architecture for checkout: server-side pricing, stock reservations, payment intents, idempotency, webhooks, the outbox pattern and failure recovery.
Exploring GCP Compute Engine Instances
Exploring GCP Compute Engine Instances "Infrastructure is no longer just a cost center — it's a competitive differentiator. And nowhere is that more visible than in how you choose, configure, and optimize your virtual machines.