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.
A learning path
Learn the cloud, in order.
Three tracks, each building on the last. Start with how the cloud works, move on to machines that learn, and finish with the generative models built on both.
- 01
Cloud computing · 34 articles
Fluffy Computing
Foundations of the cloud and hands-on tours of AWS, Google Cloud and Azure — from your first VM to multi-cloud strategy.
- Understanding Cloud Computing
- Getting to Know AWS
- Exploring Google Cloud Platform (GCP)
- Diving into Microsoft Azure
- Comparing the Cloud Providers
- +2 more
- 02
AI & machine learning · 9 articles
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 AI
- 03
Generative AI · 30 articles
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 AI
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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).