DeepLearning.AI vs Delta

Build AI engineering skills with DeepLearning.AI, the AI education company founded by Andrew Ng. We help software engineers, AI/ML engineers, and data scientists build the depth to solve hard problems and stay relevant. The hard part is not finding AI content. It is knowing what to trust when there is far too much of it. At DeepLearning.AI, working practitioners decide what is worth your attention and filter for what is true and useful. You learn foundational skills you can carry to any tool, and the judgment to evaluate any tool on its merits. More than 10 million learners have started here, including engineers at the companies building frontier AI. Staying current with AI is a practice, not a project you finish, and it should not stop when you leave your desk. Turn a commute or a coffee break into progress. Stream lessons on demand or download courses for offline access. Follow the field with two reads inside the app. The Batch brings you DeepLearning.AI's perspective, including Andrew Ng's take on what matters. Data Points gives you a fast scan of the week's most important developments in a few minutes. Learn the way that fits how you work. Watch video lessons, read at your own pace, and track your progress. Earn certificates that signal real, job-ready skills. Download the app and build the AI engineering skills to stay ahead. Terms of Use (EULA): https://www.apple.com/legal/internet-services/itunes/dev/stdeula/ Privacy Policy: https://www.deeplearning.ai/privacy/

You read about AI every morning. Newsletters, threads, release notes. By Friday, almost none of it is left. The habit is not the problem. The format is. Reading something once is the least effective way to learn anything, and an entire content economy sits on top of it. Delta is the other half. Thousands of short lessons on what actually changed, each ending in a question you have to answer. Being asked is what makes it stick; reading never did. Ninety seconds, usable in a queue. WHAT A CARD LOOKS LIKE Two or three paragraphs, then a question. Not a quiz question. Predict the output. Spot the bug in the IAM policy. Pick the cheaper option and say why. Name the tradeoff the benchmark table is hiding. Then the explanation, which is the part that matters, written by someone who has shipped the thing rather than read about it. Every card links to the source it was written from. We never reproduce it. NINE TOPICS Foundations and modeling. Attention variants, scaling laws, fine-tuning, evaluation methodology, reading a benchmark table honestly. Generative AI. Reasoning models, context handling, structured output, sampling. Agentic AI. Tool use, MCP, planning, memory, and the failure modes only agents have: loops, runaway cost, silent partial failure. AI engineering and inference. KV cache, quantization, batching, routing, what a token actually costs you in production. Data and retrieval. RAG and how it fails, chunking, reranking, drift. AI security. Prompt injection direct and indirect, exfiltration through tools, MCP supply chain, sandboxing, least privilege, the OWASP LLM Top 10. Responsible AI and governance. Bias measurement, interpretability that survives an audit, the EU AI Act by risk tier, NIST AI RMF, ISO 42001, model documentation and audit trails. Always framed as what you have to build, never as policy commentary. Enterprise AI architecture. Landing zones, multi-tenancy, identity for agents acting for users, build versus buy. AI operations and reliability. Monitoring nondeterministic systems, evaluation in production, regression testing prompts, incident response when the model is the incident. This is for AI Engineers. Almost nobody develops anything for the engineer who has to get an agent through a security review, satisfy an auditor, and keep it inside an SLA. IT REMEMBERS WHAT YOU FORGOT A scheduler runs underneath, tracking what you got right, what you got wrong, and how long you took. A correct answer that took twelve seconds is not the same as one that took two. You never see any of it. There is no review queue, no due count, no deck to clear. Cards you are about to forget simply reappear in the feed, indistinguishable from new ones. BUILT FOR PEOPLE WHO ALREADY KNOW THINGS Delta assumes you know what a transformer is. It will not explain backpropagation. It is for practitioners with a few years of shipping behind them who want to know what changed this month and why it matters, not what a neural network is. NO ADS, EVER The entire premise is that this is the calm alternative to a feed that monetises your attention. There is no advertising identifier anywhere in the app and there never will be. Nothing about you is sold or shared. FREE AND PAID A free set in every topic, so you can see the whole range before deciding. The subscription opens the rest: thousands of cards across all nine topics, new material as the field moves, and a scheduler that brings back what you got wrong before you forget it. Delta Pro is $4.99 per month or $49.99 per year. Payment is charged to your Apple ID. Subscriptions renew automatically unless auto-renew is turned off at least 24 hours before the period ends, and you can manage or cancel in your Apple ID settings at any time. Terms: https://drvncrypto-org.github.io/delta-study/terms.html Privacy: https://drvncrypto-org.github.io/delta-study/privacy.html