Frequently Asked Questions About Ng Deep Learning Project Execution Skill
21 answers covering everything from basics to advanced usage.
// Basics
What is deep learning in simple terms?
Deep learning is a type of machine learning that uses neural networks trained on large amounts of data. For practical purposes it's interchangeable with 'neural networks.' Its defining advantage is that performance keeps improving as you add more data and scale up the network, unlike traditional ML algorithms that plateau. It's currently the most effective category of machine learning algorithms known.
What are hyperparameters and why do they matter so much?
Hyperparameters are the parameters that control the parameters — settings like learning rate, network size, and batch size that govern how a neural network trains, as opposed to the weights learned during training. Your practical skill at tuning them directly determines how quickly you get a model to train well. Learning rate and network size are the most important. This decisively separates fast teams from slow ones.
What's the difference between structured and unstructured data?
Structured data is large tables of numbers — essentially giant Excel or Google Sheets spreadsheets. Unstructured data is text, audio, images, and video. The distinction matters because it gates your approach: unstructured text often works with LLM prompting, while audio, vision, and structured data usually require deep learning algorithms directly rather than prompting an LLM.
Is generative AI the same as deep learning?
No. Generative AI is one application of deep learning — specifically transformer neural networks trained on large internet-scraped datasets to generate text and sometimes images or audio. Treating them as interchangeable is a pitfall. Many use cases in audio, vision, and structured data require deep learning algorithms directly, not LLM prompting. Know which layer your problem actually lives at.
// How To
How do I run diagnostics on a stuck AI project?
After a baseline model exists, resist acting on hype. Examine error patterns: where is the model failing, and is the gap due to data quantity, data quality, model capacity, hyperparameter settings, or a task-definition mismatch? For complex systems, map all sub-components and run error analysis to find which one accounts for most failures. Only after diagnosis should you choose an intervention.
How much data do I need to start a deep learning project?
Don't assume you know. If a comparable application exists in research literature or your experience, benchmark against it. For greenfield projects, collect a small dataset and train a quick baseline — how well it works is your best diagnostic for data requirements. Remember: 100 data points can be enough, and 100 billion can still be too few.
How do I prioritize interventions once I've diagnosed the problem?
Follow Ng's priority order: (1) fix data quality or collect targeted data for the specific failure mode; (2) tune hyperparameters, especially learning rate and network size; (3) adjust model architecture, like a ConvNet for vision or a transformer for text; (4) fine-tune a pre-trained foundation model on your engineered data. Only buy more compute after exhausting these.
How do I set up a proper sandbox for prototyping?
Build in a contained environment with no sensitive data and no external exposure. This lets you explicitly relax security and scalability requirements. Use AI-assisted coding to accelerate the build. Treat it as a feedback instrument, not a shippable product — the goal is discovering what's in your data and whether your approach works, so you can run many cheap experiments rather than betting on one.
// Troubleshooting
My model has been below target for months — what am I doing wrong?
You're likely picking interventions at random rather than diagnosing. The fix is step 4: map sub-components if it's a complex system, run error analysis to find which component or issue accounts for most failures, then intervene only where evidence points. Teams stuck for months usually alternate between 'collect more data' and 'buy more GPUs' without ever verifying which is the actual constraint.
I've been tuning prompts for weeks with no progress — what should I do?
Drop one layer deeper into deep learning. If after roughly a month of prompt tuning you can't close the performance gap, the problem likely doesn't live at the GenAI layer. Document this decision explicitly so your team doesn't keep cycling between layers. For audio, vision, or structured data especially, prompting an LLM may never be enough.
My AI-generated code caused a database error — how do I prevent this?
Reserve agentic coders for prototype work and review their output rigorously before any production database operation. Agentic tools can cause irreversible data loss, such as wiping records during migrations. In production mode you reintroduce full reliability standards and apply AI-assisted coding far more carefully — generated code must be reviewed before deployment, not trusted blindly.
My experiments keep failing — should I run fewer of them?
No, run more and cheaper ones. Because prototyping cost is now low, the right response to failure isn't fewer experiments but faster, cheaper experiments. Expect most of your 20 proof-of-concept variants not to work. The one or two that do will justify all the others. Betting everything on a single proof of concept is a classic pitfall.
// Comparisons
How does this compare to a generic agile or Scrum approach?
Agile manages generic software delivery; Ng's method targets the specific uncertainty of ML, where output depends on both code and data you can't fully know upfront. It emphasizes diagnostic-first intervention selection and treating prototypes as feedback instruments. Agile doesn't tell you whether to add data, tune hyperparameters, or fine-tune a model — the diagnostic step is what makes this ML-specific.
How does fine-tuning compare to just calling an LLM API?
LLM API calls are ideal at prototype stage — costs are negligible and iteration is fast. But at scale those bills can become breathtaking. Fine-tuning a smaller, task-specific deep learning model bends the cost curve back down while often matching performance on your narrow task. The decision point is scale: prompt to prove the concept, fine-tune to make it economically viable.
How does 'move fast and be responsible' differ from 'move fast and break things'?
Ng reframes speed as a safety mechanism, not a risk. High iteration speed in a responsible, sandboxed environment is itself the mechanism for discovering what's in the data and what users actually want — which is the best way to find and fix problems before production. The fastest teams are often among the most responsible, because fast prototyping surfaces issues early.
Why does Ng say learning to code is still worth it in the AI era?
Because advising people not to code since AI will automate it will prove to be some of the worst career advice ever given. Easier coding tools mean more people should code, not fewer. Knowing how computers, deep learning, and GenAI work is the vocabulary you need to direct AI tools with precision — like knowing art history lets you prompt an image generator far better.
// Advanced
What are scaling laws and how should they influence my project?
Scaling laws are the empirically observed, predictable relationship between compute/data investment and model performance, popularized by OpenAI. They mean performance gains from scaling deep learning are forecastable. Design projects to exploit this — more data and larger models are often the lever — but only after verifying it holds for your specific application. Don't scale on faith; scale on evidence from your baseline.
How do I handle a truly greenfield project with no benchmarks?
When no parallel projects or research literature exist, there's no reliable way to estimate data requirements upfront. Collect a small initial dataset, train a quick baseline model, and use that model's performance as a diagnostic instrument. Adjust your data collection plan based on observed results rather than guessing. This is exactly how a researcher would approach a novel biosignal no one has collected before.
What are the layers of abstraction in AI capability?
AI capability is layered: CS fundamentals → Machine Learning → Deep Learning → Generative AI. Each layer builds on the ones below. When prompting an LLM at the GenAI layer isn't enough, drop one layer deeper into deep learning to get the application working. Knowing which layer your problem lives at prevents wasted months at the wrong level of abstraction.
How do I diagnose a complex multi-component AI system?
Break it into sub-components and run error analysis per component. For a biometric access system, that means image capture, face detection, face registration, face comparison, and spoof detection. Determine which sub-component accounts for most failures before intervening. This prevents the trap of applying a global fix like 'collect more data' when the bottleneck is actually one specific component or hyperparameter issue.
When should I transition from prototype to production-grade code?
Once a prototype proves the approach is viable. At that point, shift to production-grade, enterprise-grade, robust, reliable software and reintroduce full security, scalability, and reliability requirements. Continue using AI-assisted coding but apply it more carefully — review generated code rigorously, especially for database operations where agentic coders can cause irreversible data loss.