Applied Machine Learning
Neural Studio Foundations
Tensor graph literacy, tensorboard tracing, and reproducible notebooks for engineers entering applied deep learning.
- Duration
- 6 weeks · hybrid
- Format
- Cohort + labs
- Level
- Intermediate
- Tuition (informational)
- ₩890,000
Program narrative
This cohort moves from tensor mechanics to small-model training loops you can defend in a design review. You will version datasets, log experiments, and pair every training run with a written hypothesis so results stay traceable when stakeholders ask why a metric moved.
What is included
- · GPU notebook provisioning with shared baseline images
- · Experiment registry templates aligned to internal audit trails
- · Gradient health checks and spectral norm spot checks
- · Mixed-precision toggles with documented trade-offs
- · Checkpoint rotation policy you can paste into runbooks
- · Pairing blocks with a senior instructor on your own dataset slice
- · Office hours focused on debugging dataloader edge cases
Outcomes you can demo
- · Ship a reproducible training script with pinned seeds and dependency lockfiles
- · Produce a one-page experiment card stakeholders can skim before release
- · List three quantitative risks you will monitor after the first production deploy
Mentor of record
Haneul Min
Former research engineer on vision systems for industrial QA; now curriculum lead for applied deep learning.
Participant questions
Do you supply GPUs for the full duration?
We provide shared cloud credits sufficient for the lab briefs. Heavy personal experiments beyond the brief are not included and should use your employer sandbox.
Is prior linear algebra coursework required?
Comfort with matrix notation and derivatives is assumed. We publish a refresher notebook two weeks before kickoff; it is not a substitute for first exposure.
What is explicitly out of scope?
Large-scale distributed training, bespoke CUDA kernels, and production SLAs for inference are not covered. We point you to follow-on MLOps and LLM integration tracks instead.
Recent participant notes
“Week two’s graph sanity drills caught a shape bug we had masked with silent broadcasting. The MLOps primer deck from Haneul is pinned in our wiki.”
“Dense, in a good way. I wanted one more session on data leakage patterns, but the office hours notes filled the gap.”