MACHINE LEARNING ENGINEER ROADMAP · STAGE 03 OF 04
Deep learning and experimentation
Understand neural-network training and run controlled, reproducible experiments.
Train a modest neural model, diagnose learning behavior, and compare it fairly with a baseline.
CORE SKILLS
Key concepts in deep learning and experimentation
Neural-network building blocks
Learn how layers, activations, loss functions, and gradients combine during model training.
Topics include
- Weights and activations
- Loss and gradient descent
- Training versus inference
Data preparation and representation
Represent numerical, categorical, image, or text inputs in a form that matches the model and task.
Topics include
- Normalization and encoding
- Embeddings and tokenization basics
- Batching and data loaders
Training diagnostics
Read learning curves and identify underfitting, overfitting, optimization issues, or data problems.
Topics include
- Learning rates and optimizers
- Regularization and early stopping
- Training and validation curves
Experiment tracking
Record experiment settings and outcomes so comparisons can be reproduced and interpreted.
Topics include
- Configuration and random seeds
- Metrics and artifact logging
- Fair comparisons and ablations
APPLY WHAT YOU LEARN
Practical project
Train a small text or image classifier, track its configuration and metrics, and report a controlled comparison against a simpler baseline.
CHECK YOUR UNDERSTANDING
You are ready to move on when
- Can diagnose a training curve before tuning blindly.
- Can reproduce an experiment and state its limitations.