Deep Learning Specialization — Lesson 3: Convolutional, Sequence and Generative Architectures

Learning Objectives

• Compare convolutional, recurrent, attention-based, transformer, autoencoder, and generative model patterns
• Select an architecture based on data type, latency, accuracy, interpretability, compute, and operational constraints
• Validate convolutional, sequence and generative architectures decisions with documented requirements, evidence, risks, and measurable acceptance criteria

Detailed Topics

• Compare convolutional: Compare convolutional, recurrent, attention-based, transformer, autoencoder, and generative model patterns
• Select an architecture based on data type: Select an architecture based on data type, latency, accuracy, interpretability, compute, and operational constraints
• Validate convolutional: Validate convolutional, sequence and generative architectures decisions with documented requirements, evidence, risks, and measurable acceptance criteria

Guided Demonstration

1. Review the requirement, users, data, dependencies, permissions, risks, and expected result.
2. Demonstrate the relevant configuration, development, analysis, administration, or migration workflow step by step.
3. Capture settings, outputs, logs, screenshots, calculations, test results, and other useful evidence.
4. Compare the observed result with acceptance criteria and explain any corrective action.

Hands-On Activity

Analyze a realistic business case, compare implementation options, and validate the selected approach.

• Document assumptions, prerequisites, and the planned sequence before starting.
• Complete the activity and preserve evidence of normal, boundary, and failure behavior.
• Record issues, corrective actions, reusable notes, and the next improvement priority.

Scenario Analysis

• Identify the business or technical decision that must be made.
• Compare valid implementation approaches and their trade-offs.
• Consider security, privacy, accessibility, performance, support, and operational impact.

Troubleshooting Focus

• Separate symptoms from causes and collect evidence before applying a fix.
• Check configuration, data, permissions, dependencies, capacity, connectivity, and recent changes.
• Validate rollback, recovery, ownership, user impact, and business continuity.

Validation Checklist

• The requirement, scope, inputs, and expected outcome are clear.
• The selected design or configuration is documented and follows approved standards.
• Normal, boundary, failure, security, accessibility, and operational conditions are reviewed.
• The result is supported by evidence and the next improvement is prioritized.

Lesson Outcome

Learners can explain and apply Convolutional, Sequence and Generative Architectures in a realistic scenario and validate the result with documented evidence.