Machine Learning chevron_right Review & Next Steps

Review & Next Steps

calendar_today schedule 3 min read

Learning Objectives: By the end of this lesson, you will be able to:

  • Summarize major concepts in basic deep learning
  • Distinguish between traditional ML and deep learning workflows
  • Plan your next learning journey steps based on interests

1. Welcome and Introduction (5 minutes)

Icebreaker Activity

  • Share one ‘aha’ moment you’ve had in deep learning so far.

Lesson Overview

  • Duration: 30 minutes
  • Prerequisites: Completion of previous deep learning lessons
  • Materials Needed: Notebook, slide deck for summary

2. Learning Objectives and Session Outline (5 minutes)

What We’ll Cover Today:

  1. Concept recap of NNs, training, and frameworks
  2. Differences between ML and DL toolchains
  3. Pathways for advanced topics (CNNs, RNNs, Transformers)
  4. Resources and study strategies

Success Criteria:

  • Ability to articulate the end-to-end process of DL projects and outline a personalized study plan.

3. Foundation Knowledge Recap (10 minutes)

Key Recap Points

  • Neural network building blocks: layers, activations, loss
  • Training pipeline: forward, backward, update
  • Framework usage: setup, definition, training

Quick Terms

  • Overfitting vs. Underfitting
  • Hyperparameters
  • Backpropagation

4. Traditional ML vs. Deep Learning Workflows (5 minutes)

Aspect Classical ML Deep learning
Representations Often relies more on chosen or engineered features Multi-layer networks can learn representations from data, while still using preprocessing
Model choices Includes linear models, trees, kernels, and other families Uses neural networks with many architecture choices
Compute Cost varies by algorithm and dataset; some methods are very efficient Cost varies by model and dataset; GPUs can help but are not required for every task
Tuning Validation-based search and careful feature/model choices Validation-based search over architecture and training settings

These are broad tendencies, not rules. Both approaches require data preparation, validation, and choices matched to the task.

5. Next Learning Pathways (5 minutes)

  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs) & LSTM
  • Transformer architectures (BERT, GPT)
  • Generative models (GANs, VAEs)

Resources

  • Online courses (fast.ai, CS231n)
  • Research papers and blogs
  • Kaggle competitions

6. Planning Your Study Plan (5 minutes)

Steps

  1. Choose a domain (vision, NLP, or generative)
  2. Select a small project
  3. Set weekly milestones
  4. Join communities (forums, meetup groups)

7. Summary and Review Questions (5 minutes)

Key Takeaways

  1. Deep learning is a subset of ML that uses multi-layer neural networks and often learns useful representations from data.
  2. Frameworks streamline workflow but require careful configuration
  3. Continuous learning is key: keep exploring new architectures

Review Questions

  1. What are the main steps in a training loop?
  2. How does feature engineering differ between ML and DL?
  3. What resources would you use to learn about Transformers?

For Instructors

Teaching Tips

  • Use live polls to recap key points
  • Encourage peer discussions for planning projects

Assessment Ideas

  • Create a mini project rubric
  • Oral presentations of study plans

Adaptation Notes

  • For accelerated classes, shorten recap and focus on projects
  • For long workshops, add more hands-on next-topic previews