AI, Machine Learning, Deep Learning... so What Are We Talking About?
summary
A 20-minute overview that explains the differences between AI, Machine Learning, Deep Learning and Reinforcement Learning, clarifying myths versus realities. It describes these technologies in simple terms, outlines their benefits and limitations, and shows how to apply them appropriately to solve problems previously infeasible.
AI-written summary · about
chapters (14)
- 0:00Introduction
- 1:03AI and ML definition
- 3:30Symbolic vs ML learning
- 4:53ML with examples
- 6:20From ML to DL
- 7:44Deep learning with images
- 9:07Neural networks brain analogies
- 10:08Training cycle data weights
- 12:35Activation loss optimization
- 16:02Activation functions
- 18:10Loss function optimization
- 19:36Training cycle summary
- 19:57Conclusion
- 20:42Recommended resources
