ML Buzzwords Demystified
summary
A non-technical overview that explains current machine learning trends and concepts without formulas, covering AutoML, machine learning bias, and generative adversarial networks (GANs). It aims to clarify common ML buzzwords and give a high-level sense of what those technologies and issues are.
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chapters (46)
- 0:00Start
- 2:402001 A Space Oddysey (1968)
- 3:21Passing the Turing test
- 4:20Biggest Threat?
- 4:46New Reality?
- 5:20Assistant or Controller?
- 7:38Biased humans
- 7:52Biased word associations by Word2Vec
- 8:20Devices reflecting stereotypes
- 9:07Experimenter's bias
- 9:43Debiasing by post-processing
- 10:03Debiasing by removing features
- 10:46Correcting bias before training
- 13:20Model Agnostic Interpretation
- 14:16Simpler models?
- 15:46Model Interpretability?
- 17:32Can data scientist be replaced?
- 17:57Proof of Concept
- 18:24Framing a project
- 18:54Building data pipelines
- 19:21Model development
- 19:31NAS: Neural Architecture Search
- 21:45Automated Machine Learning: Azure ML
- 22:27Automation Machine Learning
- 23:10Automation - Been there, done that?
- 23:48Automated ML future
- 24:14Transfer skills and knowledge
- 24:29Different and yet...Similar?
- 25:14Norwegian Moose Detector
- 25:27Transfer Learning: replace prediction unit
- 25:52Pretrained networks
- 26:46GAN: Generate the data?
- 27:16GAN: Generative Adversarial Networks
- 27:52GAN: Architecture
- 30:05GAN: Image reconstruction
- 30:18Image to Image transition
- 32:10GAN: Adversarial attacks
- 33:52Reward and Punishment
- 34:05Feedback, not Label
- 35:14Deep Reinforcement Learning
- 35:57Alpha Go Victory
- 36:53Boston Dynamics: back to basics
- 41:24Adaptability
- 42:03Correlation is not Causation
- 43:08Cause-Effect connection
- 43:37Causal Learning
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