CHAPTER1 INTRODUCTION -- Deep Learning Book Reading notes
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1. AI system -> Deep learning
- Background: programmable computer -> artifical intelligence
- Problem: Abstract and formal problem(easy) -> Informal and intuitive problem(hard)
- Projects:
- Rely on knowledge from human: hard-code knowledge in formal language (unwieldy)
- Gain knowledge from raw data: machine learning
- Rely on representation of data
- Representation learning:
- Simple: Auto-encoder
- Sophisticated, high-level, abstract feartures: Deep learning
- Deep learning
- Bulid complex concepts
- how to measure the depth of the network
- the number of sequential instructions (depth of computational graph)
- deep probabilistic model (depth of related concepts)
- A Venn diagram showing the relation of Deep learning, Representation learning, Machine learning and AI.
- Flowcharts showing how AI system relate to each other.
2.Historcal Trends in Deep Learning
Three historical waves of artifical neural nets research
The figure of three historical waves of artifical neural nets research shows as follow:
Increasing Dataset Sizes & Model Sizes & Accuracy, Complexity and Real-Word Impact
Supervised learning
Unsupervised learning
Reinforcement learning
关于RL及其SL,UL的区别,参考此页.
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