Master Neural Architecture Search (NAS) to automate the creation of neural networks.
4 topics you need to cover⬇️
4 topics you need to cover⬇️
1) The concept of NAS
1. Read one of the fundamental papers, "A Survey on Neural Architecture Search" @IBMResearch
2. Explore our dedicated Edge#4 for free thesequence.substack.com/p/thesequence-…
1. Read one of the fundamental papers, "A Survey on Neural Architecture Search" @IBMResearch
2. Explore our dedicated Edge#4 for free thesequence.substack.com/p/thesequence-…
2) NAS algorithms
1. Differentiable Architecture Search
2. Differentiable ArchiTecture Approximation
3. eXperts Neural Architecture Search
4. Petridish (Read "Efficient Forward Architecture Search")
1. Differentiable Architecture Search
2. Differentiable ArchiTecture Approximation
3. eXperts Neural Architecture Search
4. Petridish (Read "Efficient Forward Architecture Search")
2) Search strategies in NAS
1. Grid search and random search
2. Bayesian Optimization
3. Reinforcement learning and evolutionary algorithms
1. Grid search and random search
2. Bayesian Optimization
3. Reinforcement learning and evolutionary algorithms
4) Implement NAS
1. Try to create your own model with @UberEng's Ludwig
2. Execute state-of-the-art NAS methods in @MSFTResearch Archai
3. Use AdaNet to learn ensembles of models
1. Try to create your own model with @UberEng's Ludwig
2. Execute state-of-the-art NAS methods in @MSFTResearch Archai
3. Use AdaNet to learn ensembles of models
• • •
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