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- Autoencoders and anomaly detection screencast and demo
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Classroom Contents
Intro to Deep Learning - ML Tech Talks
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- 1 - Intro and outline
- 2 - TensorFlow.js demos + discussion
- 3 - AI vs ML vs DL
- 4 - What’s representation learning?
- 5 - A cartoon neural network more on this later
- 6 - What features does a network see?
- 7 - The “deep” in “deep learning”
- 8 - Why tree-based models are still important
- 9 - How your workflow changes with DL
- 10 - A couple illustrative code examples
- 11 - What’s a hyperparameter?
- 12 - The skills that are important in ML
- 13 - An example of applied work in healthcare
- 14 - Families of neural networks + applications
- 15 - Encoder-decoders + more on representation learning
- 16 - Families of neural networks continued
- 17 - Are neural networks opaque?
- 18 - Building up from a neuron to a neural network
- 19 - A demo of representation learning in TF Playground
- 20 - Importance of activation functions
- 21 - What’s a neural network library?
- 22 - Overfitting and underfitting
- 23 - Autoencoders and anomaly detection screencast and demo
- 24 - Book recommendations
