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Description
Gorgonia needs a website, I've been told. There are many features people don't know about, and they should be documented. Here's a rough framework in mind
- Basic Section
- Fundamentals: Computation Graphs
- Fundamentals: Tensors
- Fundamentals: Backpropagation and How It Relates to Differentiation
- Fundamentals: Stochastic Gradient Descent (VanillaSGD and RMSProp)
- Examples
- Logistic Regression
- Stacked Autoencoder
- RNNs
- LSTM
- Advanced section
- Different execution modes (
LispMachine
vsTapeMachine
) - Optimizing the computation graph
- Using MKL/CUDA
- Exploring the various SGD solvers provided in Gorgonia: ADAM, AdaGrad, etc
- Extending Gorgonia
- New Ops
- New Solvers
- Debugging your neural network
- Different execution modes (
- Behind the Scenes
- The design of Tensors
- Design Decisions and Rationales
1l0, Kvaz1r, linkerlin, keryoo and vwxyzjn
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