TODO List for 0.1 release

### Base Classes
- [x] [nn::Module](https://github.com/arrayfire/arrayfire-ml/pull/30)
- [x] [autograd::Variable](https://github.com/arrayfire/arrayfire-ml/pull/30)
- [x] [Solver](https://github.com/arrayfire/arrayfire-ml/issues/35)
- [x] [nn::Loss](https://github.com/arrayfire/arrayfire-ml/issues/34)

### Autograd
- [x] Broadcasting [(sum, tile)](https://github.com/arrayfire/arrayfire-ml/pull/30)
- [x] [Math](https://github.com/arrayfire/arrayfire-ml/pull/30) (exp, pow, abs, sqrt, log)
- [x] Binary ([add, subtract, multiply, divide](https://github.com/arrayfire/arrayfire-ml/pull/30), [min, max, logical operators](https://github.com/arrayfire/arrayfire-ml/pull/32))
- [x] [matrix multiplication](https://github.com/arrayfire/arrayfire-ml/pull/30)
- [ ] [Convolutions and strided Convolutions](https://github.com/arrayfire/arrayfire-ml/issues/33)
- [ ] [Indexing and assignment](https://github.com/arrayfire/arrayfire-ml/issues/43)

### Neural Network
- [x] [Linear](https://github.com/arrayfire/arrayfire-ml/pull/30)
- [ ] [Convolve](https://github.com/arrayfire/arrayfire-ml/issues/33)
- [ ] [Pooling (Min, Max, Average)](https://github.com/arrayfire/arrayfire-ml/issues/33)
- [x] Activations: [Sigmoid, Tanh](https://github.com/arrayfire/arrayfire-ml/pull/30), [ReLU](https://github.com/arrayfire/arrayfire-ml/pull/31) and [related](https://github.com/arrayfire/arrayfire-ml/pull/32)
- [ ] [Recurrent neural networks (RNN, LSTM, GRU)](https://github.com/arrayfire/arrayfire-ml/issues/20)
- [ ] [Various Losses](https://github.com/arrayfire/arrayfire-ml/issues/34)
- [x] [Containers (Sequential)](https://github.com/arrayfire/arrayfire-ml/pull/30)
- [x] [Initializers](https://github.com/arrayfire/arrayfire-ml/issues/24)

### Solvers / Optimizers
- [x] [SGD](https://github.com/arrayfire/arrayfire-ml/issues/35)
- [x] [ADAM](https://github.com/arrayfire/arrayfire-ml/issues/35)

### Examples

Beginne damit, die nicht abgehakten verlinkten Issues zu Faltungen, Indexierung und Zuweisung, rekurrenten neuronalen Netzen und verschiedenen Loss-Funktionen zu prüfen. Die Checkliste umfasst mehrere Komponenten neuronaler Netze und von autograd. Bestätige daher vor der Auswahl einer klar abgegrenzten Aufgabe, welche Punkte noch relevant sind. Als abgeschlossen gilt die Arbeit, wenn die vereinbarten verbleibenden Arbeiten für das 0.1-Release erledigt sind.

Vom Indexierungsmodell aus dem Issue-Text verfasst.