# JosiahNet: a no dependency deep learning lib in GOLANG

# JosiahNet: live coding a GOLANG Deep Learning Library

**SourceCode:** github.com/thenomemac/josiahnet

Inspired by Joel Grus’s youtube live coding of a deep learning frameworks, I wondered as a very new to GOLANG user could I live code a deep learning framework?

It ended up taking a few hours to code this up as I didn’t have numpy as a starting point, but I found GOLANG to be very suitable for implementing a **Deep Learning Library in Go with no dependencies**.

Creating this way a great way for me to learn more about Go package creation and non-trivial uses of interfaces.

### Things I might add to this library in the future:

- Data Parallel training with Go Channels
- MNIST example

### Things this library is:

- a simple self contained way to learn about deep learning
- a way to learn about how matix algebra can be implemented from scratch
- a fun toy example

### Things this is not:

- a production deep learning lib for Go, see: Gorgonia

To play with this yourself: `go get github.com/thenomemac/josiahnet/jnet`

Run the XOR example:

```
2018-04-07 21:30:50 ⌚ thenome-lpc-13 in ~/gocode/src/github.com/thenomemac/josiahnet
○ → go run examples/xor.go
----- Begin Training -----
Epoch/Loss: 0 | 87.865
Epoch/Loss: 10 | 1.928
Epoch/Loss: 20 | 1.261
Epoch/Loss: 30 | 0.906
Epoch/Loss: 40 | 0.667
Epoch/Loss: 50 | 0.492
Epoch/Loss: 60 | 0.365
Epoch/Loss: 70 | 0.281
Epoch/Loss: 80 | 0.220
Epoch/Loss: 90 | 0.170
Epoch/Loss: 100 | 0.132
Epoch/Loss: 110 | 0.103
Epoch/Loss: 120 | 0.080
Epoch/Loss: 130 | 0.062
Epoch/Loss: 140 | 0.048
Epoch/Loss: 150 | 0.038
Epoch/Loss: 160 | 0.030
Epoch/Loss: 170 | 0.023
Epoch/Loss: 180 | 0.018
Epoch/Loss: 190 | 0.015
Epoch/Loss: 200 | 0.012
Epoch/Loss: 210 | 0.009
Epoch/Loss: 220 | 0.008
Epoch/Loss: 230 | 0.006
Epoch/Loss: 240 | 0.005
Epoch/Loss: 250 | 0.004
Epoch/Loss: 260 | 0.003
Epoch/Loss: 270 | 0.003
Epoch/Loss: 280 | 0.002
Epoch/Loss: 290 | 0.002
Epoch/Loss: 300 | 0.001
Epoch/Loss: 310 | 0.001
Epoch/Loss: 320 | 0.001
Epoch/Loss: 330 | 0.001
Epoch/Loss: 340 | 0.001
Epoch/Loss: 350 | 0.001
Epoch/Loss: 360 | 0.000
Epoch/Loss: 370 | 0.000
Epoch/Loss: 380 | 0.000
Epoch/Loss: 390 | 0.000
Epoch/Loss: 400 | 0.000
Epoch/Loss: 410 | 0.000
Epoch/Loss: 420 | 0.000
Epoch/Loss: 430 | 0.000
Epoch/Loss: 440 | 0.000
Epoch/Loss: 450 | 0.000
Epoch/Loss: 460 | 0.000
Epoch/Loss: 470 | 0.000
Epoch/Loss: 480 | 0.000
Epoch/Loss: 490 | 0.000
----- End Training -----
Predictions: [0.9979224722394722 0.0026328332573238855 0.002664968993301098 0.9972001895643201]
Targets: [1 0 0 1]
And we're done! Deep Learning is fun.
```

### FYI: Here’s the plan of attack I followed while live coding this library:

- Tensors
- Loss Functions
- Layers
- Neural Nets
- Optimizers
- Data : ended up skipping this
- Training
- XOR Example