1.
Tanmay Bakshi
TED Speaker, Author, Algorithm-ist
Swift for TensorFlow

2.
First-class compiler support
for Machine Learning

3.
Native Automatic Differentiation

4.
1
1 + 𝑒$%
𝑤1 = 1
𝑤3 = 𝑤1 ⋅
−𝑤2
𝑤3,
𝑤5 = 𝑤3
𝑤7 = 𝑤5 ⋅ 𝑤5 ⋅ ln 𝑤6
𝑤8 = −𝑤7

5.
Many-Language problem

6.
C++Python
Other

7.
Swift
PythonDockerfile

8.
Julia
Other

9.
Why change?

10.
Performance & optimizations
Flexibility & Control
Rapid Prototyping
Interoperability & reverse-compatibility
Gentle learning curve

11.
TensorFlow is fundamentally not Pythonic.
But we’re trying!

12.
import as
10 8
3 3
10
def
return
for in 1 10000
with as
-0.1
print

13.
import
var ref Float 10 8
var guess Float 3 3
var distance Float 10
func error Tensor Float Tensor Float Tensor Float
Tensor Float
let
return
for _ in 1 10000
let Tensor Float in
return
-0.1
Optimized for you.

14.
It’s all in your hands.

15.
Write code naturally,
Swift for TensorFlow takes care of the rest.

16.
import
extension Tensor where Scalar TensorFlowFloatingPoint
func squaredError Tensor Scalar Tensor Scalar
return self

17.
1.05x
Circle-edge finding
0.95x
MLPNN Training
Without Compiler Optimizations!

18.
Forcibly safe code

19.
Interoperability!

20.
import
let np "numpy"
let p1 2 -2
let p2 10 8
func norm PythonObject PythonObject
return 2
let ex

21.
Code time!

22.
@TajyMany
tanmay bakshi
Tanmay Bakshi
TajyMany