Machine Learning for Gophers

My dotGo 2017 talk

Transcript

1. Machine Learning for Gophers @francesc
2. 1996 - Exciting times
3. 1996 - Exciting times (at least for computers)
4. 2016 - Very exciting times
5. 2016 - Very exciting times
6. name elo rating matches Fan Hui 3,144 5:0 against Fan Hui Lee Sedol 3,739 4:1 against Lee Sedol Master 4,858 60:0 against professional players Zero 5,185 100:0 against AlphaGo Lee 89:11 against AlphaGo Master training hardware 176 GPU 48 TPU 4 TPU 4 TPU 2017 - E_TOO_EXCITE
7. deepmind.com/blog/alphago-zero-learning-scratch
8. What is Machine Learning? “A computer program is said to learn from experience E, with respect to some task T, and some performance measure P, if its performance on T as measured by P improves with experience E” - Tom Mitchell
9. What is Machine Learning? “A computer program is said to learn from experience E, with respect to some task T, and some performance measure P, if its performance on T as measured by P improves with experience E” - Tom Mitchell
10. What is Machine Learning? “A computer program is said to learn from experience E, with respect to some task T, and some performance measure P, if its performance on T as measured by P improves with experience E” - Tom Mitchell
11. What is Machine Learning?
12. What is Machine Learning? • task: recognizing handwritten digits • performance measure: percentage of digits labeled correctly • experience: try to predict, fail, improve it, repeat
13. Input 0 0 0 0 0 0 0 0 0 0 255 0 0 255 0 0 0 0 255 0 0 255 0 0 0 0 255 255 255 255 0 0 0 0 0 0 0 255 0 0 0 0 0 0 0 255 0 0 0 0 0 0 0 255 0 0 0 0 0 0 0 0 0 0 28 x 28 , 4
14. • Very simple technique y = X * W + b Logistic regression input biases weights prediction
15. • Average of how “off” predictions were J = -1/m * sum(dist(pred, label)) Cost function
16. • Finding the W and b that minimize J • We know what direction to go by deriving J vs W and b not gonna show that Optimizing
17. mnist tutorial?
18. Can we do this in Go?
19. • Reading images • Multiplying matrices • Visualization of datasets • Making it fast What do we need?
20. • matrices and linear algebra • statistics • probability distributions • differentiation, integration, and optimization • plotting • and much more! gonum
21. 1 2m (X * theta - y)2 Σ
22. gonum func ComputeCost(X, y, theta *mat.Dense) float64 { m, _ := X.Dims() h := new(mat.Dense) h.Mul(X, theta) h.Sub(h, y) h.MulElem(h, h) return mat.Sum(h) / float64(2*m) }
24. Let’s reinvent the wheel! github.com/campoy/mat • focus on API over performance • immutable matrices • only float64 matrices
25. ComputeCost with github.com/campoy/mat func ComputeCost(X, y, theta mat.Matrix) float64 { h := mat.Minus(mat.Product(X, theta), y) return mat.Product(h, h).Sum() / float64(2 * X.Rows()) }
26. demo time! * * not guaranteed to work
27. References github.com/campoy/mat github.com/campoy/goml github.com/campoy/justforfunc github.com/campoy/tools/imgcat
28. Merci! @francesc VP of Developer Relations at source{d} sourced.tech