Hands on Mahout!
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1. The document summarizes a presentation about Apache Mahout, an open source machine learning library. It discusses algorithms like clustering, classification, topic modeling and recommendations. 2. It provides an overview of clustering Reuters documents using K-means in Mahout and demonstrates how to generate vectors, run clustering and inspect clusters. 3. It also discusses classification techniques in Mahout like Naive Bayes, logistic regression and support vector machines and shows code examples for generating feature vectors from data.
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Hands on Mahout!
- 1. Hands on! Speakers: Ted Dunning, Robin Anil OSCON 2011, Portland
- 12. Part 1 - Clustering
- 24. K-Means clustering c1 c2 c3
- 25. K-Means clustering c1 c2 c3
- 26. K-Means clustering c1 c2 c3 c1 c2 c3
- 27. K-Means clustering c1 c2 c3
- 34. Part 2 - Classification
- 37. Data Flow Not quite so simple
- 40. Data Flow
- 42. The pipeline Classifiable Data Vectors
- 43. Instance and Target Variable
- 44. Instance and Target Variable
- 45. Hashed Encoding
- 47. Let’s write some code (cue relaxing background music)
- 50. K-means features
- 51. More code! (cue relaxing background music)
- 53. Parallel Stochastic Gradient Descent Average models Train sub model Model I n p u t
- 54. Variational Dirichlet Assignment Update model Gather sufficient statistics Model I n p u t
- 57. Modeling architecture Feature extraction and down sampling I n p u t Side-data Data join Sequential SGD Learning Map-reduce Now via NFS
- 58. More in Mahout
Editor's Notes
- 9.1 top left
- 9.1 top right
- 9.1 bottom left
- 9.1 bottom right