Mark Alen
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I am a PhD candidate at UC Berkeley I sometimes work as an independent consultant in the fields of machine learning, data mining and NLP |
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21 Questions
1
votes
2
answers
1k
views
Simple distributed and approximate linear regression
0
votes
1
answers
1k
views
Rating inference problem
0
votes
1
answers
1k
views
Conditional Expectations on Graphical Models
0
votes
3
answers
1k
views
Rating inference using SVM or similar models
1
votes
2
answers
4k
views
GPU vs. CPU and Machine Learning
1
votes
2
answers
2k
views
Going from bag-of-words to better language models
1
votes
2
answers
6k
views
Automatically selecting the number of topics in LDA
4
votes
2
answers
8k
views
Well Tested Latent Dirichlet Allocation in Python
1
votes
2
answers
1k
views
Scientific literature on question answering algorithms
1
votes
2
answers
5k
views
How does IBM Watson work?
1
votes
1
answers
2k
views
Should we use "Function word" instead of "Stop word"?
1
votes
3
answers
6k
views
LDA toolbox for MATLAB
2
votes
3
answers
3k
views
Good tutorial on feature selection
0
votes
1
answers
2k
views
Using SVM instead of logistic regression
7
votes
5
answers
8k
views
What are the pros and cons of using t-SNE
22 Answers
56 Votes
56
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50 Tags
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nlp
× 16 data × 16 visualization × 16 hadoop × 7 lda × 7 regression × 7 corpus × 6 gpu × 6 mapreduce × 6 datasets × 6 |
pca
× 5 dimensionality-reduction × 5 t-sne × 5 supervised-learning × 4 cuda × 4 svm × 4 pig × 4 nltk × 4 logistic-regression × 4 bag-of-words × 4 |
natural-language-processing
× 4 qa × 3 python × 3 feature-selection × 3 matlab × 3 collocation × 3 references × 3 featurization × 3 sparsity × 3 sparse × 3 |
watson
× 3 question-answering × 2 unsupervised-learning × 2 sparse-coding × 2 java × 2 topic-models × 2 dataset × 2 neural-networks × 2 linear-regression × 2 sampling × 1 |
code
× 1 convex × 1 analysis × 1 community × 1 hashing × 1 db × 1 implementation × 1 noob × 1 statistics × 1 mturk × 1 |
19 Kudos
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● Well-Known Question × 21 ● Conspicuous Question × 21 ● Prominent Question × 21 ● Appreciated Question × 8 ● Prized Question × 8 ● Appreciated Answer × 8 |
● Esteemed Question × 5 ● Cherished Question × 4 ● Esteemed Answer × 3 ● Cognoscenti × 2 ● Revisionist × 1 ● Blue Pencil × 1 |
● Mentor × 1 ● Advocate × 1 ● Inquisitive × 1 ● Guide × 1 ● Adulated Question × 1 ● Participant × 1 |
● Adulated Answer × 1 |