amplifier_khan

16 ms·

61.
Unsupervised Feature Learning and Deep Learning Tutorial(ufldl.stanford.edu)
4 points by amplifier_khan 10y ago|0 comments
62.
Machine Learning Is Fun: Modern Face Recognition with Deep Learning(medium.com)
3 points by amplifier_khan 10y ago|0 comments
63.
InfoGAN: using the variational bound on mutual information (twice)(inference.vc)
2 points by amplifier_khan 10y ago|0 comments
64.
AI Revolution 101 Our last invention, greatest nightmare, or pathway to utopia?(medium.com)
1 points by amplifier_khan 10y ago|0 comments
65.
The real 10 algorithms that dominate our world(medium.com)
19 points by amplifier_khan 10y ago|1 comments
66.
Thesauropod.us: Building a Podcast Recommendation Algorithm(insightdatascience.com)
2 points by amplifier_khan 10y ago|0 comments
67.
Large Scale Decision Forests: Lessons Learned(blog.siftscience.com)
1 points by amplifier_khan 10y ago|0 comments
68.
What Is the Difference Between AI, Machine Learning, and Deep Learning?(blogs.nvidia.com)
1 points by amplifier_khan 10y ago|0 comments
69.
How Human-Machine Learning Partnerships Can Reduce Unconscious Bias(entrepreneur.com)
1 points by amplifier_khan 10y ago|0 comments
70.
Bayesian Machine Learning, Explained(kdnuggets.com)
3 points by amplifier_khan 10y ago|0 comments
71.
A quick comment on structured input vs. structured output learning(nlpers.blogspot.hr)
1 points by amplifier_khan 10y ago|0 comments
72.
Make Algorithms Accountable(nytimes.com)
3 points by amplifier_khan 10y ago|0 comments
73.
One Genius’ Lonely Crusade to Teach a Computer Common Sense(wired.com)
2 points by amplifier_khan 10y ago|0 comments
74.
Deep Learning and the Future of AI: Talk by Yann LeCun(cds.cern.ch)
3 points by amplifier_khan 10y ago|0 comments
75.
10 Questions for the Nation’s First Chief Data Scientist(sciencefriday.com)
1 points by amplifier_khan 10y ago|0 comments
76.
Top Programming Languages Trends: The Rise of Big Data(spectrum.ieee.org)
2 points by amplifier_khan 10y ago|0 comments
77.
by amplifier_khan 10y ago
Aside from algorithmic gender bias and "mathwashing" as mentioned in the article, what other sources need be considered?
78.
Why We Should Expect Algorithms to Be Biased(technologyreview.com)
1 points by amplifier_khan 10y ago|1 comments
79.
How predictive analytics discovers a data breach before it happens(flipboard.com)
2 points by amplifier_khan 10y ago|0 comments
80.
The 10 Algorithms Machine Learning Engineers Need to Know(medium.com)
3 points by amplifier_khan 10y ago|0 comments
81.
10 Data Acquisition Strategies for Startups(medium.com)
1 points by amplifier_khan 10y ago|0 comments
82.
by amplifier_khan 10y ago
Yup it seems like currently recs are based on content filtering techniques, based on defined properties of each beer. I'd be interested in seeing this project utilize a collaborative filter to take into account user interactions/
83.
Creating a Beer Recommendation Engine(willnetsky.github.io)
14 points by amplifier_khan 10y ago|5 comments
84.
Tensorflow 3 Ways(gab41.lab41.org)
4 points by amplifier_khan 10y ago|0 comments
85.
Limitations of Deep Learning and strategic observations(datasciencecentral.com)
3 points by amplifier_khan 10y ago|0 comments
86.
The relativity of raw data(simplystatistics.org)
1 points by amplifier_khan 10y ago|0 comments
87.
Moving Beyond CTR: Better Recommendations Through Human Evaluation(blog.echen.me)
1 points by amplifier_khan 10y ago|0 comments
88.
Gradient Boosting Interactive Playground(arogozhnikov.github.io)
1 points by amplifier_khan 10y ago|0 comments
89.
Performance Metrics for Recommender Systems(gab41.lab41.org)
1 points by amplifier_khan 10y ago|2 comments
90.
Anatomy of an Elasticsearch Cluster: Part II(insightdataengineering.com)
2 points by amplifier_khan 10y ago|0 comments

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