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Brain of mat kelcey. Simple tensorboard visualisation for gradient norms. June 27, 2017 View Comments. I've had three people recently ask me about how i was visualising gradient norms in tensorboard so, according tomy three strikes rule, i now have to "automate" it by writing a blog post about it )one really useful visualisation. After 2,350 days in america we are moving home. June 14, 2017 View Comments. August 11, 2016 View Comments. Learning to do laps with reinforcement learning and neural nets.

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Brain of mat kelcey. Simple tensorboard visualisation for gradient norms. June 27, 2017 View Comments. I've had three people recently ask me about how i was visualising gradient norms in tensorboard so, according tomy three strikes rule, i now have to automate it by writing a blog post about it )one really useful visualisation. After 2,350 days in america we are moving home. June 14, 2017 View Comments. August 11, 2016 View Comments. Learning to do laps with reinforcement learning and neural nets.
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brain of mat kelcey | matpalm.com Reviews

https://matpalm.com

Brain of mat kelcey. Simple tensorboard visualisation for gradient norms. June 27, 2017 View Comments. I've had three people recently ask me about how i was visualising gradient norms in tensorboard so, according tomy three strikes rule, i now have to "automate" it by writing a blog post about it )one really useful visualisation. After 2,350 days in america we are moving home. June 14, 2017 View Comments. August 11, 2016 View Comments. Learning to do laps with reinforcement learning and neural nets.

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brain of mat kelcey

http://matpalm.com/blog/2012/08/item_similarity_by_bipartite_graph_dispersion

Brain of mat kelcey. Item similarity by bipartite graph dispersion. August 20, 2012 View Comments. The basis of most recommendation systems is the ability to rate similarity between items. there are lots. Of different ways to do this. One model is based the idea of an interest graph. Where the nodes of the graph are users and items and the edges of the graph represent an interest, whatever that might mean for the domain. If we only allow edges between users and items the graph is bipartite. The result of...

2

audioscrobbler experiments

http://matpalm.com/audioscrobbler

Part 1: bands.like.the.bands.i.like. Part 2: distance.from.celion.to.napalm. Part 3: multi.dimensional.tag.distance. Code available at github.com/matpalm/audioscrobbler-experiments.

3

brain of mat kelcey

http://matpalm.com/blog/2015/03/15/hallucinating_softmaxs

Brain of mat kelcey. March 15, 2015 View Comments. Language modelling is a classic problem in NLP; given a sequence of words such as "my cat likes to ." what's the next word? This problem is related to all sorts of things, everything from autocomplete to speech to text. The first approach i saw to solving this problem with neural nets is from bengio et al. "a neural probabilistic language model". Approach and the second the logisitic regression. A simple generating grammar. Rather than use real text data...

4

diy statistically improbable phrases

http://matpalm.com/sip

Diy statistically improbable phrases. Take 1: trigram frequency. Take 2: term frequency. Take 3: markov chains. Part 4: but does it scale? Code available at github.com/matpalm/sip.

5

brain of mat kelcey

http://matpalm.com/blog/2015/03/28/theano_word_embeddings

Brain of mat kelcey. Brutally short intro to theano word embeddings. March 28, 2015 View Comments. One thing in theano i couldn't immediately find examples for was a simple embedding lookup table, a critical component for anything with NLP. turns out that it's just one of those things that's so simple no one bothered writing it down :/. Tl;dr : you can just use numpy indexing. And everything just works. Numpy / theano indexing. To pick a subset of the embeddings it's as simple as just using indexing.

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Chris Wensel's Bookmarks (User cwensel)

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Sign in with Twitter. Sign in with Facebook. Sign in with Google. Type tags to add. Common tags to remove. Select a tag to rename. Share It With Others! Type Emails or User Names (comma-separated). Link shares via email will come from your email address so recipients know it's you and can reply. Lowercase tags are recommended:. Spaces are allowed but not recommended:. Reuse your tags and the tags of the community as much as possible. At the start of a tag if it's unrelated to the link content:. On Septem...

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Iterative Feature Generation | Wavii Blog

https://wavii.wordpress.com/2013/02/25/iterative-feature-generation

A place to learn about Wavii, the technology we're building, and where we're headed. Testers, testers, testers, testers, testers! Wavii: Instant news feeds for any topic. Making Facebook out of Google. Be heard with Wavii polls. What do Lebron James, Danica Patrick, and the NHL have in common? February 25, 2013. At Wavii, we use classification. For a number of NLP tasks such as disambiguating entities. Between entities (whom did a company acquire). A common problem when performing classification. I use s...

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our kaleidic cosmos » My First Ruby Program

http://kaleidic.com/2010/my-first-ruby-program

27 Dec 2010 05:37 pm. My First Ruby Program. A year and a half ago I first put attention to Ruby, the programming language. Like many who are new to that language I took up the Ruby Koans. Within a few days I walked away from it, and only recently have I come back to writing Ruby. The last thing I wrote before that hiatus was also the first thing that felt like writing anything in the language. It was the scoring program for the dice game, Greed. This is that program:. At the time I included comments to ...

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My Education in Machine Learning via Coursera, A Review So Far « Inviting Epiphany

http://richardminerich.com/2012/12/my-education-in-machine-learning-via-cousera

A programmer's chronicle of insights and discoveries. Professional F# 2.0. My Education in Machine Learning via Coursera, A Review So Far. Note that all effort estimations include the time spent watching lectures, reading related materials, taking quizes and completing programming assignments. Classes are listed in the order they were taken. Estimated Effort: 10-20 Hours a Week. Taught by Andrew Ng. Estimated Effort: 20-30 Hours a Week. Taught by Daphne Koller. Functional Programming Principles in Scala.

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Natural Language Processing and Machine Learning, some pointers :: Hackdiary

https://www.hackdiary.com/2012/10/14/natural-language-processing-and-machine-learning-some-pointers

Natural Language Processing and Machine Learning, some pointers. October 14th, 2012 Published in data. I’ve been doing a lot of natural-language machine-learning work both for clients and in side-projects recently. Mark Needham. Asked me on Twitter for some pointers to good introductory material. Here’s what I wrote for him:. Nearly all text processing starts by transforming text into vectors:. Https:/ en.wikipedia.org/wiki/Vector space model. Https:/ en.wikipedia.org/wiki/Tf%E2%80%93idf. Or if you know ...

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brain of mat kelcey

Brain of mat kelcey. Simple tensorboard visualisation for gradient norms. June 27, 2017 View Comments. I've had three people recently ask me about how i was visualising gradient norms in tensorboard so, according tomy three strikes rule, i now have to "automate" it by writing a blog post about it )one really useful visualisation. After 2,350 days in america we are moving home. June 14, 2017 View Comments. August 11, 2016 View Comments. Learning to do laps with reinforcement learning and neural nets.

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