2018/10/04 MACHINE LEARNING Meetup KANSAI #3
2018/09/25 Machine Learning Casual Talk #6
8 Reasons Why Analytics / Machine Learning Models Fail To Get Deployed Introduction Don’t be a data scientist whose models fail to get deployed! An epic example of model deployment failure is from Netflix Prize Competition. In a short story, it was an open competition. Participants had to build a collaborative filtering algorithm to predict user rating for films. The winners received grand prize o
The Amazing Power of Word Vectors A fantastic overview of several now-classic papers on word2vec, the work of Mikolov et al. at Google on efficient vector representations of words, and what you can do with them. For today’s post, I’ve drawn material not just from one paper, but from five! The subject matter is ‘word2vec’ – the work of Mikolov et al. at Google on efficient vector representations of
[Registrations Open]Ascend Pro: Industry Immersive Program in collaboration with KPMG | Get Super Early Bird Offer Introduction Learn to connect AWS instance with your laptop / desktop for faster computation! Do you struggle with working on big data (large data sets) on your laptop ? I recently tried working on a 10 GB image recognition data set. But, due to the limited computational power of my l
Many data science competitions suffer from a test set being markedly different from a training set (a violation of the “identically distributed” assumption). It is then difficult to make a representative validation set. We propose a method for selecting training examples most similar to test examples and using them as a validation set. The core of this idea is training a probabilistic classifier t
Midwest.io is was a conference in Kansas City on July 14-15 2014. At the conference, Josh Wills gave a talk on what it takes to build production machine learning infrastructure in a talk titled “From the lab to the factory: Building a Production Machine Learning Infrastructure“. Josh Wills is a the Senior Director of Data Science at Cloudera and formally worked on Google’s ad auction system. In th
機械学習の分類の話を、主に決定境界と損失関数の観点から整理してみました。 とはいっても、k-NNとか損失関数関係ないのもいます。 最初ははてなブログに書こうとしたのですが、数式を埋め込むのが辛かったのでjupyter notebookにしました。 github.com [追記] githubだと日本語を含む数式のレンダーが壊れるので、nbviewerの方がいいかもしれません。 https://nbviewer.jupyter.org/github/chezou/notebooks/blob/master/classification.ipynb [/追記] パーセプトロンが見直されたのはなんでだっけ、SVMってどういう位置づけだっけ、というのを確認できればなぁと思っています。 多層パーセプトロンまでに至るところの流れがうまく伝わればなぁと思っています。 間違いなどがあれば、是非ご指摘いただ
Neural networks provide the possibility to solve complicated non linear problems. They can be used in various areas such as signal classification, forecasting timeseries and pattern recognition. A neural network is a model inspired by the human brain and consists of multiple connected neurons. The network consists of a layer of input neurons (where the information goes in), a layer of output neuro
Attempts to abstract and study machine learning are within some given framework or mathematical model. It turns out that all of these models are significantly flawed for the purpose of studying machine learning. I’ve created a table (below) outlining the major flaws in some common models of machine learning. The point here is not simply “woe unto us”. There are several implications which seem impo
昨年の10月から12月にかけて Cousera の機械学習オンラインコース "Machine Learning" を受講し、無事完走することができた。 www.coursera.org コースは無料で受講できるが、修得したことを公式に認定する修了証 (verified certificate) を取得するためにはお金がいる。 機械学習コースの取得料は$49と安くはないが、記念と思って取得してみた。 取得すると以下のようなパーマリンクがもらえて、修了証が閲覧できるようになる。 https://www.coursera.org/account/accomplishments/verify/WX835R39AB77 もともとアルバイトをしていたはてなでこのコースを利用した勉強会が開催され、それに参加させてもらったのが受講のきっかけだった。 運営の id:takuya-a さんをはじめ皆様にはお世
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