Making Netflix Machine Learning Algorithms ReliableJustin BasilicoResearch/Engineering Director at Netflix - Machine Learning and Recommendation Systems
Step 2.5: Choose a Model | Machine Learning | Google for Developers
Step 2.5: Choose a Model Stay organized with collections Save and categorize content based on your preferences. At this point, we have assembled our dataset and gained insights into the key characteristics of our data. Next, based on the metrics we gathered in Step 2, we should think about which classification model we should use. This means asking questions such as, “How do we present the text da
fastText/pretrained-vectors.md at master · facebookresearch/fastText · GitHub
Pre-trained word vectors We are publishing pre-trained word vectors for 294 languages, trained on Wikipedia using fastText. These vectors in dimension 300 were obtained using the skip-gram model described in Bojanowski et al. (2016) with default parameters. Format The word vectors come in both the binary and text default formats of fastText. In the text format, each line contain a word followed by
8 Reasons Why Analytics / Machine Learning Models Fail To Get Deployed
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 - KDnuggets
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
A comprehensive beginner's guide to start ML with Amazon Web Services (AWS)
[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
Adversarial validation, part one - FastML
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
機械学習の分類の話を損失関数と決定境界を中心に整理してみた - once upon a time,
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
Scala製の機械学習フレームワーク「PredictionIO」 | blue engineer blog
こんにちは、エンジニアの渋江です。 前々からGitHubで気になってStarしてた割に中々触る機会がなかったので機械学習の勉強用として触ってみました。 PredictionIOとは Spark上で動作するScala製のオープンソース機械学習フレームワークです。 以下のようなアプリケーションを作ることができます。 ・アイテムのレコメンド（例えば、映画、製品、食品） ・ユーザーの行動を予測 ・アイテムの類似性を識別 ・アイテムのランキング 公式リポジトリ：https://github.com/PredictionIO/PredictionIO/ 今回はレコメンデーションができる様になるまで公式ドキュメント(英語)を見ながら進めていきます。 前提としてMac上での操作になります。 PredictionIOのインストール 対話式でインストールしていきます。 $ bash -c "$(
Comparing Python Clustering Algorithms
Comparing Python Clustering Algorithms¶(Why you should use HDBSCAN)¶There are a lot of clustering algorithms to choose from. The standard sklearn clustering suite has thirteen different clustering classes alone. So what clustering algorithms should you be using? As with every question in data science and machine learning it depends on your data. A number of those thirteen classes in sklearn are sp
All Models of Learning have Flaws – Machine Learning (Theory)
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
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Introduction to Word Embeddings
Bagging, boosting and stacking in machine learning
Greedy Feature Selection for Subspace Clustering
[PDF]TensorFlow:Large-Scale Machine Learning on Heterogeneous Distributed Systems (Preliminary White Paper, November 9, 2015)