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EXTREME LEARNING MACHINES ELM is a Chinese invention. Imagine a classic feed-forward neural network with one hidden layer, subtract backpropagation and you have an ELM. The input-hidden weights are constant - they are apparently initialized analytically so even though they are semi-random the thing works. The model learns only the hidden-output weights, which amountsto
CONVERTING CATEGORICAL DATA INTO NUMBERS WITH PANDAS AND This functionality is available in some software libraries. We load data using Pandas, then convert categorical columns with DictVectorizer from scikit-learn. Pandas is a popular Python library inspired by data frames in R. It allows easier manipulation of tabular numeric and non-numeric data. Downsides: not very intuitive, somewhatsteep
PIPING IN R AND IN PANDAS In R community, there’s this one guy, Hadley Wickam, who by himself made R great again. One of the many, many things he came up with - so many they call it a hadleyverse - is the dplyr package, which aims to make data analysis easy and fast. It works by allowing a user to take a data frame and apply to it a pipeline of operations resulting in a desired outcome (an example in just a minute). CLASSIFYING TEXT WITH BAG-OF-WORDS: A TUTORIAL WHAT YOU WANTED TO KNOW ABOUT MEAN AVERAGE PRECISION First, we will get M out of the way. MAP is just an average of APs, or average precision, for all users. In other words, we take the mean for Average Precision, hence Mean Average Precision. If we have 1000 users, we sum APs for each user and divide the sum by 1000. This isMAP.
GOODBOOKS-10K: A NEW DATASET FOR BOOK RECOMMENDATIONS CLASSIFIER CALIBRATION WITH PLATT'S SCALING AND ISOTONICSEE MORE ONFASTML.COM
A VERY FAST DENOISING AUTOENCODER WHAT IS BETTER: GRADIENT-BOOSTED TREES, OR A RANDOM FORESTSEE MORE ONFASTML.COM
FASTMLGOOGLE'S PRINCIPLES ON AI WEAPONS, MASS SURVEILLENCE, AND SIGNING OUTCONTENTSGOODBOOKS-10K 2020-05-12. The most common form of cheating in first person shooter games is wall-hacking, or seeing enemy players through obstacles. We propose a solution to this problem building on a mechanism already used in some professional e-sports matches: taking random screenshots during gameplay. If a game takes screenshots and uploads them to“the
EXTREME LEARNING MACHINES ELM is a Chinese invention. Imagine a classic feed-forward neural network with one hidden layer, subtract backpropagation and you have an ELM. The input-hidden weights are constant - they are apparently initialized analytically so even though they are semi-random the thing works. The model learns only the hidden-output weights, which amountsto
CONVERTING CATEGORICAL DATA INTO NUMBERS WITH PANDAS AND This functionality is available in some software libraries. We load data using Pandas, then convert categorical columns with DictVectorizer from scikit-learn. Pandas is a popular Python library inspired by data frames in R. It allows easier manipulation of tabular numeric and non-numeric data. Downsides: not very intuitive, somewhatsteep
PIPING IN R AND IN PANDAS In R community, there’s this one guy, Hadley Wickam, who by himself made R great again. One of the many, many things he came up with - so many they call it a hadleyverse - is the dplyr package, which aims to make data analysis easy and fast. It works by allowing a user to take a data frame and apply to it a pipeline of operations resulting in a desired outcome (an example in just a minute). CLASSIFYING TEXT WITH BAG-OF-WORDS: A TUTORIAL WHAT YOU WANTED TO KNOW ABOUT MEAN AVERAGE PRECISION First, we will get M out of the way. MAP is just an average of APs, or average precision, for all users. In other words, we take the mean for Average Precision, hence Mean Average Precision. If we have 1000 users, we sum APs for each user and divide the sum by 1000. This isMAP.
GOODBOOKS-10K: A NEW DATASET FOR BOOK RECOMMENDATIONS CLASSIFIER CALIBRATION WITH PLATT'S SCALING AND ISOTONICSEE MORE ONFASTML.COM
A VERY FAST DENOISING AUTOENCODER WHAT IS BETTER: GRADIENT-BOOSTED TREES, OR A RANDOM FORESTSEE MORE ONFASTML.COM
EXTREME LEARNING MACHINES ELM is a Chinese invention. Imagine a classic feed-forward neural network with one hidden layer, subtract backpropagation and you have an ELM. The input-hidden weights are constant - they are apparently initialized analytically so even though they are semi-random the thing works. The model learns only the hidden-output weights, which amountsto
POPULAR: TOP TEN MOST VIEWED PAGES, AS REPORTED BY GOOGLE These are the pages that received the most unique pageviews ever, as of January 2017. Unique pageviews is the number of visits during which the specified page was viewed at least once. The order is basically the same as when counting pageviews, or hits. The numbers in parens show positions in the ADVERSARIAL VALIDATION, PART ONE Adversarial validation, part one. 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 HOW TO USE PD.GET_DUMMIES() WITH THE TEST SET Two solutions come to mind. One is two pd.concat ( ( train, test )), get_dummies () and then split the set back. If columns sets in train and test differ, you can extract and concatenate just the categorical columns to encode. Another way is to add the missing columns, filled WHAT YOU WANTED TO KNOW ABOUT MEAN AVERAGE PRECISION First, we will get M out of the way. MAP is just an average of APs, or average precision, for all users. In other words, we take the mean for Average Precision, hence Mean Average Precision. If we have 1000 users, we sum APs for each user and divide the sum by 1000. This isMAP.
TUNING HYPERPARAMS AUTOMATICALLY WITH SPEARMINT Enter Spearmint, a piece of software to automatically tune hyperparams. Now you can see the promise it offers. We will concentrate on how to use it in practice, because a learning curve might be quite steep, even though the README is pretty good. INTERACTIVE IN-BROWSER 3D VISUALIZATION OF DATASETS Interactive in-browser 3D visualization of datasets. In this post we’ll be looking at 3D visualization of various datasets using the data-projector software from Datacratic. The original demo didn’t impress us initially as much as it could, because the data there is synthetic - it shows a bunch of small spheres in rainbow colors. KAGGLE JOB RECOMMENDATION CHALLENGE 2012-08-27. This is an introduction to Kaggle job recommendation challenge. It looks a lot like a typical collaborative filtering thing (with a lot of extra information), but not quite. Spot these two big differences: There are no explicit ratings. Instead, there’s info about which jobs user applied to. This is known as one-class MICHAEL JORDAN ON DEEP LEARNING Michael Jordan on deep learning. 2014-09-14. On September 10th Michael Jordan, a renowned statistician from Berkeley, did Ask Me Anything on Reddit. These are his thoughts on deep learning. My first and main reaction is that I’m totally happy that any area of machine learning (aka, statistical inference and decision-making; see my other post NUMERAI - LIKE KAGGLE, BUT WITH A CLEAN DATASET, TOP TEN Numerai is an attempt at a hedge fund crowd-sourcing stock market predictions. It presents a Kaggle-like competition, but with a fewwelcome twists.
FASTMLBACKGROUND IMAGESLINKSREVISITING NUMERAICONTENTSGOODBOOKS-10KABOUT The most common form of cheating in first person shooter games is wall-hacking, or seeing enemy players through obstacles. We propose a solution to this problem building on a mechanism already used in some professional e-sports matches: taking random screenshots duringgameplay.
EXTREME LEARNING MACHINES What do you get when you take out backpropagation out of a multilayer perceptron? You get an extreme learning machine, a non-linear modelwith the
PIPING IN R AND IN PANDAS In R community, there’s this one guy, Hadley Wickam, who by himself made R great again. One of the many, many things he came up with - so many they call it a hadleyverse - is the dplyr package, which aims to make data analysis easy and fast. It works by allowing a user to take a data frame and apply to it a pipeline of operations resulting in a desired outcome (an example in just a minute). CLASSIFYING TIME SERIES USING FEATURE EXTRACTION When you want to classify a time series, there are two options. One is to use a time series specific method. An example would be LSTM, or a recurrent neural network in general. TUNING HYPERPARAMS AUTOMATICALLY WITH SPEARMINT CLASSIFIER CALIBRATION WITH PLATT'S SCALING AND ISOTONICSEE MORE ONFASTML.COM
WHAT YOU WANTED TO KNOW ABOUT MEAN AVERAGE PRECISION Let’s say that there are some users and some items, like movies, songs or jobs. Each user might be interested in some items. The client asks us to recommend a few items (the number is x) for each user. ONE WEIRD REGULARITY OF THE STOCK MARKET MICHAEL JORDAN ON DEEP LEARNING On September 10th Michael Jordan, a renowned statistician from Berkeley, did Ask Me Anything on Reddit. These are his thoughts on deep learning. My first and main reaction is that I’m totally happy that any area of machine learning (aka, statistical inference and decision-making; see my other post :-) is beginning to make impact on real-world problems. KAGGLE JOB RECOMMENDATION CHALLENGE This is an introduction to Kaggle job recommendation challenge.It looks a lot like a typical collaborative filtering thing (with a lot of extra information), but not quite. Spot these two big differences: FASTMLBACKGROUND IMAGESLINKSREVISITING NUMERAICONTENTSGOODBOOKS-10KABOUT The most common form of cheating in first person shooter games is wall-hacking, or seeing enemy players through obstacles. We propose a solution to this problem building on a mechanism already used in some professional e-sports matches: taking random screenshots duringgameplay.
EXTREME LEARNING MACHINES What do you get when you take out backpropagation out of a multilayer perceptron? You get an extreme learning machine, a non-linear modelwith the
PIPING IN R AND IN PANDAS In R community, there’s this one guy, Hadley Wickam, who by himself made R great again. One of the many, many things he came up with - so many they call it a hadleyverse - is the dplyr package, which aims to make data analysis easy and fast. It works by allowing a user to take a data frame and apply to it a pipeline of operations resulting in a desired outcome (an example in just a minute). CLASSIFYING TIME SERIES USING FEATURE EXTRACTION When you want to classify a time series, there are two options. One is to use a time series specific method. An example would be LSTM, or a recurrent neural network in general. TUNING HYPERPARAMS AUTOMATICALLY WITH SPEARMINT CLASSIFIER CALIBRATION WITH PLATT'S SCALING AND ISOTONICSEE MORE ONFASTML.COM
WHAT YOU WANTED TO KNOW ABOUT MEAN AVERAGE PRECISION Let’s say that there are some users and some items, like movies, songs or jobs. Each user might be interested in some items. The client asks us to recommend a few items (the number is x) for each user. ONE WEIRD REGULARITY OF THE STOCK MARKET MICHAEL JORDAN ON DEEP LEARNING On September 10th Michael Jordan, a renowned statistician from Berkeley, did Ask Me Anything on Reddit. These are his thoughts on deep learning. My first and main reaction is that I’m totally happy that any area of machine learning (aka, statistical inference and decision-making; see my other post :-) is beginning to make impact on real-world problems. KAGGLE JOB RECOMMENDATION CHALLENGE This is an introduction to Kaggle job recommendation challenge.It looks a lot like a typical collaborative filtering thing (with a lot of extra information), but not quite. Spot these two big differences:ABOUT - FASTML
About. This site is brought to you by the letters “M” and “L”. It is meant to tackle interesting topics in machine learning while being entertaining and easy to read and understand. HOW TO USE PD.GET_DUMMIES() WITH THE TEST SET It turns out that Converting categorical data into numbers with Pandas and Scikit-learn has become the most popular article on this site. Let’s revisit the topic and look at Pandas’ get_dummies() more closely. Using the function is straightforward - you specify which columns you want encoded and get a dataframe with original columns replaced with one-hot encodings.REVISITING NUMERAI
In this article, we revisit Numerai and their weekly data science tournament. New developments include a much larger dataset, tougherrequirements
EVALUATING RECOMMENDER SYSTEMS Recommendation as ranking. We approach recommendation as a ranking task, meaning that we’re mainly interested in a relatively few itemsthat we
RUNNING THINGS ON A GPU You’ve heard about running things on a graphics card, but have you tried it? All you need to taste the speed is a Nvidia card and somesoftware.
DEEP LEARNING MADE EASY As usual, there’s an interesting competition at Kaggle: The Black Box. It’s connected to ICML 2013 Workshop on Challenges in Representation Learning, held by the deep learning guys from Montreal. ONE WEIRD REGULARITY OF THE STOCK MARKET Everybody had the fantasy of predicting the stock market. We investigated the subject in Are stocks predictable?.In short, they are not, at least the prices. The next step would be to go from prices to volatility measures. WHAT YOU WANTED TO KNOW ABOUT MEAN AVERAGE PRECISION Let’s say that there are some users and some items, like movies, songs or jobs. Each user might be interested in some items. The client asks us to recommend a few items (the number is x) for each user. GOODBOOKS-10K: A NEW DATASET FOR BOOK RECOMMENDATIONS There have been a few recommendations datasets for movies (Netflix, Movielens) and music (Million Songs), but not for books. That is,until now. The
WHAT IS BETTER: GRADIENT-BOOSTED TREES, OR A RANDOM FOREST Folks know that gradient-boosted trees generally perform better than a random forest, although there is a price for that: GBT have a few hyperparams to tune, while random forest is practically tuning-free.Let’s look at what the literature says about how these two methods compare.FASTML
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ONE WEIRD REGULARITY OF THE STOCK MARKET2018-12-11
Everybody had the fantasy of predicting the stock market. We investigated the subject in Are stocks predictable? . In short, they are not, at least the prices. The next step would be to go from prices to volatility measures. The reason is that one can use the volatility to properly price stock options using the Black-Scholes model. Wikipedia
says that the formula has only one parameter that cannot be directly observed in the market: the average future volatility of the underlying asset. Therefore, the question is, can one predict thatvolatility?
Read on →
CLASSIFYING TIME SERIES USING FEATURE EXTRACTION2018-10-09
When you want to classify a time series, there are two options. One is to use a time series specific method. An example would be LSTM, or a recurrent neural network in general. The other one is to extract features from the series and use them with normal supervised learning. In this article, we look at how to automatically extract relevant features with a Python package called tsfresh.Read on →
GOOGLE’S PRINCIPLES ON AI WEAPONS, MASS SURVEILLENCE, AND SIGNINGOUT
2018-07-02
In June Google published its ”AI principles”, the post
signed by the CEO himself. It talks about AI sensors for predicting the risk of wildfires. Of farmers using AI to monitor the health of their herds. Of doctors starting to use AI to help diagnose cancer and prevent blindness. Great stuff! We take a look at the context.Read on →
HOW TO USE THE PYTHON DEBUGGER2018-02-28
This article is not about machine learning, but about a piece of software engineering that often comes handy in data science practice. When writing code, everybody gets errors. Sometimes it is difficult to debug them. Using a debugger may help, but can also be intimidating. This is a TLDR tutorial on using pdb in IPython, focused on looking at variables inside functions.Read on →
PREPARING CONTINUOUS FEATURES FOR NEURAL NETWORKS WITH GAUSSRANK2018-01-22
We present a novel method for feature transformation, akin to standardization. The method comes from Michael Jahrer, who recently has won another competition and afterwards shared the approach heused.
Read on →
TWO FACES OF OVERFITTING2017-12-05
Overfitting is on of the primary problems, if not THE primary problem in machine learning. There are many aspects to it, but in a general sense, overfitting means that estimates of performance on unseen test examples are overly optimistic. That is, a model generalizes worsethen expected.
We explain two common cases of overfitting: including information from a test set in training, and the more insidious form: overusing avalidation set.
Read on →
GOODBOOKS-10K: A NEW DATASET FOR BOOK RECOMMENDATIONS2017-11-29
There have been a few recommendations datasets for movies (Netflix, Movielens) and music (Million Songs), but not for books. That is,until now.
Read on →
REVISITING NUMERAI
2017-10-17
In this article, we revisit Numerai and their weekly data science tournament. New developments include a much larger dataset, tougher requirements for models, and biggerpayouts.
Read on →
IT’S EMBARASSING, REALLY2017-09-18
In August, we published the first version of goodbooks-10k , a new dataset for book recommendations. By pure chance, that coincided with a proclamation of Kaggle Datasets Awards. Oh, how we hoped to get one!Read on →
INTRODUCTION TO POINTER NETWORKS2017-07-03
Pointer networks are a variation of the sequence-to-sequence model with attention. Instead of translating one sequence into another, they yield a succession of pointers to the elements of the input series. The most basic use of this is ordering the elements of a variable-length sequence or set.Read on →
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* One weird regularity of the stock market * Classifying time series using feature extraction * Google’s principles on AI weapons, mass surveillence, andsigning out
* How to use the Python debugger * Preparing continuous features for neural networks with GaussRank * Two faces of overfitting * Goodbooks-10k: a new dataset for book recommendationsposts.
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