A.I, Data and Software Engineering


Fast uniform negative sampling for rating matrix

Petamind A.I

Sometimes, we want to reduce the training time by using a subset of a very large dataset while the negative samples outnumbers the positive ones, e.g. word embedding. Another situation when we deal with implicit data. In this case, we may need to populate new data for negative values. This post demonstrates how to generate data for training using uniform negative sampling. The data Originally...

Predict coronavirus deaths by days

As the pandemic is going on with an increasing number of deaths daily, let create a simple model to predict the deaths caused by 2019-nCoV (Wuhan Coronavirus). The 2019-nCoV death data I grab the death toll data from World Meters website. DateDaily DeathsFeb. 889Feb. 786……Jan. 2416Jan. 238 Plot the data Firstly, we transform the table into a Pandas data frame. 123 death_toll =...

Common Loss functions and their uses – quick note


Machines learn by means of a loss function which reflects how well a specific model performs with the given data. If predictions deviate too much from actual results, loss function would yield a very large value. Gradually, with \(optimization\) function, parameters are modified accordingly to reduce the error in prediction. In this article, we will quickly review some common loss functions and...

Generate data on the fly – Keras data generator

keras data generator

Previously, we train our model using the pre-generated dataset, for example, in the recommender system or recurrent neural network. In this article, we will demonstrate using a generator to produce data on the fly for training a model. Keras Data Generator with Sequence There are a couple of ways to create a data generator. However, Tensorflow Keras provides a base class to fit dataset as a...

Data Wrangling quick note

Petamind A.I

Data wrangling (munging), like most data analytics processes, is an iterative one – the practitioner will need to carry out these steps repeatedly in order to produce the results he desires. There are six broad steps to data wrangling, which are: 1.      Discovering In this step, the data is to be understood more deeply. Before implementing methods to clean it, you...

A.I, Data and Software Engineering

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