A.I, Data and Software Engineering

# CategoryResearch

## 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

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

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...

## One-hot encoding matrices demonstration

This post will demonstrate onehot encoding for a rating matrix, such as movie lens dataset. One-hot encoding Previously, we introduced a quick note for one-hot encoding. It is a representation of categorical variables as binary vectors. It is a group of bits among which the legal combinations of values are only those with a single high (1) bit and all the others low (0) Rating matrix If you are...

## The intuition of Principal Component Analysis

As PCA and linear autoencoder have a close relation, this post introduces again PCA as a powerful dimension reduction tool while skipping many mathematical proofs. PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly...

A.I, Data and Software Engineering

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