A.I, Data and Software Engineering


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...

One-hot encoding matrices demonstration

movie lens automation

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

Petamind A.I

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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