A.I, Data and Software Engineering

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Feature Engineering FundamentalS

Petamind A.I

The features you use influence more than everything else the result. No algorithm alone, to my knowledge, can supplement the information gain given by correct feature engineering.— Luca Massaron What is a feature and why we need engineering of it? Basically, all machine learning algorithms use some input data to create outputs. This input data comprise features, which are usually in the form...

deep learning: Linear Autoencoder with Keras

autoencoder schema

This post introduces using linear autoencoder for dimensionality reduction using TensorFlow and Keras. What is a linear autoencoder An autoencoder is a type of artificial neural network used to learn efficient data codings in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network...

Math for ML – Vector norms quick note

math for machine learning

Vector norms are used in many machine learning and computer science problems. This article covers some common norms and related applications. From a high school entrance exam… Remember the day (?/?/1998) when I took an exam to a high school, there was a problem of finding the shortest path from A to B knowing that the person can only go left/right or up/down given the following grid of m x...

New TensorFlow 2.0 vs 1.X – Quick note

Petamind A.I

TensorFlow 2.0 is out! Get hands-on practice at TF World, Oct 28-31. TensorFlow Ads Since the TF2.0 API reference lists have already been made publicly available, TF2.0 is still in RC.2 version. It is expected that the final release will be made available in the next few days (or weeks). What’s new in TF2.0: The obvious different – The version. In Colab, you can force using 2.0 by:...

A.I, Data and Software Engineering

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