A.I, Data and Software Engineering

Tagtensorflow

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...

Quick Benchmark Colab CPU GPU TPU (XLA-CPU)

CPU TPU GPU

If you ever wonder about the performance differences between CPU, GPU, and TPU for your machine learning project, this article shows a simple benchmark for these three. Memory Subsystem Architecture Central Processing Unit (CPU), Graphics Processing Unit (GPU) and Tensor Processing Unit (TPU) are processors with a specialized purpose and architecture. CPU: A processor designed to solve every...

TF2.0 Warm-up exercises (forked from @chipHuyen Repo)

Petamind A.I

Heard of Ms @huyen chip for her notable yet controversial travelling books back in the day. I enjoy reading but I am not really into travel memoirs. Nevertheless, she did surprise everyone by her achievements by getting in Stanford, teaching TensorFlow, and then became a computer/data scientist. Her story is definitely very inspiring. For ones who don’t know about Ms Huyen, I added an...

Dimension, Dimension, Dimension – Reshape your data

reshape your data

The most basic yet important thing when working with data array is its dimensions. This article will cover several data shapes and reshaping techniques. Why need reshaping data Imagine that you are starving and suddenly given a piece of delicious food. You may try to put it all in your mouth (Fig 1a) and find out it cannot help your hunger. So, you decided to arrange your food so that it not only...

Advanced Keras – Custom loss functions

Petamind A.I

When working on machine learning problems, sometimes you want to construct your own custom loss function(s). This article will introduce abstract Keras backend for that purpose. Keras loss functions From Keras loss documentation, there are several built-in loss functions, e.g. mean_absolute_percentage_error, cosine_proximity, kullback_leibler_divergence etc. When compiling a Keras model, we often...

A.I, Data and Software Engineering

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