Lightning-fast big data exploration with vaex

vaex
big data
jupyter

vaex is a powerful DataFrame library that allows us to visualise, explore, and statistically process huge data files that do not fit into memory in the blink of an eye. I’m coming back to this fantastic library and in this notebook I give just a brief intro of what it can do.

Feb 2, 2023
Lorenzo Posti

Conditioning diffusion models

neural network
diffusion
jupyter

After the intro to diffusion models, let’s take them to the next step by introducing conditioning in these simplified models and let’s do it from scratch. We will build a model able to generate rotation curves with peculiar features that we can specify upfront.

Jan 17, 2023
Lorenzo Posti

Intro to diffusion models from scratch

neural_network
diffusion
jupyter

This notebook is an introduction to diffusion models that are currently state-of-the-art in computer vision and in image generation. I build from scratch a diffusion model able to generate realistic rotation curves starting from purely random noise.

Jan 13, 2023
Lorenzo Posti

vcdisk: a python package for galaxy rotation curves

jupyter
vcdisk

This notebook presents a minimal python package that I wrote to solve Poisson’s equation in galactic disks. vcdisk is an handy toolbox of essential functions to compute the circular velocity of thick disks and flattened bulges of arbitrary surface density.

Dec 8, 2022
Lorenzo Posti

A simple neural network to predict galaxy rotation curves from photometry

neural_network
jupyter

Designing a simple feedforward neural network, made with just linear layers, activations, and batch normalization, to predict rotation curves from galaxy surface brightness profiles. Comparison to the MOND empirical law.

Nov 16, 2022
Lorenzo Posti

Rotation curve decompositions with Gaussian Processes: taking into account data correlations leads to unbiased results

gaussian_processes

Correlations between velocity measurements in disk galaxy rotation curves are usually neglected when fitting dynamical models. This notebook, which accompanies the paper Posti (2022), Res. Notes AAS, 6, 233, shows how data correlations can be taken into account in rotation curve decompositions using Gaussian Processes.

Nov 2, 2022
Lorenzo Posti

Gaussian Processes: modelling correlated noise in a dataset

gaussian_processes
bayesian
jupyter

Independent datapoints are most often just a convenient idealisation which can even hamper your model inference at times and bias your results. Learn how to embrace the reality of correlated noise in the data and marginalize the parameter posteriors with Gaussian Processes.

Oct 17, 2022
Lorenzo Posti

Variational Autoencoder: learning an underlying distribution and generating new data

neural_network
autoencoder
variational autoencoder
basics
jupyter

Constructing an autoencoder that learns the underlying distribution of the input data, generated from a multi-dimensional smooth function f=f(x_1,x_2,x_3,x_4). This can be used to generate new data, sampling from the learned distribution

Oct 7, 2022
Lorenzo Posti

Autoencoder represents a multi-dimensional smooth function

neural_network
autoencoder
basics
jupyter

Setting up a simple Autoencoder neural network to reproduce a dataset obtained by sampling a multi-dimensional smooth function f=f(x_1,x_2,x_3,x_4). As an example I’m using a disc+halo rotation curve model where both components are described by 2-parameters circular velocities

Jun 10, 2022
Lorenzo Posti
 

Ground-up construction of a simple neural network

neural_network
basics
jupyter

Constructing a simple multi-layered neural network (NN) from scratch using pure python and a bit of pytorch. This is mostly my personal re-writing of the fantastic lesson 1 of the fast.ai course Part 2

Mar 22, 2022
Lorenzo Posti

Understanding how basic linear NNs handle non-linearities

neural_network
basics
bayesian
MCMC
jupyter

A deep exploration, an visualization, of how a single-layer linear neural network (NN) is able to approximate non linear behaviours with a just handful of neurons and the magic of activation functions.

Mar 18, 2022
Lorenzo Posti
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