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Recent Posts

Normalising Flows and Neural ODEs

1 A few words about Generative Models 1.1 Introduction 1.2 Latent variables 1.3 Examples of generative models 1.3.1 Generative Adversarial Networks (GANS) 1.3.2 Variational autoencoders 1.
11 September 2020 38 min read Machine Learning, Neural ODEs, Julia

Change of template

I moved to a richer Hugo template, partly to move things around under the hood, but importantly it gives a sounder platform for the future. However, it took many hours of frustration to get blogdown and the template to nicely render \(\LaTeX\) formulas.
12 August 2020 1 min read miscellaneous

Presentation at the Kong Kong Machine Learning meetup

I recently made a presentation at the regular Hong Kong Machine Learning meetup organised by Gautier Marti. The presentation was an introduction to Julia and used as an example a SEIR model COVID-19 I had written.
30 April 2020 1 min read Julia, Machine Learning

Forecasting the progression of COVID-19

The Neherlab COVID-19 forecast model Basic assumptions Overview Age cohorts Severity Seasonality Transmission reduction Details of the model Population compartments Model parameters Infection After infection Load data Initialise parameters Fixed constants Infrastructure Parameter vector Population Parameters vector Differential equation solver Bilibliography The Neherlab COVID-19 forecast model using CSV, Dates; using DataFrames, DataFramesMeta; using Plots, PyPlot; using DifferentialEquations; This is more a data science post than machine learning.
25 March 2020 21 min read Data Science, Julia

RNN Compressive Memory Part 1: A high level introduction.

Recurrent Neural Networks (RNN) From simple RNNs to LSTMs Long/Short Term Memory RNNs Attention Beyond LSTM: Transformers Transformer-XL Compressive Transformers Introduction Compression scheme Compression training Summary This is the first post of series dedicated to Compressive Memory of Recurrent Neural Networks.
7 March 2020 12 min read Machine Learning, Neural Network
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Gitbooks

Lending Club peer-to-peer loans scoring

Credit ratings, logistic regression, scoring systems and loan pricing using a database of peer-to-peer loans.

Movielens Recommender System

Recommender systems on movie choices, low-rank matrix factorisation with stochastic gradient descent using the Movielens dataset

Recent Talks

Emmanuel Rialland
30 April 2020 20:17
An Introduction to Julia

An Introduction to Julia

Recent talk to the Hong Kong Machine Learning Meetup about the Julia programming language and an example COVID-19 model optimisation
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Emmanuel Rialland

Emmanuel Rialland

Consultant Finance - Machine Learning

#> whoami

I am a corporate banker with two decades experience and successful machine learning / data science project track record. It has been noted that my career has not been very linear…

My education was completely scientific with a couple of engineering degrees with a particular passion for mathematics. Add to that a life-long fascination for artificial intelligence. I can trace that back to reading a particular birthday present: Gödel, Escher and Bach. This book held me riveted at 17.

BUT….

But travelling and discovering the planet was a powerful magnet that I could not resist and it turned out that banking and finance first gave me that opportunity. Followed about 20 years doing, and enjoying, practising that trade in a few countries. And now time has come to go back my roots.

I am available for consulting and, if there is a great fit, something more permanent. Get in touch and let us have a chat. 5 minutes is all it takes.

You can find more about me on my LinkedIn profile.

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