#### 2019

- My course on Hyperparameter Tuning in R is now on Data Camp!
- Posts
- Lecture slides: Real-World Data Science (Fraud Detection, Customer Churn & Predictive Maintenance)
- How do Convolutional Neural Nets (CNNs) learn? + Keras example
- Don’t reinvent the wheel: making use of shiny extension packages. Join MünsteR for our next meetup!
- February 21st & 22nd: End-2-End from a Keras/TensorFlow model to production

##### January

#### 2018

- Code for case study - Customer Churn with Keras/TensorFlow and H2O
- Trust in ML models. Slides from TWiML & AI EMEA Meetup + iX Articles

##### December

- Machine Learning Basics - Gradient Boosting & XGBoost
- Slides from my talks about Demystifying Big Data and Deep Learning (and how to get started)
- TWIMLAI European Online Meetup about Trust in Predictions of ML Models
- 'How do neural nets learn?' A step by step explanation using the H2O Deep Learning algorithm.

##### November

- Machine Learning Basics - Random Forest
- Slides from my talk at the R-Ladies Meetup about Interpretable Deep Learning with R, Keras and LIME
- Slides from my m-cubed talk about Explaining complex machine learning models with LIME
- Sketchnotes from TWiML&AI: Evaluating Model Explainability Methods with Sara Hooker
- Using R to help plan the future of transport. Join MünsteR for our next meetup!
- Image clustering with Keras and k-Means

##### October

- Slides from talk: 'Decoding The Black Box' at the Frankfurt Data Science Meetup
- I'll be talking about 'Decoding The Black Box' at the Frankfurt Data Science Meetup
- November 8th & 9th in Munich: Workshop on Deep Learning with Keras and TensorFlow in R
- I'll be talking at the R-Ladies Meetup about Interpretable Deep Learning with R, Keras and LIME

##### September

- W-JAX 2018 talk: Deep Learning - a Primer
- Slides from my SAP webinar: Explaining Keras Image Classification Models with LIME
- MünsteR Meetup on Blog Mining: Deriving the success of blog posts from metadata and text data.

##### August

- Explaining Black-Box Machine Learning Models - Code Part 2: Text classification with LIME
- Explaining Black-Box Machine Learning Models - Code Part 1: tabular data + caret + iml
- My upcoming conference talks & workshops: M-cubed, ML Summit & data2day

##### July

- Addendum: Text-to-Speech with the googleLanguageR package
- Code for Workshop: Introduction to Machine Learning with R
- Text-to-speech with R
- Explaining Keras image classification models with lime
- Sketchnotes from TWiML&AI: Practical Deep Learning with Rachel Thomas
- It's that easy! Image classification with keras in roughly 100 lines of code.

##### June

- rOpenSci unconference 2018 + introduction to TensorFlow Probability & the 'greta' package
- July 5th & 6th in Münster: Workshop on Deep Learning with Keras and TensorFlow in R
- Sketchnotes from TWiML&AI: Adversarial Attacks Against Reinforcement Learning Agents with Ian Goodfellow & Sandy Huang

##### May

- Comparing dependencies of popular machine learning packages with `pkgnet`
- Slides from my JAX 2018 talk: Deep Learning - a Primer
- Sketchnotes from TWiML&AI #121: Reproducibility and the Philosophy of Data with Clare Gollnick
- Update: Can we predict flu outcome with Machine Learning in R?
- Look, something shiny: How to use R Shiny to make Münster traffic data accessible. Join MünsteR for our next meetup!
- HH Data Science Meetup slides: Explaining complex machine learning models with LIME
- Sketchnotes from TWiML&AI #124: Systems and Software for Machine Learning at Scale with Jeff Dean
- Meetup slides: Introducing Deep Learning with Keras

##### April

- Join MünsteR for our next meetup on deep learning with Keras and R
- My upcoming meetup talks about Deep Learning with Keras and explaining complex Machine Learning Models with LIME
- Sketchnotes from TWiML&AI #115: Scaling Machine Learning at Uber with Mike Del Balso
- Another Game of Thrones network analysis - this time with tidygraph and ggraph

##### March

- Sketchnotes from TWiML&AI #111: Learning “Common Sense” and Physical Concepts with Roland Memisevic
- April 12th & 13th in Hamburg: Workshop on Deep Learning with Keras and TensorFlow in R
- Announcing my talk about explainability of machine learning models at Minds Mastering Machines conference
- I talk about machine learning with Daniel Mies (Podcast in German, though)

##### February

- JAX 2018 talk announcement: Deep Learning - a Primer
- Sketchnotes from TWiML&AI #94: Neuroevolution: Evolving Novel Neural Network Architectures with Kenneth Stanley
- Join MünsteR for our next meetup on obtaining functional implications of gene expression data with R
- Sketchnotes from TWiML&AI #92: Learning State Representations with Yael Niv
- How to make your machine learning model available as an API with the plumber package
- Sketchnotes from TWiML&AI #91: Philosophy of Intelligence with Matthew Crosby
- Looking beyond accuracy to improve trust in machine learning
- TWiMLAI talk 88 sketchnotes: Using Deep Learning and Google Street View to Estimate Demographics with Timnit Gebru

##### January

#### 2017

- Registration now open for workshop on Deep Learning with Keras and TensorFlow using R
- Explaining Predictions of Machine Learning Models with LIME - Münster Data Science Meetup

##### December

- MICE (Multiple Imputation by Chained Equations) in R - sketchnotes from MünsteR Meetup
- Workshop on Deep Learning with Keras and TensorFlow in R
- How to combine point and boxplots in timeline charts with ggplot2 facets
- Explore Predictive Maintenance with flexdashboard

##### November

- Blockchain & distributed ML - my report from the data2day conference
- From Biology to Industry. A Blogger’s Journey to Data Science.
- Why I use R for Data Science - An Ode to R
- Moving my blog to blogdown
- Data Science for Fraud Detection
- Migrating from GitHub to GitLab with RStudio (Tutorial)