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Back to the Future with Adaptive Backtesting
Why we ended up building a Time Series Adaptive ML Engine from scratch. Adaptive Backtesting and Composite Models with speed!
 • 
Daniel Szemerey
 and 
Mark Aron Szulyovszky
New
Say Goodbye to Aggregate Metrics: Track Your Time-Series Predictions over Time
Get Insights into Your Forecasting Model with Krisi and Be Aware of Regime Changes
 • 
Daniel Szemerey
 and 
Mark Aron Szulyovszky
Zen and the Art of Generalisation
Is it enough to report performance on a single dataset? Can we trust the reported improvement of new model architectures? We created 144 experiments for…
 • 
Daniel Szemerey
 and 
Mark Aron Szulyovszky
The dark side of Graph Neural Networks
The current limitations of Graph Neural Networks. We continue our two part series on ML on Graphs, by asking: could graphs replace other domain specific…
 • 
Daniel Szemerey
 and 
Mark Aron Szulyovszky
How I Learned to Stop Worrying and Love Graphs
Why Machine Learning on Graphs could be the future of AI. Many researchers are doubling down on scaling models like GPT-3 on an ever bigger corpus of…
 • 
Daniel Szemerey
 and 
Mark Aron Szulyovszky
Two Mortals’ Journey into a Deep Reinforcement Learning Project
Deep Reinforcement Learning is about learning from mistakes - although all machine learning algorithms depend on making sense of the magnitude of error…
 • 
Daniel Szemerey
 and 
Mark Aron Szulyovszky
Welcome to Applied Exploration
This is Applied Exploration, a newsletter about SOTA Machine Learning in practice.
 • 
Daniel Szemerey
 and 
Mark Aron Szulyovszky
Applied Exploration
Applied Exploration
The official blog of Dream Faster (dreamfaster.ai), specialized in Nowcasting/Forecasting and Machine Learning.
Open-Core Libraries
FoldA Time Series Cross-Validation library that lets you build, deploy and update composite models easily.
KrisiLightweight Evaluation of Time Series Forecasting (Classification and Regression) with powerful Reporting.
Fold-ModelsModels and wrappers for 3rd party models to be used with `fold` (github.com/dream-faster/fold)
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