- Scikit-Learn vs mlr for Machine Learning - Sep 10, 2019.
How does the scikit-learn machine learning library for Python compare to the mlr package for R? Following along with a machine learning workflow through each approach, and see if you can gain a competitive advantage by knowing both frameworks.
- TensorFlow vs PyTorch vs Keras for NLP - Sep 3, 2019.
These three deep learning frameworks are your go-to tools for NLP, so which is the best? Check out this comparative analysis based on the needs of NLP, and find out where things are headed in the future.
- TensorFlow 2.0: Dynamic, Readable, and Highly Extended - Aug 27, 2019.
With substantial changes coming with TensorFlow 2.0, and the release candidate version now available, learn more in this guide about the major updates and how to get started on the machine learning platform.
- A Summary of DeepMind’s Protein Folding Upset at CASP13 - Jul 17, 2019.
Learn how DeepMind dominated the last CASP competition for advancing protein folding models. Their approach using gradient descent is today's state of the art for predicting the 3D structure of a protein knowing only its comprising amino acid compounds.
- Examining the Transformer Architecture – Part 2: A Brief Description of How Transformers Work - Jul 2, 2019.
As The Transformer may become the new NLP standard, this review explores its architecture along with a comparison to existing approaches by RNN.
- Do Conv-nets Dream of Psychedelic Sheep? - Jun 25, 2019.
In deep learning, understanding your model well enough to interpret its behavior will help improve model performance and reduce the black-box mystique of neural networks.