About Kevin Vu

Kevin Vu manages Exxact Corp blog and works with many of its talented authors who write about different aspects of Deep Learning.

Kevin Vu Posts (7)

• Transfer Learning Made Easy: Coding a Powerful Technique - 13 Nov 2019
  
  While the revolution of deep learning now impacts our daily lives, these networks are expensive. Approaches in transfer learning promise to ease this burden by enabling the re-use of trained models -- and this hands-on tutorial will walk you through a transfer learning technique you can run on your laptop.
• Scikit-Learn & More for Synthetic Dataset Generation for Machine Learning - 19 Sep 2019
  
  While mature algorithms and extensive open-source libraries are widely available for machine learning practitioners, sufficient data to apply these techniques remains a core challenge. Discover how to leverage scikit-learn and other tools to generate synthetic data appropriate for optimizing and fine-tuning your models.
• Scikit-Learn vs mlr for Machine Learning - 10 Sep 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 - 03 Sep 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 - 27 Aug 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 - 17 Jul 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.
• Do Conv-nets Dream of Psychedelic Sheep? - 25 Jun 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.