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 (12)

• 3 Reasons to Use Random Forest® Over a Neural Network: Comparing Machine Learning versus Deep Learning - 08 Apr 2020
  
  Both the random forest algorithm and Neural Networks are different techniques that learn differently but can be used in similar domains. Why would you use one over the other?
• 2 Things You Need to Know about Reinforcement Learning – Computational Efficiency and Sample Efficiency - 07 Apr 2020
  
  Experimenting with different strategies for a reinforcement learning model is crucial to discovering the best approach for your application. However, where you land can have significant impact on your system's energy consumption that could cause you to think again about the efficiency of your computations.
• The 4 Best Jupyter Notebook Environments for Deep Learning - 19 Mar 2020
  
  Many cloud providers, and other third-party services, see the value of a Jupyter notebook environment which is why many companies now offer cloud hosted notebooks that are hosted on the cloud. Let's have a look at 3 such environments.
• Enabling the Deep Learning Revolution - 05 Dec 2019
  
  Deep learning models are revolutionizing the business and technology world with jaw-dropping performances in one application area after another. Read this post on some of the numerous composite technologies which allow deep learning its complex nonlinearity.
• Popular Deep Learning Courses of 2019 - 03 Dec 2019
  
  With deep learning and AI on the forefront of the latest applications and demands for new business directions, additional education is paramount for current machine learning engineers and data scientists. These courses are famous among peers, and will help you demonstrate tangible proof of your new skills.
• 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.