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Topics: AI | Data Science | Data Visualization | Deep Learning | Machine Learning | NLP | Python | R | Statistics

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 (39)

  • Not Only for Deep Learning: How GPUs Accelerate Data Science & Data Analytics - 26 Jul 2021
    Modern AI/ML systems’ success has been critically dependent on their ability to process massive amounts of raw data in a parallel fashion using task-optimized hardware. Can we leverage the power of GPU and distributed computing for regular data processing jobs too?
  • Understanding BERT with Hugging Face - 20 Jul 2021
    We don’t really understand something before we implement it ourselves. So in this post, we will implement a Question Answering Neural Network using BERT and a Hugging Face Library.
  • 7 Open Source Libraries for Deep Learning Graphs - 15 Jul 2021
    In this article we’ll go through 7 up-and-coming open source libraries for graph deep learning, ranked in order of increasing popularity.
  • How to Use NVIDIA GPU Accelerated Libraries - 01 Jul 2021
    If you are wondering how you can take advantage of NVIDIA GPU accelerated libraries for your AI projects, this guide will help answer questions and get you started on the right path.
  • Overview of AutoNLP from Hugging Face with Example Project - 21 Jun 2021
    AutoNLP is a beta project from Hugging Face that builds on the company’s work with its Transformer project. With AutoNLP you can get a working model with just a few simple terminal commands.
  • The 7 Best Open Source AI Libraries You May Not Have Heard Of - 09 Jun 2021
    AI researchers today have many exciting options for working with specialized tools. Although starting original projects from scratch is often not necessary, knowing which existing library to leverage remains a challenge. This list of generally unknown yet awesome, open-source libraries offers an interesting collection to consider for state-of-the-art research that spans from automatic machine learning to differentiable quantum circuits.
  • Disentangling AI, Machine Learning, and Deep Learning - 04 May 2021
    The field of Artificial Intelligence is extremely broad and captures a winding history through the evolution of various sub-fields that experienced many ups and downs over the years. Appreciating AI within its historical contexts will enhance your communication with the public, colleagues, and potential hiring managers, as well as guide your thinking as you progress in the application and study of state-of-the-art techniques.
  • DeepMind’s AlphaFold & the Protein Folding Problem - 10 Mar 2021
    Recently, DeepMind's AlphaFold made impressive headway in the protein structure prediction problem. Read this for an overview and explanation.
  • GPT-2 vs GPT-3: The OpenAI Showdown - 17 Feb 2021
    Thanks to the diversity of the dataset used in the training process, we can obtain adequate text generation for text from a variety of domains. GPT-2 is 10x the parameters and 10x the data of its predecessor GPT.
  • Vision Transformers: Natural Language Processing (NLP) Increases Efficiency and Model Generality - 02 Feb 2021
    Why do we hear so little about transformer models applied to computer vision tasks? What about attention in computer vision networks?

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