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 Efficient Distributed Training in Deep Learning

Efficient Distributed Training in Deep Learning

  • Posted by Daitan Innovation Team
  • On January 21, 2022
  • Deep Learning, DevOps, Machine Learning
At the heart of any deep learning system, there is a significant effort in building it. In the last few years, scaling deep learning models has become one of the most promising paths for artificial intelligence, specifically for large AI laboratories like OpenAI and DeepMind. To have a clear feeling of this trend, let us examine recent large language models (LLMs) such as BERT and GPT-3.
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 Daitan Data Scientist Presents at Ninth International Conference on Learning Representations (ICLR) 2021

Daitan Data Scientist Presents at Ninth International Conference on Learning Representations (ICLR) 2021

  • Posted by Marketing Daitan
  • On May 7, 2021
  • AI, Data Science, Deep Learning
Daitan is proud to announce that at the Ninth International Conference on Learning Representations (ICLR) our Data Scientist, Thalles Santos SIlva is presenting (virtually) the paper titled, Consistent Assignment for Representation Learning,  which is being held during the Energy Based Models Workshop session at ICLR2021 on May 7th, 2021. The presentation introduces Consistent Assignment for […]
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 Building a Voice Recognition System with PyTorch by Taking Advantage of Computer Vision Techniques

Building a Voice Recognition System with PyTorch by Taking Advantage of Computer Vision Techniques

  • Posted by Daitan Innovation Team
  • On June 18, 2020
  • AI, Biometrics, Computer Vision, Deep Learning, NLP, PyTorch, Voice Recognition
In this piece we describe how we built a reasonably performing Voice Recognition System with PyTorch, using deep learning Computer Vision techniques. With results as good as 90.2% accuracy using different training and testing samples, with only 25% of the original dataset size, we demonstrate how it is currently possible for different AI domains to leverage knowledge from each other to improve their techniques and outcomes.
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 Privacy-Preserving Data Sharing for Data Science

Privacy-Preserving Data Sharing for Data Science

  • Posted by Daitan Innovation Team
  • On April 15, 2020
  • Artificial Intelligence, Data, Data Science, Data Sharing, Deep Learning, Differential Privacy, Privacy
In the last 2 decades, with the increasing availability of sensors and the popularity of the internet, data has never been so ubiquitous. Yet, having access to personal data to perform statistical analysis is hard. In fact, that is one of the main reasons we, as data analysts, spend so much time doing research using “toy” datasets, instead of using real-world data.
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 Assessing Audio Quality with Deep Learning

Assessing Audio Quality with Deep Learning

  • Posted by Daitan Innovation Team
  • On February 12, 2020
  • Deep Learning, Tensor Flow 2.0, VoIP
How to train a Deep Learning system to estimate Mean Opinion Score (MOS) using TensorFow 2.0. -- If you’ve ever used VoIP (Voice Over IP) applications like Skype or Hangouts, you know that audio degradation can be a problem. In video or audio conferences, perhaps with clients and prospects, audio quality is important.
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 The Fundamental Tool That Data Scientists Can’t Miss

The Fundamental Tool That Data Scientists Can’t Miss

  • Posted by Daitan Innovation Team
  • On December 20, 2019
  • Convex Optimization, Data Science, Deep Learning
How business requirements can prevent you from using available Machine Learning tools and what to do about it. -- When hearing the term Convex Optimization, most people will immediately start talking about how gradient descent is the most awesome thing there is, how we can add momentum to it, how can we choose, adapt, or even circumvent the choice of the step-size parameter, and so on. However, in reality, convex optimization goes well beyond gradient descent and its variants.
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 How To Build a Deep Audio De-Noiser Using TensorFlow 2.0

How To Build a Deep Audio De-Noiser Using TensorFlow 2.0

  • Posted by Daitan Innovation Team
  • On December 1, 2019
  • AI, Audio, Audio De-noiser, Deep Learning, Tensor Flow 2.0
In this article, we tackle the problem of speech de-noising using Convolutional Neural Networks (CNNs). Given a noisy input signal, we aim to build a statistical model that can extract the clean signal (the source) and return it to the user. Here, we focus on source separation of regular speech signals from ten different types of noise often found in an urban street environment.
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