How to Build Your Own Intelligent Assistant Using RASA
- Posted by Daitan Innovation Team
- On August 1, 2020
Chatbots are everywhere. So are the t
Building a Voice Recognition System with PyTorch by Taking Advantage of Computer Vision Techniques
- Posted by Daitan Innovation Team
- On June 18, 2020
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
- Posted by Daitan Innovation Team
- On April 15, 2020
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
- Posted by Daitan Innovation Team
- On February 12, 2020
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
- Posted by Daitan Innovation Team
- On December 20, 2019
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
- Posted by Daitan Innovation Team
- On December 1, 2019
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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Storing and Retrieving Machine Learning Models at Scale With Distributed Object Storage
- Posted by Daitan Innovation Team
- On September 6, 2019
The need to quickly create, store, and fetch machine learning models at scale is rapidly increasing. Examples of applications include recommender systems that are based on individual customers’ purchasing habits or detection of fraud attempts based on each customer’s past behavior, among many others.
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Leveraging Deep Learning on the Browser for Face Recognition
- Posted by Daitan Innovation Team
- On August 20, 2019
Face recognition is probably one of the long-awaited technologies of recent decades. From Hollywood movies and TV sci-fi series to actual cell phone solutions, the face seems to be the perfect authenticator. But, despite the hype, the tech didn’t look ready for a long time. However, recent advances in machine learning seem to be worth the wait. To get an idea, let’s take a look at what the big four tech companies are doing in this area.
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Exponential Smoothing Methods for Time Series Forecasting
- Posted by Daitan Innovation Team
- On August 7, 2019
In our last two articles, we covered basic concepts of time series data and decomposition analysis. Following that, it’s now time to apply that knowledge to a practical algorithm. In this piece, we provide an overview of Exponential Smoothing Methods.
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A Visual Guide to Time Series Decomposition Analysis
- Posted by Daitan Innovation Team
- On August 2, 2019
Last time, we talked about the main patterns found in time series data. We saw that, trend, season, and cycle are the most common variations in data recorded through time. However, each of these patterns might affect the time series in different ways. In fact, when choosing a forecasting model, after identifying patterns like trend and season, we need to understand how each one behaves in the series. With this goal in mind, let’s explore two different pre-processing techniques — additive and multiplicative decomposition.
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Fundamentals of Time Series Data and Forecasting
- Posted by Daitan Innovation Team
- On July 25, 2019
Time series forecasting is the use of statistical methods to predict future behavior based on historical data. This is similar to other statistical learning approaches, such as supervised or unsupervised learning. However, time series forecasting has many nuances that make it different from regular machine learning. From data processing all the way to model validation, time series forecasting is a different beast.Many companies are exploring time series forecasting as a way of making better business decisions.
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