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The Dasha Blog
By Industry
11 minute read
Black Box Interpretations
Machine learning (ML) mainly answers such questions as “What?” / “Who?” / “How much?” as well as "What is depicted?" t and so on. The most natural human question that follows is “Why?”. In addition to the black box answer (whether it be boosting or a neural network), we would like to receive the argumentation of this answer. Below is an overview of the interpretation problem.
Alex Dyakonov, Chief Research Scientist
12 minute read
Ensemble in Machine Learning
An ensemble (Multiple Classifier System) is an algorithm that consists of multiple machine learning algorithms, and the process of building an ensemble is called ensemble learning.
Alex Dyakonov, Chief Research Scientist
7 minute read
Random Forest in Machine Learning
Random forest is one of the most amazing machine learning (ML) algorithms invented by Leo Breiman and Adele Cutler back in the last century. It has come down to us in its "original form" (no heuristics have been able to improve it significantly) and is one of the few universal algorithms.
Alex Dyakonov, Chief Research Scientist
15 minute read
Breakthroughs in speech recognition achieved with the use of transformers
Let’s talk about something that cannot but excite your imagination - let’s talk about key breakthroughs that have occurred in speech recognition thanks to transformers.
Dmitry Obukhov, ML Researcher
15 minute read
Confidence Calibration Problem in Machine Learning
Surprisingly, it is extremely difficult to find a good overview of all the methods of model calibration - a process as a result of which the “black boxes” not only qualitatively solve the classification problem, but also correctly assess their confidence in the answer given.
Alex Dyakonov, Chief Research Scientist
18 minute read
Log Loss Function
The log loss function is also commonly called logistic loss or cross-entropy loss (or simply cross entropy), and is often used in classification problems. Let's figure out why it is used and what meaning it has. To fully understand this post, you need a good ML and math background, yet I would still recommend ML beginners to read it (even though the material in this post wasn’t written in a step-by-step format).
Alex Dyakonov, Chief Research Scientist
16-minute read
A Short Guide to Speech Enhancement
Whether you need to drown out extraneous noise in recorded speech, get rid of echoes, or simply separate the voice from the music, this guide can be very helpful to you. It's okay if you've never worked with Speech Enhancement models and Denoising tools - I’m here to describe all the steps and provide you with a couple of valuable tips.
Valery Nikiforov, Junior ML Researcher
8 minute read
What is external TTS and how to work with it
In the process of Dasha's dialogue with a person, depending on the phrase said by the person and the algorithm implemented in DashaScript, Dasha chooses a response phrase. After that, to convey that phrase to the person, the TTS service comes into play.
Dmitry Ionov, Junior Software Engineer
15 minute read
Proper Scoring Rules
Today you will learn about functions that "force" the algorithm to return the probabilities of attributes to classes. This blog post is designed for readers who have prior knowledge of this topic.
Alex Dyakonov, Chief Research Scientist