Posted on Jul 16, 2021
Currently, Attention-based recurrent Encoder-Decoder models provide an elegant way of building end-to-end models for different tasks, like automatic speech recognition (ASR), machine translation (MT), etc. An end-to-end ASR model folds traditional acoustic model, pronunciation model, and language model (LM) into a single network. An encoder maps the input speech to a sequence of higher-level...
Throughout the evolution, several metrics have been introduced to evaluate the performance of a Machine Learning algorithm or model. Sometimes it can be tricky to choose the correct metric for evaluating our model. In this article I have tried to discuss some basic matrics used in ML-related tasks and when to use which metric....
Nowadays for a lot of tasks, we are using deep neural networks or deep learning. While working with deep learning very often we face situations like overfitting and overconfidence. Overfitting is relatively well studied and can be tackled with several strategies like dropout, weight regularization, early stopping, etc. We have tools for tackling overconfidence...
In machine learning, overfitting is a very commonly used term. But what is overconfidence? Let’s talk about this today with some solid background and examples. Well, if we think about overconfidence in our general life – what pops in our mind about it? It’s being excessively confident about something. Like we are driving a...
Posted on Jul 02, 2021
In this post, we are going to discuss KFold and StratifiedKFold with some real-time examples. While working with a supervised machine learning model, we have some data with features and labels or targets. During the training, we give some portion of the data and keep a portion for test the model. Thus, we need...
