A step-by-step implementation to classify audio signals using continuous wavelet transform (CWT) as features.
Introduction
Audio classification is a very important task. In the past decade, a lot of research has been done on classifying audio using different kinds of features and neural network architectures. Some real-world applications of this are…
Introduction
Recently, I got to work on a research project involving analyzing GeoSpatial data. To analyze such data, visualization is an extremely important step. You can see several patterns easily on a map. If the data contains 100 features, it would be nice if we could plot a map of those…
Introduction
Bioacoustics is very useful in studying the environment. It has been used for a long time to track submarines and whales. Birds help a lot to shape the plant life we see around us. Recognizing bird songs is very important for automatic monitoring of wildlife and studying the behavior of…
Learn how to achieve good accuracy on a classification task with just a few samples per class and imbalanced distribution of classes
Introduction
Nowadays there are several deep learning models like BERT, GANs, and U-Nets that are achieving a state-of-the-art performance of tasks like image recognition, image segmentation, and language modeling. Hardly a day goes by without a new innovation in Machine Learning. Tech Giants like Google, Microsoft, and Amazon are coming up…
Introduction
TensorBoard is a tool that provides measurements and visualizations needed during machine learning workflow.
In this tutorial, I will give a quick tutorial on how to visualize your feature vectors on TensorBoard. Using a machine learning model, sometimes we want to learn embeddings of different classes. Embeddings are a way…
A step-by-step tutorial to explain the working of PCA and implementing it from scratch in python
Introduction
Principal Component Analysis or PCA is a commonly used dimensionality reduction method. It works by computing the principal components and performing a change of basis. It retains the data in the direction of maximum variance. The reduced features are uncorrelated with each other. These features can be used for unsupervised…
Aditya Dutt
Machine Learning PhD Student at University of Florida (he/him) https://adityadutt.github.io/
