Data Science and AI Blog.
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In NLP, encoding text is the heart of understanding language. There are many implementations like Glove, Word2vec, fastText which are aware of word embedding. However, these embeddings are only useful for word-level and may not perform well in case we would like to expand to encode for sentences or in general, greater than one word. …
EXPLORING UNIVERSAL SENTENCE ENCODER MODEL (USE) Read More »
In AI Lab, one of our projects is “Weather Forecasting”, in which we process the data from weather radars, and perform prediction using nowcasting techniques, i.e. forecasting in a short period like 60 minutes. To develop a forecasting model and do an evaluation, we need to run many test cases. Each test case corresponds to …
Introduction to Healthcare Data Science (Overview) Healthcare analytics is the collection and analysis of data in the healthcare field to study determinants of disease in human populations, identify and mitigate risk by predicting outcomes. This post introduces some common epidemiological study designs and an overview of the modern healthcare data analytics process. Types of Epidemiologic …
Introduction Tiny Machine Learning (TinyML) [1] is, unsurprisingly, a machine learning technique but this technique is often utilized in building machine learning applications, which require high performance but have limited hardware. a tiny neural network on a microcontroller with really low power requirements (sometimes <1mW). Figure 1: Tiny ML, the next AI revolution [5] TinyML is …
What is AWS Recognition? Amazon Recognition is a service that makes it easy to add powerful, image and video-based, visual analysis to your applications. Recognition Image lets you easily build powerful applications to search, verify, and organize millions of images. Recognition Video lets you extract motion-based context from stored or live stream videos and helps …
Introduction With data that have time-related information, time features can be created to possibly add more information to the models. Since how to consider time series for machine learning is a broad topic, this article only aims to introduced basic ways to create time features for those models. Type of data that is expected for …
Definition In computer science, most algorithms run to completion: they provide a single answer after performing some fixed amount of computation. But nowadays, in machine learning generation, the models take a long time to train and predict the result, the user may wish to terminate the algorithm before completion. That’s where anytime algorithm came in, …
In any data science project, once the variables that correspond to business objectives are defined, typically the project proceeds with the following aims: (1) to understand the data quantitatively, (2) to describe the data by producing a ‘model’ that represents adequately the data at hand, and (3) to use this model to predict the outcome …
Introduction Autoencoders provide a powerful framework for learning compressed representations by encoding all of the information needed to reconstruct a data point in a latent code. In some cases, autoencoders can “interpolate”: By decoding the convex combination of the latent codes for two data points, the autoencoder can produce an output that semantically mixes characteristics …
Implementation for Adversarially Constrained Autoencoder Interpolation (ACAI) Read More »
Pytorch is a deep learning framework and a scientific computing package The scientific computing aspect of PyTorch’s is primarily a result PyTorch’s tensor library and associated tensor operations. That means you can take advantage of Pytorch for many computing tasks, thanks to its supporting tensor operation, without touching deep learning modules. Important to note that …
