NLP Leave a comment

Introducing the Plato Research Dialogue System: A Flexible Conversational AI Platform

Intelligent conversational agents have evolved significantly over the past few decades, from keyword-spotting interactive voice response (IVR) systems to the cross-platform intelligent personal assistants that are becoming an integral part of daily life.  Along with this growth comes the need for intuitive, flexible, and comprehensive research and development platforms that can act as open testbeds to help evaluate new algorithms, quickly prototype, and reliably deploy conversational agents. At Uber AI, we developed the    

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NLP Leave a comment

Controlling Text Generation with Plug and Play Language Models

This article is based on the paper “Plug and Play Language Models: A Simple Approach To Controlled Text Generation” by Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, and Rosanne Liu. The transformer neural network architecture, developed by Vaswani et al. (2017), has enabled larger models and momentous progress in natural language processing (NLP) over the last    

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Computer Vision, Datasets, Deep Learning, Intermediate, Listicle, Machine Learning, NLP, Project Leave a comment

Top 15 Open-Source Datasets of 2020 that every Data Scientist Should add to their Portfolio!

Overview Here is a list of Top 15 Datasets for 2020 that we feel every data scientist should practice on The article contains 5 datasets each for machine learning, computer vision, and NLP By no means is this list exhaustive. Feel free to add other datasets in the comments below   Introduction For the things we have to learn before we can do them, we learn by doing them -Aristotle I am sure everyone can attest to this saying. No […]

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Analysis

When is programming needed in most leading Self Service configurations

To all Data Analysts big and small: Many Corporates typically have Self service BI and DWH solutions ( I am asking only about those who did NOT build an inhouse solution) :  -When is programming needed in most leading Self Service configurations? -When do analysts and Business executives require coding and programming when the Self service application, slice and dice, filtering and fields are not enough?! – IN SOME PLACES, us junior analysts are getting a feeling (that may be […]

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Machine Learning

Dynamic Classifier Selection Ensembles in Python

Dynamic classifier selection is a type of ensemble learning algorithm for classification predictive modeling. The technique involves fitting multiple machine learning models on the training dataset, then selecting the model that is expected to perform best when making a prediction, based on the specific details of the example to be predicted. This can be achieved using a k-nearest neighbor model to locate examples in the training dataset that are closest to the new example to be predicted, evaluating all models […]

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Neural Machine Translation (NMT), NMT Leave a comment

Machine Translation Weekly 62: The EDITOR

Papers about new models for sequence-to-sequence modeling have always been my favorite genre. This week I will talk about a model called EDITOR that was introduced in a pre-print of a paper that will appear in the TACL journal with authors from the University of Maryland. The model is based on the Levenshtein Transformer, a partially non-autoregressive model for sequence-to-sequence learning. Autoregressive models generate the output left-to-right (or right-to-left), conditioning each step on the previously generated token. On the other […]

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python_tutorials
Python

Python: Check if Key Exists in Dictionary

Introduction Dictionary (also known as ‘map’, ‘hash’ or ‘associative array’) is a built-in Python container that stores elements as a key-value pair. Just like other containers have numeric indexing, here we use keys as indexes. Keys can be numeric or string values. However, no mutable sequence or object can be used as a key, like a list. In this article, we’ll take a look at how to check if a key exists in a dictionary in Python. In the examples, […]

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python_tutorials
Python

Calculating Pearson Correlation Coefficient in Python with Numpy

Introduction This article is an introduction to the Pearson Correlation Coefficient, its manual calculation and its computation via Python’s numpy module. The Pearson correlation coefficient measures the linear association between variables. Its value can be interpreted like so: +1 – Complete positive correlation +0.8 – Strong positive correlation +0.6 – Moderate positive correlation 0 – no correlation whatsoever -0.6 – Moderate negative correlation -0.8 – Strong negative correlation -1 – Complete negative correlation We’ll illustrate how the correlation coefficient varies […]

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Artificial intelligence Leave a comment

‘Seeing’ on tiny battery-powered microcontrollers with RNNPool

Computer vision has rapidly evolved over the past decade, allowing for such applications as Seeing AI, a camera app that describes aloud a person’s surroundings, helping those who are blind or have low vision; systems that can detect whether a product, such as a computer chip or article of clothing, has been assembled correctly, improving quality control; and services that can convert information from hard-copy documents into a digital format, making it easier to manage personal and business data. All […]

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Machine Learning

Random Forest for Time Series Forecasting

Random Forest is a popular and effective ensemble machine learning algorithm. It is widely used for classification and regression predictive modeling problems with structured (tabular) data sets, e.g. data as it looks in a spreadsheet or database table. Random Forest can also be used for time series forecasting, although it requires that the time series dataset be transformed into a supervised learning problem first. It also requires the use of a specialized technique for evaluating the model called walk-forward validation, […]

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