January 8, 2021 Python

Comparing Datetimes in Python – With and Without Timezones

Introduction When working with dates, oftentimes, you’d like to know if a given date comes before or after another date. We can get these answers by comparing dates. In this article, we will learn how to use the Python datetime module to create and compare both naive (without timezone info) and aware (with timezone info) dates. To compare the dates, we will use the comparison operators in Python: , ==, =, !=. Note: The datetime module has two methods for […]

January 7, 2021 Advanced, Data Exploration, NLP, Python, Resource, Text, Unstructured Data, Unsupervised, Use Cases Leave a comment

Analysis of Brazilian E-commerce Text Review Dataset Using NLP and Google Translate

This article was published as a part of the Data Science Blogathon. Introduction Comprehending the reviews of customers is very crucial for a business to be successful. Analyzing the reviews helps to properly discern the customer different preferences, likes, dislikes, etc. These extracted insights can then be used to improve customer service and experience. In this article, we would be working on a Brazilian E-commerce reviews dataset where we would perform some exploratory data analysis (EDA) on reviews text, derive […]

January 7, 2021 Advanced, Deep Learning, NLP Leave a comment

Introduction to Automatic Speech Recognition and Natural Language Processing

This article was published as a part of the Data Science Blogathon. Introduction In this article, we will take a closer look at how speech recognition really works. Now, when we say speech recognition, we’re really talking about ASR, or automatic speech recognition. With automatic speech recognition, the goal is to simply input any continuous audio speech and output the text equivalent. We want our ASR to be speaker-independent and have high accuracy. Such a system has long been a […]

January 7, 2021 Machine Learning

Matrix Types in Linear Algebra for Machine Learning

Last Updated on January 5, 2021 A lot of linear algebra is concerned with operations on vectors and matrices, and there are many different types of matrices. There are a few types of matrices that you may encounter again and again when getting started in linear algebra, particularity the parts of linear algebra relevant to machine learning. In this tutorial, you will discover a suite of different types of matrices from the field of linear algebra that you may encounter […]

January 7, 2021 Machine Learning

A Gentle Introduction to Machine Learning Modeling Pipelines

Applied machine learning is typically focused on finding a single model that performs well or best on a given dataset. Effective use of the model will require appropriate preparation of the input data and hyperparameter tuning of the model. Collectively, the linear sequence of steps required to prepare the data, tune the model, and transform the predictions is called the modeling pipeline. Modern machine learning libraries like the scikit-learn Python library allow this sequence of steps to be defined and […]

January 6, 2021 Advanced, Deep Learning, NLP Leave a comment

GPT-3 THE NEXT BIG THING! Foundation of Future?

This article was published as a part of the Data Science Blogathon. Introduction Did you ever have a thought or a wish that you just wanted to write two lines of an essay or a journal and the computer just wrote the rest for you? If yes, then GPT-3 is the answer for you. Baffled? So are the people who got their hands on the GPT-3. Every field in AI is making advancements and NLP & Deep learning are such […]

January 6, 2021 Artificial intelligence Leave a comment

Microsoft DeBERTa surpasses human performance on the SuperGLUE benchmark

Natural language understanding (NLU) is one of the longest running goals in AI, and SuperGLUE is currently among the most challenging benchmarks for evaluating NLU models. The benchmark consists of a wide range of NLU tasks, including question answering, natural language inference, co-reference resolution, word sense disambiguation, and others. Take the causal reasoning task (COPA in Figure 1) as an example. Given the premise “the child became immune to the disease” and the question “what’s the cause for this?,” the […]

January 5, 2021 Python

Python: Safely Create Nested Directory

Introduction File manipulation is one of the most important skills to master in any programming language, and doing it correctly is of utmost importance. Making a mistake could cause an issue in your program, other programs running on the same system, and even the system itself. Possible errors can occur due to the parent directory not existing, or by other programs changing files in the file system at the same time, creating something that is called a race condition. A […]

January 5, 2021 Python

How to Merge DataFrames in Pandas – merge(), join(), append(), concat() and update()

Introduction Pandas provides a huge range of methods and functions to manipulate data, including merging DataFrames. Merging DataFrames allows you to both create a new DataFrame without modifying the original data source or alter the original data source. If you are familiar with the SQL or a similar type of tabular data, you probably are familiar with the term join, which means combining DataFrames to form a new DataFrame. If you are a beginner it can be hard to fully […]

January 5, 2021 NLP

Best Practices for Data-Efficient Modeling in NLG: How to Train Production-Ready Neural Models with Less Data

December 8, 2020 By: Ankit Arun, Soumya Batra, Vikas Bhardwaj, Ashwini Challa, Pinar Donmez, Peyman Heidari, Hakan Inan, Shashank Jain, Anuj Kumar, Shawn Mei, Karthik Mohan, Michael White Abstract Natural language generation (NLG) is a critical component in conversational systems, owing to its role of formulating a correct and natural text response. Traditionally, NLG components have been deployed using template-based solutions. Although neural network solutions recently developed in the research community have been shown to provide several benefits, deployment of […]

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