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ArgMiner: End-to-End Argument Mining
Author(s): Yousef Nami Originally published on Towards AI. A PyTorch-based package for processing, augmenting, training, and performing inference on SOTA Argument Mining datasets A pictorial representation of the task of Argument Mining Argument Mining (AM) is the task of extracting argument components …
How To Train a Seq2Seq Summarization Model Using “BERT” as Both Encoder and Decoder!! (BERT2BERT)
Author(s): Ala Alam Falaki Originally published on Towards AI. BERT is a well-known and powerful pre-trained “encoder” model. Let’s see how we can use it as a “decoder” to form an encoder-decoder architecture. Photo by Aaron Burden on Unsplash The Transformer architecture …
XGBoost: Its Present-day Powers and Use Cases for Machine Learning
Author(s): Anil Tilbe Originally published on Towards AI. Being that XGBoost achieves implementations with the ability to handle missing values, which are one of the major drawbacks in most of the other algorithms, scalabilities, not just time-efficiencies, are very promising for the …
NLP using DeepLearning Tutorials: A Sentiment Classifier based on perceptron (Part 4/4)
Author(s): Abdelkader Rhouati Originally published on Towards AI. Evaluation of test data This image is uploaded from source Natural Language Processing is one of the most complicated fields of machine learning, basically due to the complexity and ambiguity of the language. However, …
Zero-shot Learning Deep Dive: How to Select One and Present-day Challenges
Author(s): Anil Tilbe Originally published on Towards AI. How to build the learning into a zero-shot classifier with just a few hundred labeled instances per class? First, we have to clarify at a high level the difference between zero-shot learning and deep …
What is the article’s topic means?
Author(s): Akash Dawari Originally published on Towards AI. Quantify the Performance of Classifiers In this article, we will discuss the following question and try to find the answer to them. What is the article’s topic means? What is a confusion matrix? What …
Machine Learning for Time Series Data in Python [Regression]
Author(s): Youssef Hosni Originally published on Towards AI. A practical guide for time series forecasting using machine learning models in Python Time series data is one of the most common data types in the industry and you will probably be working with …
Inside NLLB-200, Meta AI’ New Super Model that Achieved New Milestones in Machine Translations Across 200 Languages
Author(s): Jesus Rodriguez Originally published on Towards AI. One of the most important achievements to bring machine translation to low-resource languages. Source: https://gigazine.net/gsc_news/en/20220707-meta-nllb-200/ I recently started an AI-focused educational newsletter, that already has over 125,000 subscribers. TheSequence is a no-BS (meaning no …
Master Data Wrangling First: Top 20 Python Libraries + Best Practices
Author(s): Anil Tilbe Originally published on Towards AI. Processes, approaches, Top 20 libraries, along with important best practices — how to do it all with Python By Pixabay from Pexels Data Wrangling is transforming data from one format to another. Python is …
The Quality of Projects in your Portfolio Matters a Lot when Applying for Data Scientist or Machine Learning Engineer Positions
Author(s): Suhas Maddali Originally published on Towards AI. The real-world impact that a project makes with the use of machine learning and data science can be a game changer when it comes to interviewing for data science and machine learning related positions. …
Regression Analysis Is Exceedingly Difficult: How to Master It Without Coding
Author(s): Anil Tilbe Originally published on Towards AI. Achieve a strong foundational understanding of the key concepts, 3 battle-tested models, and 3 challenges with regression analysis By Jukan Tateisi from Unsplash Regression analysis is a technique that can be used to [10] …
Unstructured vs. Structured Data: The 5 Most Important Differences
Author(s): Anil Tilbe Originally published on Towards AI. A breakdown of structured data, and unstructured data, the advantages of each, and how to deploy them together simultaneously for your use cases By Dietmar Becker from Unsplash A basic definition of structured data: …
The Future of AI Is Quantum Computing: 10 of the Most Important Use Cases
Author(s): Anil Tilbe Originally published on Towards AI. A comprehensive but abridged explanation of the intersection of AI and quantum computing (with use cases and challenges) By Erik Mclean from Pexels Quantum computing is still in its early stages [13], potentially impacting …
Bayesian Inference: The Best 5 Models and 10 Best Practices for Machine Learning
Author(s): Anil Tilbe Originally published on Towards AI. The advantages, top 5 models, and top 10 best practices for applying Bayesian inference to machine learning problems From Pixabay Bayesian inference is a popular machine learning technique that allows for an algorithm to …
Blockchain and NLP: Top 10 Approaches and Opportunities
Author(s): Anil Tilbe Originally published on Towards AI. When tied together, blockchain and NLP have numerous advantages, applications, and use cases across multiple industries By CocaKolaLips from Pexels Blockchain is a new and innovative technology that has the potential to change the …
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