If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
From their early days at MIT, and even before, Emma Liu '22, MNG '22, Yo-whan "John" Kim '22, MNG '22, and Clemente Ocejo '21, MNG '22 knew they wanted to perform computational research and explore artificial intelligence and machine learning. "Since high school, I've been into deep learning and was involved in projects," says Kim, who participated in a Research Science Institute (RSI) summer program at MIT and Harvard University and went on to work on action recognition in videos using Microsoft's Kinect. As students in the Department of Electrical Engineering and Computer Science who recently graduated from the Master of Engineering (MEng) Thesis Program, Liu, Kim, and Ocejo have developed the skills to help guide application-focused projects. Working with the MIT-IBM Watson AI Lab, they have improved text classification with limited labeled data and designed machine-learning models for better long-term forecasting for product purchases. For Kim, "it was a very smooth transition and … a great opportunity for me to continue working in the field of deep learning and computer vision in the MIT-IBM Watson AI Lab." Collaborating with researchers from academia and industry, Kim designed, trained, and tested a deep learning model for recognizing actions across domains -- in this case, video.
The function for the negation handler is available at my Github repo. An example of the function output is shown below. 'Negation' is the main function being called on the tokenized sentence as shown. In the function, whenever a negation word (like'not', "n't", 'non-', 'un-', etc) is encountered, a set of cognitive synonyms called synsets are generated for the word next to the negation. These synsets are interlinked by conceptual semantic and lexical relations to each other in a lexical database called WordNet.
I personally do believe all the fancy ML research and advanced AI algorithm works have very minimal value if not zero until the date when they can be applied to real-life projects without asking the users for an insane amount of resources and excessive domain knowledge. And Hugging Face builds the bridge. Hugging Face is the home for thousands of pre-trained models which have made great contributions to democratizing artificial intelligence through open source and open science. Today, I want to give you an end-to-end code demo to compare two of the most popular pre-trained models by conducting a multi-label text classification analysis. The first model is SentenceTransformers (SBERT).
This notebook classifies movie reviews as positive or negative using the text of the review. This is an example of binary--or two-class--classification, an important and widely applicable kind of machine learning problem. We'll use the IMDB dataset that contains the text of 50,000 movie reviews from the Internet Movie Database. These are split into 25,000 reviews for training and 25,000 reviews for testing. The training and testing sets are balanced, meaning they contain an equal number of positive and negative reviews.
On the Internet, there are a lot of sources that provide enormous amounts of daily news. Further, the demand for information by users has been growing continuously, so it is important to classify the news in a way that lets users access the information they are interested in quickly and efficiently. Using this model, users would be able to identify news topics that go untracked, and/or make recommendations based on their prior interests. Thus, we aim to build models that take news headlines and short descriptions as inputs and produce news categories as outputs. The problem we will tackle is the classification of BBC News articles and their categories.
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Text mining is a popular topic for exploring what text you have in documents etc. Text mining and NLP can help you discover different patterns in the text like uncovering certain words or phases which are commonly used, to identifying certain patterns and linkages between different texts/documents. Combining this work on Text mining you can use Word Clouds, time-series analysis, etc to discover other aspects and patterns in the text. Check out my previous blog posts (post 1, post 2) on performing Text Mining on documents (manifestos from some of the political parties from the last two national government elections in Ireland). These two posts gives you a simple indication of what is possible.
BERT (Bi-Directional Encoder Representation from Transformers) is that type of transformer introduced by Google which consists of only encoder and no decoder. Finally after following a similar approach on test data we perform our test evaluation using Mathew's correlation Coefficient which is highly recommended as a metric for classification type of problems. Voila!!! we finally fine tuned our bert model as per our use-case. Complete implementation can be found here…..
We will cover all the topics related to solving Multi-Class Text Classification problems with sample implementations in Python / TensorFlow / Keras environment. We will use a Kaggle Dataset in which there are 32 topics and more than 400K total reviews. You can access all the codes, videos, and posts of this tutorial series from the links below. In this tutorial series, there are several parts to cover the Text Classification with various Deep Learning Models topics. You can access all the parts from this index page.
I will recommend this class to any one looking towards Data Science" "This course so far is breaking down the content into smart bite-size pieces and the professor explains everything patiently and gives just enough background so that I do not feel lost." "This course is really good for me. it is easy to understand and it covers a wide range of NLP topics from the basics, machine learning to Deep Learning. The codes used is practical and useful. I definitely satisfy with the content and surely recommend to everyone who is interested in Natural Language Processing"