Let's learn about Natural Language Processing via these 128 free stories. They are ordered by most time reading created on HackerNoon. Visit the /Learn Repo to find the most read stories about any technology.
1. Startup Interview with Changsu Lee, Allganize's Founder and CEO
This interview with Changsu Lee, founder and CEO of Allganize, Inc. goes into details the main reasons why he started his AI NLU company back in 2017.
2. How to Use ChatGPT for Effective Sales Messaging
ChatGPT is an ideal tool for crafting sales messages that resonate with potential customers.
3. ChatGPT is a Plague Upon Online Publications
Ethics are a crucial part of Artificial Intelligence, which is why tech like ChatGPT must go through gruelling tests of bias.
4. Meet Lettria: Our Place in the AI Revolution Begins with NLP
While natural language processing has received tons of attention in the field of AI, generative AI is also making great strides.
5. How I Got to Top 24% on a Kaggle Text Classification Challenge Without Writing a Single Line of Code
In this post, we will see how to use the platform and get a submission that achieves a respectable 83% Accuracy on the test set.
6. Softmax Temperature and Prediction Diversity
This article is about tweaking the softmax distribution to control how diverse and novel the predictions are.
7. A Subreddit Where Only AI Chatbots Can Post
There’s a subreddit with a called r/SubSimulator that took three years in the making and which is fully powered by bots
8. What Is Conversational AI: Principles and Examples
In this article, we will take the time to explain what conversational AI is: principles and examples to have a better idea of how you can implement it.
9. How to Perform Emotion detection in Text via Python
In this tutorial, I will guide you on how to detect emotions associated with textual data and how can you apply it in real-world applications.
10. Checking If Your Headline A Clickbait: A How-To Guide
For those who don’t know what that is… It is basically a magical tool that allows anyone to take existing AI models and train them for their own data, however, small the dataset maybe. Sounds good, right?
11. What is GPT-3 and Why Do We Need it?
GPT has become a hot topic over the last few years, and with good reason. It provides a general-purpose “text in, text out” interface
12. Spoken Language Understanding (SLU) vs. Natural Language Understanding (NLU)
Differences between SLU (Spoken Language Understanding) and NLU (Natural Language Understanding). Top FOSS and paid engines and their approach to SLU.
13. Behind the Scenes of an OCR Receipt and Invoice API Engine
Find out how an accurate, adaptive and multi-lingual receipt OCR API engine works!
14. ChatGPD Doesn't Exist: It's ChatGPT
ChatGPD is one of the most common misspellings of the viral language model developed by Open AI. The correct term is ChatGPT.
15. An Essential Python Text-to-Speech Tutorial Using the pyttsx3 Library
Basically, what we want to do is to give some piece of text to our program and it will convert that text into the speech and will read that to us.
16. The Impact of AI Transformers on the Customer Experience
I have spent the last few weeks understanding the impact of a great revolution in the world of Artificial Intelligence and NLP on the customer experience. Not from a purely technical point of view, but trying to estimate the competitive advantage that this new approach can generate. We are facing yet another disruptive innovation, and it can bring significant advantages, let's try to find out which ones.
17. Conferencing and The Art of 'Paper Blitzing'
There are soooo many papers in the field of machine learning, natural language processing nowadays. I’ll share the paper blitz method to "read them all".
18. 14 Open Datasets for Text Classification in Machine Learning
Text classification datasets are used to categorize natural language texts according to content. For example, think classifying news articles by topic, or classifying book reviews based on a positive or negative response. Text classification is also helpful for language detection, organizing customer feedback, and fraud detection. Though time consuming when done manually, this process can be automated with machine learning models. The result saves companies time while also providing valuable data insights.
19. Affective computing
Affective computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects.
20. Getting Started with OpenAI API in JavaScript
Learn beginner-friendly AI development using OpenAI API and JavaScript. Includes installation guide and code examples for building AI-enabled apps.
21. How a natural gas company is using machine learning in natural gas exploration
The modern business world is becoming increasingly technology-driven and machine learning (MD) is currently at the forefront. While one might not inherently in
22. AI's Role in Language Learning: Stuart Barrass, Kaizen Languages CEO
We spoke with Stuart Barrass, CEO and Co-founder of Kaizen Languages, a startup that helps people with language aquisition through AI-Driven conversations.
23. 6 Chatbot Mistakes that Scare Your Customers Away
Six unforgivable mistakes that scare off your customers and prospects? The Smart Tribune team answers you.
24. Is it Ethical for Media Outlets to Use AI to Write Stories?
If media outlets are hiding their usage of AI-generated content, is it because this is ethically wrong?
25. Power Virtual Agents: Use GPT-3.5 to Help With Trigger Phrases and Custom Entities
Use OpenAI Chat-GPT to help generate trigger phrases and content entities for power virtual agents.
26. How AI Has Changed Natural Language Processing
How natural language processing has been revolutionized by Artificial Intelligence and how this is currently affecting businesses.
27. Language Modeling - A Look at the Most Common Pre-Training Tasks
This article is about putting all the popular pre-training tasks used in various language modelling tasks at a glance.
28. The Metaverse's Content Problem...
How do we make 3D content easy to make? What will user-generated-content look like in the metaverse?
29. Neural Entity Linking and How JPMorgan Chase Plans to Use it
This article summarizes the problem statement, solution, and other key technical components of the paper: End-to-End Neural Entity Linking in JP Morgan Chase
30. Nir Eyal Discusses Becoming 'Indistractable,' Time Management, Focus and ChatGPT
The emergence of ChatGPT has stirred major buzz around the world and massive disruptions across multiple industries.
31. How to Build a Python Interpreter Inside ChatGPT
You don't need an interpreter anymore!
32. Answering Whither Artificial Intelligence By Building A Bot
During one of our call with Yardy, discussing our next venture, we thought about implementing AI to streamline certain functions. Given that I had some experience with Machine Learning, our fund had a project aiming to evaluate ICOs & Coins on specific criteria.
33. What Patients Are Asking Our COVID-19 Virtual Assistant
According to a recent Pew Research Center poll, in just one week (March 16–24), the number of Americans who view the coronavirus as a major threat to public health spiked by nearly 20%, from 47% to 66% — a figure that is growing exponentially.
34. What Kind of Scientist Are You?
Data science came a long way from the early days of Knowledge Discovery in Databases (KDD) and Very Large Data Bases (VLDB) conferences.
35. How to Get Started With Embeddings
Getting started with embeddings using open-source tools.
36. 10 Best Reddit Datasets for NLP and Other ML Projects
In this post, I wanted to share a Reddit dataset list that gained a lot of traction on social media when it was first posted.
37. What Happens to Mobile Apps When AI and Machine Learning Join Forces?
A model can take into consideration a lot more parameters than the human brain.
38. Natural Language Processing and How it Could Improve Employee Engagement
Internal communication and employee engagement are key when it comes to the smooth functioning of an organization and building a reputation, especially in today’s age when more and more people are opting to work remotely and teams are scattered across the world.
39. Open AI's ChatGPT Pricing Explained: How Much Does It Cost to Use GPT Models?
How much does it cost to use GPT-3 in a commercial project? We ran an experiment and a project simulation based on the results.
40. NO! GPT-3 Will Not Steal Your Programming Job
TL;DR; GPT-3 will not take your programming job (Unless you are a terrible programmer, in which case you would have lost your job anyway)
41. About Cisco, Alascom and Fanuc's Progress on Building Collaborative Cobots
By harnessing Natural language to allow for more seamlessly collaborative Robotics projects, Cisco, Alascom and Fanuc are drawing a roadmap for the future.
42. CLIP: An Innovative Aqueduct Between Computer Vision and NLP
A rudimentary article describing the concept behind the "CLIP" algorithm in deep learning, its approach, implementation, scope & limitations.
43. Stable Diffusion, Unstable Me: Text-to-image Generation
Text to image generation is not a new idea. What if, you feed <your name> to a state-of-the-art image generation model?
44. Subtitles for Living: AR's Role in Language Translation
AR shines when our relationship with technology becomes more intuitive and in 2022, emerging AR capabilities are taking language translation a step further.
45. What to do When Reviewing Academic Papers
Academic paper reviews is a necessary civic duty for researchers in all fields, humanities, science, engineering or anything in between.
46. Cocktail Alchemy: Creating New Recipes With Transformers
Build a transformer model with natural language processing to create new cocktail recipes from a cocktail database.
47. An Intro to Transfer Learning & Retraining
In simple terms, transfer learning is a machine learning approach where a model that is already trained on a specific data set and developed for a specific task
48. "AI Can’t 'Think” Like Us Independently," - says Machine Learning Engineer Mani Sarkar
In our new blog series, we’re interviewing data scientists and machine learning engineers about their career paths, areas of interest and thoughts on the future of AI. We kick off this week with a 20-year veteran and jack-of-all-trades when it comes to machine learning and data science: Mani Sarkar. Mani is a strategic machine learning engineer based in London, UK, who believes in getting beyond the theoretical and applying AI to real-world problems.
49. This Entire Article Was Written by ChatGPT's Grandfather
As a historical reference, here is what ChatGPT’s grandfather, GPT2 was able to produce all the way back in 2020. It’ll be interesting to compare it to what Cha
50. Naive Sentiment Analysis Using R
Cleuton Sampaio, October 2019
51. My Experiments With AI Poetry And Some Random Thoughts
I have become a ‘covidiot’ nowadays. I’m stuck in the home since last one and half months since COVID-19 outbreak. There is hardly any physical activity and I’m spending the longest era of my life without underwear since my adulthood.
52. My Journey Into Predicting States Using Emoji Observations With Viterbi Algorithm
See the implementation of the Viterbi algorithm in Python
53. How Do Chatbots Work in Call Centers?
Machine learning technologies help to significantly reduce the cost of providing services, as well as increase the efficiency of call centers.
54. We Released Modern Google-level Speech-to-Text Models
Our models are on par with premium Google models and also really simple to use.
55. No. You Still Cannot Have A Real Conversation With a Chatbot.
Chatbots do not really understand what you are saying and you cannot have a real conversation with a personal assistant like you can with another person.
56. What Is Open AI Foundry and How Does It Change Generative AI?
OpenAI Foundry may just be a rumor, but it took the tech news space by storm. Learn what we can expect, when, and who will benefit from Foundry first.
57. Mistakes of Microsoft's New Bing: Can ChatGPT-like Generative Models Guarantee Factual Accuracy?
We uncover several factual mistakes in Microsoft’s new Bing and Google’s Bard demonstrations, suggesting limitations in conversational AI models like ChatGPT.
58. B2B Sales Is Broken. New Tech Can Help
Closing b2b deals is difficult. People are not buying aggressive selling techniques. Existing sales softwares aren't helping. New tech can help.
59. Augmented Analytics & Data Storytelling: Covid Ups FP&A Demand
Businesses need agile tools to quickly identify and communicate actionable insights for more informed decision-making.
60. How to Convert Speech to Text in Python
Speech Recognition is the ability of a machine or program to identify words and phrases in spoken language and convert them to textual information.
61. Introductory Guide to Automatic Language Translation in Python
Today, I'm going to share with you guys how to automatically perform language translation in Python programming.
62. ChatGPT Is Making the Internet More Fun and Less Confusing
This is a short story about the rise of ChatGPT :) I hope you like it.
63. Shakespeare Meets Google's Flax
Some are born great, some achieve greatness, and some have greatness thrust upon them.
William Shakespeare, Twelfth Night, or What You Will
64. ChatGPT Offers 5 Multi-Million Dollar Business Ideas Built With ChatGPT
I wanted to ask ChatGPT about ideas worth millions of dollars. Here are the answers:
65. ChatGPT Writes The Great Gatsby Set in a Zombie Apocalypse
I told OpenAI's ChatGPT model to write The Great Gatsby, but with zombies. Here's what happened...
66. 5 Case Studies that Prove Bots Are Here to Help Businesses Scale
It was about three years ago that Microsoft CEO, Satya Nadella, was quoted stating “Bots are the new apps,” during a 3-hour keynote to kick off the company’s Build conference. That statement has probably never been truer, especially since NLP bots Enterprise bots have appeared on the scene.
67. Importance of Sentiment Analysis as a Key Marketing Tool
Sentiment Analytics can help your marketing team to understand the sentiment of your target audience and identify any potential issues or concerns.
68. How I Built a Demo App to Listen to 5000+ Hours of Joe Rogan With the Help of AI
I’m consuming 5500+ hours of Joe Rogan with the help of AI
69. Natural Language Processing with Python: A Detailed Overview
A detailed overview of an AI subfield called Natural Language Processing or NLP and how to learn NLP.
70. How to detect plagiarism in text using Python
Intro
71. How to Build a Plagiarism Checker Using Machine Learning
Using machine learning, we can build our own plagiarism checker that searches a vast database for stolen content. In this article, we’ll do exactly that.
72. How LinkedIn Uses NLP to Design their Help Search System
This is the summary and my key takeaways from the original post by LinkedIn on how NLP is being used (as of 2019) in designing its Help Search System.
73. Using BERT Transformer with SpaCy3 to Train a Relation Extraction Model
A step-by-step guide on how to train a relation extraction classifier using Transformer and spaCy3.
74. How to Remove Gender Bias in Machine Learning Models: NLP and Word Embeddings
Most word embeddings used are glaringly sexist, let us look at some ways to de-bias such embeddings.
75. Top Tips For Competing in a Kaggle Competition
Hi, my name is Prashant Kikani and in this blog post, I share some tricks and tips to compete in Kaggle competitions and some code snippets which help in achieving results in limited resources. Here is my Kaggle profile.
76. A Deep Learning Overview: NLP vs CNN
Artificial Intelligence is a lot more than a tech buzzword these days. This technology has disrupted almost every industry within a decade. Every company wants to implement this cutting edge technology in its system to cut costs, save time, and make the overall process more efficient with automation.
77. Getting Started with the Weaviate Vector Search Engine
Everybody who works with data in any way shape or form knows that one of the most important challenges is searching for the correct answers to your questions. There is a whole set of excellent (open source) search engines available but there is one thing that they can’t do, search and related data based on context.
78. Can GPT-3 Finish Writing My Zombie Novel?
My biggest worry (and excitement) is that AI will progress enough to become more creative than humans.
79. I Got Close to Winning an NLP Comp — With No Machine Learning Knowledge
Learn how to leverage software developer tools to beat the best in a Natural Language Processing competition on Kaggle, without using any Machine Learning.
80. Evidence That AI Will Soon Pass the Turing Test (or maybe it already has)
You might be wondering if machines are a threat to the world we live in, or if they’re just another tool in our quest to improve ourselves. If you think that AI is just another tool, you might be surprised to hear that some of the biggest names in technology have a clear concern for it. As Mark Ralston wrote, “The great fear of machine intelligence is that it may take over our jobs, our economies, and our governments”.
81. Training Your Own Text Classification Model From Scratch With Tensorflow Is As Easy As ABC
Hello ML Newb! In this article, you will learn to train your own text classification model from scratch using Tensorflow in just a few lines of code.
82. Is AI Affecting Your Business? Here's How To Make it Work For You Not Against You
What comes to your mind when you hear the phrase artificial intelligence (A.I)? Is it voice-controlled assistants such as Amazon Alexa or Google Home? Or, a self-deploying robotic vacuum that can determine how much vacuuming your room needs without human assistance?
83. How Far Are We From a Real World Jarvis?
A Brief History of NLP Applications in the 21st Century
84. How To Build and Deploy an NLP Model with FastAPI: Part 1
Learn how to build an NLP model and deploy it with a fast web framework for building APIs called FastAPI.
85. Text Classification in iOS using tensorflowlite [A How-To Guide]
Text classification is task of categorising text according to its content. It is the fundamental problem in the field of Natural Language Processing(NLP). More general applications of text classifications are in email spam detection, sentiment analysis and topic labelling etc.
86. Natural Language Inference and NLP
How it can give us something we hitherforto though cobblers: a computer-you-can-ask-anything!
87. NLP Datasets from HuggingFace: How to Access and Train Them
The Datasets library from hugging Face provides a very efficient way to load and process NLP datasets from raw files or in-memory data. These NLP datasets have been shared by different research and practitioner communities across the world.
88. Natural Language Processing: Explaining BERT to Business People
<TLDR> BERT is certainly a significant step forward in the context of NLP. Business activities such as topic detection and sentiment analysis will be much easier to create and execute, and the results much more accurate. But how did you get to BERT, and how exactly does the model work? Why is it so powerful? Last but not least, what benefits it can bring to the business, and our decision to integrate it into the sandsiv+ Customer Experience platform.</TLDR>
89. Innovation Opportunities in Data, AI, AR, Robots, Biotech, More [Overview]
Digital Technology is everywhere and it is redefining how we live, communicate, and work. Most importantly, it accelerates how we innovate.
90. The Art of Transformers: How AI Intuitively Summarizes Business Papers Using NLP
“I don’t want a full paper, just give me a concise summary of it”. Who hasn't found themselves in this situation, at least once? Sound familiar?
91. Analyzing Sentiment Of Tweets Is Really Easy If You Follow This Tutorial
Hello, Guys,
92. Building a Job Entity Recognizer Using Amazon Comprehend - A How-To Guide
With the advent of Natural Language Processing (NLP), traditional job searches based on static keywords are becoming less desirable because of their inaccuracy and will eventually become obsolete. While the traditional search engine performs simple keyword searches, the NLP based search engine extract named entities, key phrases, sentiment, etc. to enrich the documents with metadata and perform search query based on the extracted metadata. In this tutorial, we will build a model to extract entities, such as skills, diploma and diploma major, from job descriptions using Named Entity Recognition (NER).
93. Data and Analytics Predictions for 2020 [A Top 5 List]
It would be no exaggeration to say that the capacity of technology to advance itself is proceeding at a faster rate than our ability to process these changes all at the same time. This is both amazing and alarming in the same breath.
94. Artificial Intelligence is the Future, and It's Already Here
By 2030, artificial intelligence is projected to contribute at least $15.7 trillion to the global economy.
95. Native Analytics On Elasticsearch With Knowi
Table of Contents
96. AI Dungeon: An AI-Generated Adventure Game by Nick Walton
The original AI Dungeon was made just over a year ago, the result of a curious gamer, a hackathon, and the GPT-2 text transformer. Fast forward to the present day, and AI Dungeon has expanded into a unique example of creative AI technology. The game now boasts 1.5 million players, multiple genres for stories, and even multiplayer adventures.
97. 8 of the Best AI Chatbots for 2023
Thanks to artificial intelligence and machine learning, chatbots are becoming a practical tool in the business world. This is good news for many companies, as chatbots can increase engagement, revenue and ROI. The potential of artificial intelligence is there to be harnessed, and AI-powered chatbots are examples of the effective usage of the technology. However, choosing a chatbot can be overwhelming. Let's take a look at the most popular AI chatbots currently on the market.
98. How AI is Making it Easier to Spread Fake News
Is Bitcoin the revolution against unequal economic systems, or a scam and money laundry mechanism? Will artificial intelligence (AI) improve and boost humankind, or terminate our species? These questions present incompatible scenarios, but you will find supporters for all of them. They cannot be all right, so who’s wrong then?
99. Top 6 Applications of Natural Language Processing in Healthcare
For many healthcare providers, the industry is shaping up to be more of a shifting quandary of regulatory issues, financial turmoil, and unforeseeable eruptions of resentment from practitioners on the edge of revolt. The industry is now taking the opportunity to scale up their big data defenses and develop the technological infrastructure required to meet the imminent challenges.
100. The Basics Of Natural Language Processing in 10 Minutes
Do you also want to learn NLP as Quick as Possible ? Perhaps you are here because you also want to learn natural language processing as quickly as possible, like me.
101. Introducing aasaan.ai: No-Code Yelp Sentiment Classification
Introduction
102. How To Build An n8n Workflow To Manage Different Databases and Scheduling Workflows
Learn how to build an n8n workflow that processes text, stores data in two databases, and sends messages to Slack.
103. Everything You Need to Know About Google BERT
Google BERT will help you to kickstart your NLP journey by showing you how the transformer’s encoder and decoder work.
104. How to Talk to ChatGPT: An Intro to Prompt Engineering
Prompting is pretty much the only skill you now require to be a master of these new large and powerful generative models such as ChatGPT.
105. How to Fine Tune a 🤗 (Hugging Face) Transformer Model
How to fine-tune a Hugging Face Transformer model for Sequence Classification
106. The Ten Must Read NLP/NLU Papers from the ICLR 2020 Conference
The International Conference on Learning Representations (ICLR) took place last week, and I had a pleasure to participate in it. ICLR is an event dedicated to research on all aspects of representation learning, commonly known as deep learning. This year the event was a bit different as it went virtual. However, the online format didn’t change the great atmosphere of the event. It was engaging and interactive and attracted 5600 attendees (twice as many as last year). If you’re interested in what organizers think about the unusual online arrangement of the conference, you can read about it here.
107. How To Compare Documents Similarity using Python and NLP Techniques
In this post we are going to build a web application which will compare the similarity between two documents. We will learn the very basics of natural language processing (NLP) which is a branch of artificial intelligence that deals with the interaction between computers and humans using the natural language.
108. Your Guide to Natural Language Processing (NLP)
Everything we express (either verbally or in written) carries huge amounts of information. The topic we choose, our tone, our selection of words, everything adds some type of information that can be interpreted and value extracted from it. In theory, we can understand and even predict human behaviour using that information.
109. Content-Based Recommender Using Natural Language Processing (NLP)
A guide to build a movie recommender model based on content-based NLP: When we provide ratings for products and services on the internet, all the preferences we express and data we share (explicitly or not), are used to generate recommendations by recommender systems. The most common examples are that of Amazon, Google and Netflix.
110. What is OpenAI's Whisper Model?
Have you ever dreamed of a good transcription tool that would accurately understand what you say and write it down? Not like the automatic YouTube translation tools… I mean, they are good but far from perfect. Just try it out and turn the feature on for the video, and you’ll see what I’m talking about.
111. How to Build a Twitter Sentiment Analysis System
Understanding the sentiment of tweets is important for a variety of reasons: business marketing, politics, public behavior analysis, and information gathering are just a few examples. Sentiment analysis of twitter data can help marketers understand the customer response to product launches and marketing campaigns, and it can also help political parties understand the public response to policy changes or announcements.
112. A Beginner Guide to Incorporating Tabular Data via HuggingFace Transformers
Transformer-based models are a game-changer when it comes to using unstructured text data. As of September 2020, the top-performing models in the General Language Understanding Evaluation (GLUE) benchmark are all BERT transformer-based models. At Georgian, we often encounter scenarios where we have supporting tabular feature information and unstructured text data. We found that by using the tabular data in these models, we could further improve performance, so we set out to build a toolkit that makes it easier for others to do the same.
113. Getting Started with Natural Language Processing: US Airline Sentiment Analysis
By: Comet.ml and Niko Laskaris, customer facing data scientist, Comet.ml
114. How to Perform Data Augmentation in NLP Projects
In machine learning, it is crucial to have a large amount of data in order to achieve strong model performance. Using a method known as data augmentation, you can create more data for your machine learning project. Data augmentation is a collection of techniques that manage the process of automatically generating high-quality data on top of existing data.
115. 15 Must-read Machine Learning Articles for Data Scientists
As always, the fields of deep learning and natural language processing are as busy as ever. Despite many industries being hindered by the quarantine restrictions in many countries, the machine learning industry continues to move forward.
116. Positional Embedding: The Secret behind the Accuracy of Transformer Neural Networks
An article explaining the intuition behind the “positional embedding” in transformer models from the renowned research paper - “Attention Is All You Need”.
117. NLP Tutorial: Creating Question Answering System using BERT + SQuAD on Colab TPU
Open sourced by Google Research team, pre-trained models of BERT achieved wide popularity amongst NLP enthusiasts for all the right reasons! It is one of the best Natural Language Processing pre-trained models with superior NLP capabilities. It can be used for language classification, question & answering, next word prediction, tokenization, etc.
118. Meta's New Model OPT is an Open-Source GPT-3
We’ve all heard about GPT-3 and have somewhat of a clear idea of its capabilities. You’ve most certainly seen some applications born strictly due to this model, some of which I covered in a previous video about the model. GPT-3 is a model developed by OpenAI that you can access through a paid API but have no access to the model itself.
119. ChatGPT Explained in 5 Minutes
ChatGPT has taken over Twitter and pretty much the whole internet, thanks to its power and the meme potential it provides.
120. How to Build a Multi-label NLP Classifier from Scratch
Attacking Toxic Comments Kaggle Competition Using Fast.ai
121. Sentiment Analysis with Python and AssemblyAI’s Speech Recognition API
If you’ve never heard of Sentiment Analysis, I hadn’t either before I stumbled on it in the documentation. That’s why I thought it would be interesting to try.
122. Building a Metaverse For Everyone
123. How To Build and Deploy an NLP Model with FastAPI: Part 2
Learn how to build an NLP model and deploy it with a fast web framework for building APIs called FastAPI.
124. Text Embedding Explained: How AI Understands Words
Large language models are a specific type of machine learning-based algorithm that understand and can generate language
125. Text Classification Models: All Tips And Tricks From 5 Kaggle Competitions
In this article (originally posted by Shahul ES on the Neptune blog), I will discuss some great tips and tricks to improve the performance of your text classification model. These tricks are obtained from solutions of some of Kaggle’s top NLP competitions.
126. How to Play Chess Using a GPT-2 Model
OpenAI’s transformer-based language model GPT-2 definitely lives up to the hype. Following the natural evolution of Artificial Intelligence (AI), this generative language model drew a lot of attention by engaging in interviews and appearing in the online text adventure game AI Dungeon.
127. The Hitchhikers's Guide to PyTorch for Data Scientists
PyTorch has sort of became one of the de facto standard for creating Neural Networks now, and I love its interface. Yet, it is somehow a little difficult for beginners to get a hold of.
128. A Complete(ish) Guide to Python Tools You Can Use To Analyse Text Data
Exploratory data analysis is one of the most important parts of any machine learning workflow and Natural Language Processing is no different.
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