Let's learn about Nlp via these 248 free blog posts. They are ordered by HackerNoon reader engagement data. Visit the Learn Repo or LearnRepo.com to find the most read blog posts about any technology.
nlp - Natural language processing. A branch of AI that helps computers understand text and words like humans.
1. Decoding Transformers' Superiority over RNNs in NLP Tasks
Explore the intriguing journey from Recurrent Neural Networks (RNNs) to Transformers in the world of Natural Language Processing in our latest piece: 'The Trans
2. 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.
3. 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.
4. How to Use Fuzzy Query Matches in Elasticsearch
Typo is something that often happens and can reduce user’s experience, fortunately, Elasticsearch can handle it easily with Fuzzy Query.
5. 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.
6. Vector Databases: Getting Started With ChromaDB and More
In this article, we will explore another well-known vector store called ChromaDB. Chroma DB is a vector store that is open-source.
7. Mastering Few-Shot Learning with SetFit for Text Classification
This article deals with a technique called "SetFit" that requires minimum data to train a ML model that outperforms the GPT-3 model performance significantly.
8. NLP Tutorial: Creating Question Answering System using BERT + SQuAD on Colab TPU
9. Embeddings 101: Unlocking Semantic Relationships in Text
Text embeddings power AI language understanding. Learn how words become numbers that machines can interpret and why it matters.
10. Fine-Tuning RoBERTa for Topic Classification
Learn how to fine tune a RoBERTa topic classification model in python with the hugging face transformers and libraries.
11. Document-Term Matrix in NLP: Count and TF-IDF Scores Explained
In NLP, Document-Term Matrix (DTM) is a matrix representation of the text corpus. The TF-IDF score is widely used to populate the DTM.
12. I Built a Python Script to Make 10,000 Laws Understandable
Built an AI tool that scrapes, cleans, and summarizes Texas bills to make government legislation readable and transparent for everyone.
13. Build an Abstractive Text Summarizer in 94 Lines of Tensorflow !! (Tutorial 6)
This tutorial is the sixth one from a series of tutorials that would help you build an abstractive text summarizer using tensorflow , today we would build an abstractive text summarizer in tensorflow in an optimized way .
14. Make LLM for Text Summarisation Great Again
In recent months, LLMs have gained popularity and are now widely used in various applications. Data collection is essential for building these models, and crowd
15. 10 Best Hugging Face Datasets for Building NLP Models
Hugging Face offers solutions and tools for developers and researchers. This article looks at the Best Hugging Face Datasets for Building NLP Models.
16. How Search Engines Actually Answer Your Questions
Modern search Q&A explained: how knowledge graphs, DeepQA, and MRC turn messy web pages into direct, trustworthy answers.
17. 7 NLP Project Ideas to Enhance Your NLP Skills
Learn different NLP project ideas that focus on practical implementation to help you master the NLP techniques and be able to solve different challenges.
18. Revolutionizing Product Management: The Confluence of AI, NLP and Agile Methodologies
How AI, NLP, and Agile methodologies are transforming product management, driving data-driven decisions, customer insights, and adaptive innovation.
19. 📚 Summarization With Wine Reviews Using spaCy📋
20. 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.
21. AI vs. Machine Learning: Key Differences Explained
Eliminate your confusion between AI and ML, two different topics that are often confused for one another.
22. How to use NLP to SQL API?
Data is useless without the ability to easily get and act on it. The success of future enterprises will combine sophisticated information collection with better user experience, and the Natural Language User Interface comprises much of this user experience.
23. 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.
24. GPT-3 is Already Making Programmers' Lives Better and There's More to Come
GPT-3 was meant to understand and construct natural language. But as these tools prove, it's pretty good at programming languages, too.
25. ChatSQL: Enabling ChatGPT to Generate SQL Queries from Plain Text
ChatGPT was released in June 2020 that it is developed by OpenAI. It has led to revolutionary developments in many areas. One of these areas is the creation of
26. Text Classification With Zero Shot Learning
Zero-shot text classification using trnasformers and TARSclassifier.
27. Train a NER Transformer Model with Just a Few Lines of Code via spaCy 3
Transformer models have become by far the state of the art in NLP technology, with applications ranging from NER, Text Classification, and Question Answering
28. How to Fine Tune a 🤗 (Hugging Face) Transformer Model
How to fine-tune a Hugging Face Transformer model for Sequence Classification
29. A Brief Intro to the GPT-3 Algorithm
OpenAI GPT-3 is the most powerful language model. It has the capacity to generate paragraphs so naturally that they sound like a real human wrote them.
30. 10 Best Python Machine Learning Tutorials
The Python ecosystem has a large number of libraries and tools that support machine learning, such as NumPy, Pandas, Matplotlib, TensorFlow, and scikit-learn.
31. 6 Best APIs for Topic Detection in 2022
This article examines the best APIs on the market for performing Topic Detection in 2022.
32. 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.
33. How to Build a Multi-label NLP Classifier from Scratch
Attacking Toxic Comments Kaggle Competition Using Fast.ai
34. Scale Vision Transformers (ViT) Beyond Hugging Face
Speed up state-of-the-art ViT models in Hugging Face 🤗 up to 2300% (25x times faster ) with Databricks, Nvidia, and Spark NLP 🚀
35. A Big Step for AI: 3D-LLM Unleashes Language Models into the 3D World
3D-LLM is a novel model that bridges the gap between language and the 3D realm we inhabit.
36. 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.
37. How to Perform Sentiment Analysis with Amazon Comprehend
How to analyze the sentiments from a text using AWS services like Amazon Comprehend, AWS IAM, AWS Lambda, and Amazon S3.
38. How to Scrape NLP Datasets From Youtube
Too lazy to scrape nlp data yourself? In this post, I’ll show you a quick way to scrape NLP datasets using Youtube and Python.
39. wav2vec2 for Automatic Speech Recognition In Plain English
Plain English description of how Meta AI Research's wav2vec2 model works with respect to automatic speech recognition (ASR).
40. How to Integrate Dialogflow Chatbot with AngularJS [Step-By-Step Guide]
There are numerous posts about developing a chatbot using Dialogflow. But creating chatbot isn’t enough. Connecting Dialogflow to the web interface is even more interesting and challenging. With Angular being a popular and emerging platform, here is our guide to integrate Dialogflow chatbot with Angular JS.
41. Analyzing Twitter Conversations with the New Twitter V2 API
Getting actionable insights around a topic using the new Twitter API v2 endpoint
42. How To Create a Python Data Engineering Project with a Pipeline Pattern
In this article, we cover how to use pipeline patterns in python data engineering projects. Create a functional pipeline, install fastcore, and other steps.
43. GPT-4 Explained: ChatGPT's Big Brother is Here to Disrupt Everything
If you thought ChatGPT was good, just wait until you try GPT-4.
44. 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.
45. How to build a message moderation system
<em>By </em><a href="https://medium.com/@irastepanyuk"><em>Ira Stepanyuk</em></a><em>, Data Scientist at </em><a href="https://potehalabs.com/"><em>Poteha Labs</em></a>
46. The Ultimate Guide to Best News APIs
The ultimate guide to the best news APIs, written by a professional data wrangler and ML practitioner.
47. Deploying Twitter Bot to Heroku
Most of us are familiar with Twitter. But we are not much familiar that we can automate the activities like status posting, retweeting, liking, commenting and so on. So,here I'll show you how we can automate some of the activities like getting the twitter data,posting the status and retweeting with Node.js and a npm package called Twit.
48. 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
49. TextStyleBrush Translates Text in Images While Emulating the Font
This new Facebook AI model can translate or edit the text in an image, while maintaining the same font and design as the original.
50. Transformers: Age of Attention
Simple explanation of the Transformer model from the revolutionary paper "Attention is All You Need" which is the basis of many advanced AI systems.
51. 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”.
52. 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.
53. 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.
54. Why Use Kubernetes for Distributed Inferences on Large AI/ML Datasets
This blog provides you with some strong rationale to use Kubernetes on large AI/ML datasets on which distributed inferences are performed. Loop in for more.
55. 'El transformador ilustrado' una traducción al español
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56. AI for Noobs: How Amazon Alexa Works
How Amazon Alexa AI processes and implements commands.
57. Galactica is an AI Model Trained on 120 Billion Parameters
On November 15th, MetaAI and Papers with Code announced the release of Galactica, a game-changer, open-source large language model trained on scientific knowledge with 120 billion parameters.
58. 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.
59. 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
60. [Reviewed] 6 Conversational Feedback Tools
Chatbots for surveys, surveybots, conversational surveys, feedback chatbots, conversational survey tools, AI survey tools, chatbot questionnaires. The list of names goes on and on. But how do they compare and which one is the best performer?
61. 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:
62. 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".
63. How a recommendation system Web App was Built
How to create, build and deploy every component behind a Bike recommendation system
64. Natural Language Inference and NLP
How it can give us something we hitherforto though cobblers: a computer-you-can-ask-anything!
65. An Introduction to 4 Types of Audio Classification
Audio classification is the process of listening to and analyzing audio recordings. Also known as sound classification, this process is at the heart of a variety of modern AI technology including virtual assistants, automatic speech recognition, and text-to-speech applications. You can also find it in predictive maintenance, smart home security systems, and multimedia indexing and retrieval.
66. Scratching the Singularity Surface: The Past, Present and Mysterious Future of LLMs
A brief overview of Natural Language Understanding industry and out current point of LLMs achieving human level reasoning abilities and becoming an AGI
67. How Machine Generated Virtual Assistants can 10x Your Productivity in 2022
AI assistant technology is in many ways similar to a traditional chatbot but integrates next-generation machine learning, AR/VR and data science.
68. Stop the LLM From Rambling: Using Penalties to Control Repetition
A practical guide to penalty settings that reduce repetition and fluff in LLM outputs—without making the text weird.
69. 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.
70. Dingo: A Microframework for Building Conversational AI Agents
Integrate any Python function into ChatGPT in a single line of code.
71. How to Build Basic Chatbot Without Coding and Deploy to Websites
Build best automated AI chat bot using Google Dialog flow
72. 8 Companies Using Machine Learning in Cool Ways
When asked what advice he'd give to world leaders, Elon Musk replied, "Implement a protocol to control the development of Artificial Intelligence."
73. Natural Language Processing Is a Revolutionary Leap for Tech and Humanity: An Explanation
Explore the fascinating world of Natural Language Processing - its history, growth, impact, future, and potential challenges. Dive into NLP now!
74. Chatbots Are Breaking Bad with Messed Up Responses
This almost maniacal obsession with possessing an all knowing chatbot is sweeping across industries and geographies.
75. Prompt Length vs. Context Window: The Real Limits of LLM Performance
how prompt length interacts with an LLM’s context window—why it matters, how it breaks, and how to design prompts that stay sharp and scalable.
76. Meet SOPHIA: A Clinical Decision Support System Built with Open Source Technologies
A Clinical Decision Support System (CDSS) provides the doctor with a tool that eases their work, and increases the value of the time spent with the patient.
77. Getting Started with Natural Language Processing: US Airline Sentiment Analysis
By: Comet.ml and Niko Laskaris, customer facing data scientist, Comet.ml
78. 10 Free Resources to Become a Health Data Scientist
Becoming a health data scientist can be challenging but rewarding; it merges statistical analysis with other tools to gain insights from healthcare data.
79. 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...
80. The Usefulness Of Data Science In Law Enforcement
Law enforcement agencies are not new to the data and its usage, but with the advancement in technology, Data science in law enforcement has become a need.
81. ATLAS: A Multi-Agent AI Architecture for Natural Language Service Management
A technical deep-dive into building conversational AI interfaces for enterprise ITSM systems using a three-stage multi-agent pipeline.
82. A Deep Dive Into Facebook’s AI Transcoder
Just over a week, most of you would have heard that Facebooks AI research team (FAIR) developed a neural transcompiler, that converts code from high level programming language like C++, Python, Java, Cobol into another language using ‘unsupervised translation’ . The traditional approach had been to tokenize the source language and convert it into an Abstract Syntax Tree (AST) which the transcompiler would use to translate to the target language of choice, based on handwritten rules that define the translations, such that abstract or the context is not lost.
83. ChatGPT Answered 50,000 Trivia Questions - Here's How It Did
I made ChatGPT answer 50,000 trivia questions. Find out what happens
84. A Comparison of GPT-3 and Existing Conversational AI Solutions
Earlier this year, Elon Musk-backed artificial intelligence laboratory, OpenAI, released its latest, much anticipated autoregressive language model, the Generative Pre-trained Transformer 3 (GPT-3). Emerging to much fanfare and slated as the usherer of a new age of artificial intelligence, the number of articles, blog posts, and news pieces about this language model, perhaps match only the number of parameters the GPT-3 learned; 175 billion (Ok, this may be an exaggeration, but you get my point).
85. 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.
86. How AI Has Changed Natural Language Processing
How natural language processing has been revolutionized by Artificial Intelligence and how this is currently affecting businesses.
87. Using the LDA Algorithm for Websites
Have you ever had to find unique topics in a set of documents? If you have, then you’ve probably worked with Latent Dirichlet Allocation (or LDA).
88. 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.
89. 7 AI-powered Chatbots
90. 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.
91. 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
92. ICDAR 2021 Competition: Detecting Tables Using Image Recognition
To participate go to: https://competitions.codalab.org/competitions/26979
93. TMNT: Translation Memory and Neural Translation
As we advance the state of machine translation, translation memory has its place in todays’ translation tech stack that benefits MT users and human translators.
94. 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
95. Embeddings in Machine Learning: Everything You Need to Know
Here's a deep dive into the history of machine learning embeddings, common uses, and current infrastructure solutions, including the vector database.
96. Analyzing Customer Reviews with Natural Language Processing
In this article, we build a machine-learning model to guess the tone of customer reviews based on historical data.
97. The Essential Guide to Data Augmentation in NLP
There are many tasks in NLP from text classification to question answering, but whatever you do the amount of data you have to train your model impacts the model performance heavily.
98. 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.
99. Using Explainable AI in Decision-Making Applications
Here we explore the essence of explainability in AI and analyzing how applies to decision support systems in healthcare, finance, and other different industries
100. Building a Trivia App for Google Assistant
Using a Template to Create a Trivia Voice App for The Office
101. Get The Most Out Of Everything You Read Using Python
Imagine reading something, and never losing track of that information.
102. To be Relevant or not to be: a Search Story about Precision and Recall
With the amount of data created growing exponentially each year and forecasted to reach 59 zettabytes in 2020 and more than 175 zettabytes by 2025, the importance of discovering and understanding this data will continue to be, even more than before, a decisive and competitive differentiator for many companies.
103. 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.
104. A Brief Into to NLP in the Media & Communication Industry
In this write-up, we will understand the role of NLP in the media industry, its impact, and how it will help to clear out the issues hampering growth.
105. Improving AllenNLP’s Method of Replacing Coreferences
We’ve decided to consider AllenNLP as our main model, and utilize Huggingface as more of a reference while using it mostly as a refinement to AllenNLP output.
106. RAG Systems Are Breaking the Barriers of Language Models: Here's How
Explore how RAG systems differ from traditional large language models by leveraging real-time data access and applications.
107. 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.
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. Going Beyond Word Clouds: Using Thematic Clustering to Understand Your Customers
Artificial Intelligence (AI) technology is too far evolved to still be relying on basic, word cloud analysis for your survey data
111. Generative Language Flashcards
Using ChatGPT, Stable Diffusion and SpeechT5 to automatically generate word list flashcard for early childhood right brain education
112. From Facebook to MindverseAI: Felix Tao's Insights on AI Evolution and the Future of Large Language
NLP expert discusses the evolution of AI, waking up the consciousness and the biggest issues with LLMs...
113. Our Proposed Framework: Using LLMs for Thematic Analysis
The framework relies on OpenAI’s GPT-4 model’s capabilities to perform complex NLP tasks in zero-shot settings
114. 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>
115. 8 Factors to Consider When Building an AI App for Android or iOS
Creating an app with AI for Android and iOS can be a challenging but rewarding task. Step-by-step guide on how to create an app with AI.
116. Russian Politicians Want To Take Back Alaska…and Other AI-Generated Jokes
A fun, quick summary of recent research around ML-generated humor and why everyone should be watching this space. Laugh, earthlings.
117. On Raising $17M to Build AI Based Personal Assistants for Salespeople
An interview with the founder of Winn.AI, a mixture of Alex and Salesforce that aims to help b2b sales with its advanced machine learning capabilities
118. Vector Embedding 101: The Key to Semantic Search
As the amount of data continues to grow at an unprecedented rate, traditional keyword-based search will become less effective.
119. Your AI Can’t Understand Language Until It Learns This Trick
Discover the power of word embeddings in NLP! Learn how these vector-based representations capture semantic and syntactic relationships between words.
120. Creating an Efficient Book Search Field Using Apache SeaTunnel, Milvus, and ChatGPT
Using Apache SeaTunnel, Milvus, and OpenAI, we can achieve more accurate book title similarity searches through large language models.
121. An Intro to MedPaLM: ChatGPT's Healthcare-Focused "Cousin"
ChatGPT for healthcare? Learn everything you need to know about MedPaLM, a new LLM developed by Google specifically for medical and clinical applications.
122. Processing Structured and Unstructured Data with SuperAGI and LlamaIndex
SuperAGI's latest integration with LlamaIndex can extend the overall agent’s capability of understanding and working with a wide range of data types and source.
123. 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.
124. 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.
125. ChatGPT Translator VS Mine: Which One Is Better?
Does ChatGPT Translator really so good as mentioned in many posts ?
126. Accelerating Excavation and Refinement of Data Gold Mines
Unlock the potential of data-driven decision-making with generative AI and NLP.
127. Unconventional Sentiment Analysis: BERT vs. Catboost
Unconventional sentiment analysis with CatBoost. The result is comparable to BERT SOTA.
128. 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.
129. New Formula Could Make AI Agents Actually Useful in the Real World
A mathematical framework for optimizing large language models in multi-agent systems using a formal objective function balancing brevity and context.
130. 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.
131. How Effective is ChatGPT in Customer Support, Lead Generation, and Data Analytics?
Find out how to integrate Chat GPT into an app or a website. Business and technical ChatGPT use cases explained for product owners to benefit from GPT models.
132. 5 Things I Learned from Google’s New ML-Powered Recorder App
There are tons of audio recording apps in the app store, but you know things will be a bit different if Google developed a brand new one. Google recently released a new ‘Recorder’ app that is powered by its state-of-the-art Machine Learning algorithm that can transcribe what it hears with impressive precision in real-time. This is not the first time Google tried to bless its product with some AI ‘superpower’. Some of their prior attempts failed (I’m talking to you Google Clips!) and some had quite formidable success, for example, Google’s Pixel phone camera app.
133. When Did Beyoncé Start Becoming Popular? - Tackling One of the Most Common Problems in NLP: Q/A
Hello! Today I’d like to explain how to solve one of the most troublesome tasks in NLP — question answering.
134. 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.
135. Chatbots Are NOT a Replacement for Human Agents: Here’s Why
Nothing excites business owners more than the opportunities to cut cost. So it’s no surprise that in the era of chatbots, many customer service organizations are jumping at the opportunity to show human agents the door.
136. GPTerm: Creating Intelligent Terminal Apps with ChatGPT and LLM Models
In this article, the exciting realm of making terminal applications smarter is delved into by integrating ChatGPT, a cutting-edge language model.
137. LLMs Excel in NLP: Enabling Sophisticated Search Functionalities in E-commerce Platforms
Provide exceptional shopping experiences, businesses must leverage the power of LLMs as the e-commerce industry continues to evolve.
138. RAG Is Not a Feature: Why Your AI Still Hallucinates
Moving from a RAG demo to prod requires more than just vector search. Learn the four critical layers of production grade RAG systems including hybrid retr
139. How to Leverage AI in Learning Management Systems
Discover the transformative power of integrating AI into Learning Management Systems (LMS) for enhanced educational experiences.
140. 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.
141. Why No Single Algorithm Solves Deduplication — and What to Do Instead
No one-size-fits-all deduplication method exists. Learn how hybrid pipelines combine blocking, LSH, and embeddings for scalable, high-recall entity matching.
142. Why Financial Sentiment Analysis Failed Without Explainability (And How I Fixed It)
If you're building AI systems for high-stakes domains—finance, healthcare, criminal justice—remember this: a model is not a product until it's explainable.
143. 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.
144. 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)
145. Understanding Conversational AI: As Chat Enabled Customer Service
Technological innovations are necessary to cope up with the customer demands. Customers nowadays use multiple channels to access the services from a business. Thus, they expect multiple channel customer service from companies.
146. Technologies Behind No-code & Low-Code Solutions and How to Build Your Own
Let’s find out how no-code / low-code platforms are built and what it takes to create your own solution. We’ll focus on the development approaches, architecture
147. Foundation Models - A hidden revolution in enterprise Artificial Intelligence
An introductory article to bring a preliminary cognizance on the broadening prospects of foundation models in the AI industry.
148. 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.
149. 8 Open-source NLP Tools You Should Try
The write-up is about various free open-source NLP tools available in the market which any developer can use as per the requirement.
150. Harnessing Metaverse Technology to Build Your Brand Application
Let’s talk about what technologies are used in metaverse development and how businesses can create their own metaverse applications.
151. What is Natural Language Processing? A Brief Overview
Natural language processing (NLP) is a subfield of artificial intelligence. It is the ability to analyze and process a natural language.
152. Unsupervised Data Augmentation
More data we have, better performance we can achieve. However, it is very too luxury to annotate large amount of training data. Therefore, proper data augmentation is useful to boost up your model performance. Authors of Unsupervised Data Augmentation (Xie et al., 2019) proposed Unsupervised Data Augmentation (UDA) assistants us to build a better model by leveraging several data augmentation methods.
153. How To Be A Fantastic Data Scientist: An Expert Shares His Secrets
In the latest episode of our podcast, Machine Learning that Works, I had a great pleasure to talk to Gabriel Preda, a Lead Data Scientist at Endava and a Kaggle Grandmaster.
154. Cocktail Alchemy: Creating New Recipes With Transformers
Build a transformer model with natural language processing to create new cocktail recipes from a cocktail database.
155. 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.
156. How Machine Learning is changing Financial Services in 2020
157. AI & Its Impact Within a Homogenous Society
Sanksshep Mahendra insists that the frameworks of society aren’t moving quickly enough to keep pace with the ever-increasing rate of change AI has.
158. Blockchain-Verified Sentiment
The explosion of content on the world wide web, social media and chat networks greatly increased the interest in sentiment analysis from a growing number and variety of interested parties.
159. NATURAL LANGUAGE PROCESSING (NLP) | EXPLAINED
Natural Language Processing (NLP) refers to AI method of communicating with intelligent systems using a natural language such as English.
160. Maximizing NLP Capabilities with Large Language Models
While NLP effectively facilitates machines to understand human language, the LLM capabilities have been greatly enhanced. Read this blog post to learn more.
161. How I Extracted Meaningful Information from Inconsistent Data Using ChatGPT
Data Analyis Project using Spacy and Regular Expressions to extract specific strings from a data set.
162. How the Conversational AI Analytics will transform the business?
163. 3 Years After Forking Sphinx: A Brief Report on Manticore Search
164. As AI Gets Better at Writing, There's Some Trouble on the Horizon
In the realm of AI development, there's perhaps no more important goal than to create systems that can truly master natural language processing (NLP). That's the key to making AI broadly useful, as it will need to interact with humans (who lack the programming skills to speak machine languages). On the path to NLP, it's fair to say that getting an AI to speak human languages is a prerequisite to getting them to understand what people are saying.
165. To be Relevant or not to be: a Search Story about Precision and Recall
With the amount of data created growing exponentially each year and forecasted to reach 59 zettabytes in 2020 and more than 175 zettabytes by 2025, the importance of discovering and understanding this data will continue to be, even more than before, a decisive and competitive differentiator for many companies.
166. 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?
167. First Talos, Now GPT-3: A Deep Dive
The island Crete in Greek mythology is strongly associated with the ancient Greek gods. It is the backdrop to many famous Greek myths, my favourite being Talos.
168. What Can Recurrent Neural Networks in NLP Do?
Recurrent Neural Networks (RNN) have played a major role in sequence modeling in Natural Language Processing (NLP) . Let’s see what are the pros and cons of RNN
169. 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”.
170. How to Analyze Call Sentiment With Open-Source NLP Libraries
Unlock call sentiment analysis using open-source NLP. Discover how to analyze customer emotions, improve service, and gain valuable insights from voice data.
171. Natural Language Processing in Healthcare: A Path to Adoption
Whether you already have experience with AI or not, implementing natural language processing in healthcare can take some of the load off your employees’ .......
172. When the Best Solution Isn’t the Obvious One: A Case Study in Address Parsing
This case study explores how a "simple" XML deserialization task turned into a complex NLP problem requiring integration of a C library into Java ecosystem.
173. Ways To Overcome Linguistic Barriers with Language Technologies
COVID-19 has impacted every other industry and has made people adopt newer norms. The traditional translation industry is no different. Several disruptions have been introduced to keep things moving, thanks to Big data and machine translation technologies that have enabled the world to do business as usual.
174. Incorporating NLP Capabilities Into an Existing Application Stack Is Easier Than Ever: Here's Why
Speed development, create content and speed data-driven decisions with new ML tools that make it easy to incorporate NLP into your tech stack.
175. Exploring T5 Model : Text to Text Transfer Transformer Model
Recent years have seen a plethora of pre-trained models such as ULMFiT, BERT, GPT, etc being open-sourced to the NLP community. Given the size of such humungous models, it's nearly impossible to train such networks from scratch considering the amount of data and computation that is required. This is where a new learning paradigm "Transfer Learning" kicks in. Transfer learning is a research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem.
176. How Web3 Is Shaping the Future of Marketing
How Web3 Is Shaping the Future of Marketing
177. How Banks are Damaging their Business by Misusing Chatbots
The US State of Multichannel Customer Service, found two-thirds of customers are frustrated with companies before they speak with agents.
178. From Sustainable Agriculture to Programming - with Noonies Nominee Akis Loumpourdis
I am Akis Loumpourdis, a 2021 Noonies Nominee. This is a small interview to get to know me a bit better.
179. 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.
180. Tired of Digging Through Long PDFs? You Can Build a Bot That Can Quickly Answer Questions for You
A beginner-friendly guide to building your own “Ask My Book” bot using Retrieval-Augmented Generation (RAG).
181. How AI is Reshaping Enterprise Analytics
Thirumal Raju Pambala highlights that AI integrated into analytics platforms marks a pivotal shift.
182. 60 Stories To Learn About Conversational Ai
Learn everything you need to know about Conversational Ai via these 60 free HackerNoon stories.
183. Thrilled to be Recognized as #1 Startup in Budapest
We’re really proud that we can be in a group of like-minded technologists and be acknowledged by them.
184. What Soggy Breadsticks Taught Us About Service and Social Listening
Breadsticks, of all things, were the reason for a huge backlash at the famous Olive Garden chain of restaurants both from customers and employees.
185. LLMs Cannot Find Reasoning Errors, but They Can Correct Them!
In this paper, we break down the self-correction process into two core components: mistake finding and output correction.
186. 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.
187. Customer Service Automation: What It Is And How To Find Balance
Customer service automation is not a new thing in business. Many brands have successfully implemented automation to streamline the processes and save costs. However, there are still many questions on how to balance automation with a human touch and worries about sounding robotic and impersonal.
188. Mental Health Basics: Diagnosis, Treatment, Tech Tools
Last year I found I had ADD.
189. 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?
190. Tell If Your SMS is Spam
Introduction
191. The History of LLMs - Part 1: The Era of Mechanical Translation and How It Crashed
A series about the history of large language models (LLMs). First episode: discover the birth of mechanical translation, one of the first areas of NLP.
192. Bits of Thought: Yelp Content As Embeddings
You want an intro to embeddings, learn about the cool things done at Yelp, and later play with the off-the-shelf models available on Hugging Face? Let's dive in
193. How to Build Scalable NLP-Powered Voice Agents for Seamless User Interactions
Explore how to build scalable NLP-powered systems that turn voice requests into backend actions.
194. Why Embeddings Are the Back Bone of LLMs
You’re not alone if the term “embeddings” has ever left you scratching your head or feeling lost in a sea of technical jargon.
195. Exploring the Advancements in Few-Shot Learning with Noisy Channel Language Model Prompting
Noisy channel language model prompting takes inspiration from classic noisy channel models in machine translation to improve few-shot text classification.
196. Natural Language Processing Applications in HR Software
A company’s HR department holds a unique role that is entirely centered around the employees’ experience. Not only do these functions span over the length of the employee’s tenure, it even covers their involvement from the moment candidates are considered for the job.
197. Why Are We Training AI like Dogs Instead of Humans?
The fundamental problem of the modern AI is that it tries to create a sophisticated trained dog. This approach is a dead end and needs to be drastically changed
198. CTDS: 1 Year anniversary | Kaggle Contest | CTDS.News Launch
In the Birthday AMA Episode, Sanyam Bhutani had shared a small series of "exciting updates" coming to CTDS.Show:
199. Is GPT Powerful Enough to Analyze the Emotions of Memes?: Methodology
Explore how ChatGPT analyzes meme emotions in this study on AI-driven sentiment analysis of social media content.
200. From Theory to Tech: How Counterspeech Research Tackles Digital Abuse
Counterspeech counters online hate via supportive rebuttals. This review bridges social and computer science to enhance automated generation.
201. Enhancing Content Diversity with NLP-Based Clustering
How NLP-based clustering techniques help diversify content, reduce repetition, and promote discovery through smarter recommendation systems.
202. Concluding Remarks on Consistency Large Language Models and Future Directions
CLLMs offer a simpler, more efficient approach to LLM acceleration without extra architectures or draft models, achieving significant speedup gains.
203. Building A Chatbot On Your Own Might Not Make As Much Sense As You Think
Over the past decade plus, chatbots have dominated the conversation (no pun intended) when it comes to digital engagement. You’ve undoubtedly had experiences interacting with them, some helpful while others underwhelming, and perhaps even fiddled around with building one on your own.
204. Optimizing Language Models: Decoding Griffin’s Local Attention and Memory Efficiency
Explores how Griffin’s local attention and recurrent layers outperform traditional Transformers, improving language modeling at scale and faster inference.
205. DreamLLM: Additional Related Works to Look Out For
This breakthrough garnered a lot of attention and paved the way for further research and development in the field.
206. 2 Years In The Life Of AI, ML, DL And Java - Part II
A follow-up post on the back of the post two-years ago with the title "Two Years In The Life Of AI, ML, DL And Java"
207. "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.
208. Teaching Old LLMs New Tricks: The Consistency Model Makeover for Speed
CLLMs refine pre-trained LLMs for faster Jacobi decoding by consistently mapping trajectory states to fixed points, accelerating inference.
209. How to Streamline the Landlord-Tenant Relationship Through Conversational AI
According to Mashivor, 50% of tenants move out because they are not happy with their landlord. The landlord-tenant relationship is more often than not a contentious one. It features two sides with similar intentions but entirely different priorities. Both parties are interested in peaceful, fluid, and uneventful correspondence and both are wary of being cheated, ill-treated, and misinformed.
210. Naive Sentiment Analysis Using R
Cleuton Sampaio, October 2019
211. Approaches to Counterspeech Detection and Generation Using NLP Techniques
Counterspeech detection uses binary or multi-label classification; generation leverages LLMs like GPT-2, facing evaluation and deployment challenges.
212. Splitting Hairs: Exploring the Interrelationship of Machine Learning and AI
Navigating the Nuances: The Relationship and Differences Between AI and Machine Learning
213. Sea of Transformation: Chatbots in The Logistics Industry
214. Decoding the Magic: How Machines Master Human Language
LLMs in an easy way explanation, InstructGPT explanation, ML for non professionals, how machine learning models trained
215. Refining Jacobi Decoding for LLMs with Consistency-Based Fine-Tuning
CLLMs boost LLM inference 2.4-3.4x by refining Jacobi decoding to rapidly predict fixed points, preserving quality without extra memory.
216. 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.
217. Using Sentiment Analysis to Attain and Retain Customers
Analyzing customer sentiment allows businesses to look into how customers feel about their products & services.
218. How Advanced Analytics Can Improve the Public Sector
Advanced analytic models can identify and predict negative outcomes such as health and safety challenges or compliance risks that would be overlooked by manual.
219. How Many Glitch Tokens Hide in Popular LLMs? Revelations from Large-Scale Testing
Under-trained token indicators efficiently flag risky tokens in LLMs, with cross-model results showing 0.1–1% of vocabularies consistently problematic.
220. Is GPT Powerful Enough to Analyze the Emotions of Memes?: References
Explore how ChatGPT analyzes meme emotions in this study on AI-driven sentiment analysis of social media content.
221. Covert Google Voice Into Your Own Private Bouncer Or Receptionist
222. Is GPT Powerful Enough to Analyze the Emotions of Memes?: Abstract & Intro
Explore how ChatGPT analyzes meme emotions in this study on AI-driven sentiment analysis of social media content.
223. 6 Chatbot Mistakes that Scare Your Customers Away
Six unforgivable mistakes that scare off your customers and prospects? The Smart Tribune team answers you.
224. How Griffin’s Local Attention Window Beats Global Transformers at Their Own Game
Explores how Griffin’s local attention and recurrent layers outperform traditional Transformers, improving language modeling at scale and faster inference.
225. Stemming vs. Lemmatization: What Healthcare Text Data Taught Me About NLP Choices
An NLP experiment on stemming vs. lemmatization in healthcare text reveals why precision matters more than speed when lives are at stake.
226. Sentiment Classification for 2019 Lok Sabha Elections Using Text Based Classifiers
227. Bots + Legaltech = Meet AILIRA
Ailira (www.ailira.com) the ”artificially intelligent legal information research assistant”, is an AI chatbot that uses natural language processing. The chatbot has been designed to understand and process sophisticated technical legal questions & search quickly. Ailira was created by Adrian Cartland, the founder of Cartland Tech and the law firm without lawyers.
228. Is GPT Powerful Enough to Analyze the Emotions of Memes?: Discussion
Explore how ChatGPT analyzes meme emotions in this study on AI-driven sentiment analysis of social media content.
229. The Creation of ArSyTa, a Novel 8.27-Million-Context Dataset for Local Citation Recommendation
This paper details ArSyTa, a new, massive dataset for citation AI. It has 8.27M rich contexts from arXiv papers to improve recommendation models.
230. Artificial Minds, Human Consequences: Explaining AI’s Impact on Our Education, Cognition, and More
This text critiques AI language models' impact on learning, creativity, and ethics. Explores copyright issues, bias, and the fundamental differences between hum
231. My Journey Into Predicting States Using Emoji Observations With Viterbi Algorithm
See the implementation of the Viterbi algorithm in Python
232. Is GPT Powerful Enough to Analyze the Emotions of Memes?: Experiment Results
Explore how ChatGPT analyzes meme emotions in this study on AI-driven sentiment analysis of social media content.
233. 7 Must-Read Generative Models Papers from ICLR 2020
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.
234. A Three-Stage Architecture for Precision Citation Recommendation
SymTax, a new AI for citation recommendation, uses a "symbiotic" model and taxonomy fusion to more accurately predict relevant scientific papers.
235. Counterspeech Impact: Lessons Learned and the Path to Scalable Interventions
Counterspeech's impact varies; real-world, scalable tests are needed. Interdisciplinary collaboration is key to automate effective hate mitigation.
236. How DigiSkills AI Chatbot helped 10K students enroll for the program
DigiSkills Training Program is Pakistan’s first Online Training Program that offers free-of-cost training courses. The platform was created to train the youth with in-demand digital skills such as content marketing, graphic designing, Creatives and SEO, etc.
237. A Quantitative and Qualitative Analysis of the SymTax Citation Recommendation Model
This deep-dive analysis proves why SymTax works, showing its 'symbiotic' enricher and taxonomy fusion are essential for its state-of-the-art performance.
238. The Quest for Faster LLMs: What Came Before Consistency Models
Reviews methods for efficient LLM inference (training-free vs. training-based), LLM distillation, and consistency models, positioning CLLMs as unique.
239. My Experiments With AI Poetry And Some Random Thoughts
240. From Twitter to Reddit: Exploring Data Sources for Computational Counterspeech
Computational counterspeech studies use datasets from Twitter, YouTube, Reddit, or expert-written content, often in English, for detection and generation.
241. How Symbiotic AI Can Find Your Paper's Next Great Citation
SymTax is a novel AI for citation recommendation. It mimics human behavior by using a "symbiotic" model and hyperbolic geometry to improve accuracy.
242. A Comparative Performance Analysis of SymTax on Five Citation Recommendation Datasets
This paper presents empirical proof that the SymTax model significantly outperforms state-of-the-art AI on all major citation recommendation benchmarks.
243. Is GPT Powerful Enough to Analyze the Emotions of Memes?: Related Work
Explore how ChatGPT analyzes meme emotions in this study on AI-driven sentiment analysis of social media content.
[244. New Way for Business Optimisation is Out Now:
Rake System and Their Success Story](https://hackernoon.com/new-way-for-business-optimisation-is-out-now-rake-system-and-their-success-story-v8vy325u)
The Rake system understands and manages client requests related to company services. Regardless of the requests: text, voice - Rake’s chatbots understand and process all of them using artificial intelligence. The chatbot has been designed for W5Golf, and is the company that provides customer experience optimisation solutions and helps develop customer experience strategies that deliver results. The company’s solution helps to strengthen relationships with your customers by providing a system that optimises relevant engagements and improved services.
245. 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.
246. The Roots of Counterspeech: A Review of Social and Technical Perspectives
This article reviews social science and computational counterspeech studies, highlighting the need for interdisciplinary work to enhance hate mitigation.
247. Uber AI Labs Senior Research Scientist Talks TensorFlow 2.0 [Interview]
248. Why SymTax is Not Just Another AI, But a New Partner for Researchers
This work presents SymTax, a novel citation recommendation AI. It models human behavior via "symbiosis" and sets a new state-of-the-art.
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