"Hugging Face is making the best NLP technology available to any researcher or business."
"In NLP, the meaning of any communication is the response that you get."
"Neural machine translation changed the game."
"Seq2seq: A versatile architecture for NLP tasks."
"But one thing was clear, the moment that I would say a lot of people don't remember this, BERT was the first big moment for AI to transcend into industries, especially NLP."
"Because of the transformer architecture, the natural language processing industry can now achieve unprecedented results."
"Text classification is the process of assigning a label to a piece of text."
"We use cosine similarity to measure the similarity between sentence embeddings."
"Sentence-BERT is a model made of two branches with respect to architecture and weights."
"Whenever you set out to do some NLP work and need a model, you can check out the model Hub to see whether there's something already available."
"So when I learned NLP, I just thought, 'Wow, someone's now given me a handbook for my brain.'"
"Richard Bandler is one of those people. NLP has changed the world."
"Sentence transformers outperformed all previous models on most benchmarks."
"Sentence transformers enable us to search based on concepts rather than specific words."
"NLP is the study of not what we do, not why we do things, but how we do things."
"The goal of making word embeddings is to represent the word in a dense vector while making sure that similar words are close to each other in the embedding space."
"It is not that hard to make your own word embedding because for all of these algorithms and models that we talked about there is a library that offers you a pre-made model."
"Chris Manning... among the most influential figures across the Linguistics and NLP field."
"...the Milton model is really where NLP got the reputation for being very, very persuasive and influential."
"Because BERT was quite a success story, it inspired quite a bit of follow-up work."
"Transformers remain the preferred architecture for most NLP tasks and pre-training and fine-tuning, the paradigm itself is ubiquitous."
"Natural language processing remains a very interesting field for research and for industry applications."
"BERT, which stands for Bi-directional Encoder Representations for Transformers, was trained at Google on Wikipedia data with a language modeling objective."
"How do we use BERT in a downstream task? Well, there's multiple classes of tasks and each of them comes with its own special use case."
"BERT is available on multiple platforms, TF Hub being one of them."
"One of the most important things you can possibly do in the entire field of NLP is to learn to be able to change and adjust the way in which you communicate with yourself."
"NLP has been adapted into modern psychology."
"Many times the NLP problems can be solved completely by regular expression."
"Regular expression is super important in your NLP career."
"LSTM networks are actually considered to be quite suitable for handling NLP problems and by the end of this video you will understand why."
"Hugging Face have just announced something that I think is probably going to be a very major thing in the future of large Lounge models and NLP."
"What is sentiment analysis? It's a natural language processing technique that allows us to classify text and speech as either positive or negative, subjective or objective."
"Question Answering is one of the biggest topics in NLP at the moment."
"The SQuAD dataset is essentially one of the better known QA datasets out there."
"Word embeddings have been one of the most important ideas in NLP, in natural language processing."
"Sequence models have transformed speech recognition, natural language processing, and other areas."
"If you want to get going quickly on an NLP problem, it'd be reasonable to download someone else's word vectors and use that as a starting point."
"Once you've learned or downloaded from online a word embedding, this allows you to quite quickly build pretty effective NLP systems."
"The transformer network is an architecture that has completely taken the NLP world by storm."
"Transformers is an amazing library particularly when it comes to NLP."
"Natural language processing is just simply taking human language and processing it down to computer language."
"I've put together anxiety techniques because I'm an NLP master practitioner."
"This shows really how easily you can build these NLP neural networks now."
"Natural language processing is a field that combines linguistics and computer science."
"Transformers were designed for natural language processing, translation, chat bots, text generators, etc."
"Neuro-linguistic programming is a mental technology that you can use to create the life you desire."
"When you apply these presuppositions to the way that you think, it will immediately change the way that you think."
"Of the NLP patterns, it's probably the most powerful."
"NLP is the most comprehensive and complete study of human behavior out there that I've seen."
"Maybe that's why NLP is so effective when it comes to reframing."
"I'm pretty sure you're having fun time learning NLP in this series and I'll see you in the next video."
"Building a real life NLP application means you have to perform various steps right from data acquisition, data cleaning, to all the way till model building deployment and monitoring."
"All these steps combined are called NLP pipeline."
"I'm personally really excited about the field of natural language processing and the recent breakthroughs in the performance of large language models."
"Every NLP concept that we try to implement needs to be visualized in a way that's easier for you to understand."
"Attention mechanism is not something new, but transformer networks using attention mechanism is the beginning of the revolution in natural language processing."
"Sentiment analysis is basically a natural language processing technique used to interpret the emotions behind text data."
"Word embeddings give us a way to use an efficient dense representation in which similar words have similar representation."
"There's no reason why we cannot have natural language processing and use it for translation purposes and create made in India translators owned by Indian companies."
"You're able to leverage really sophisticated NLP models relatively easily."
"It's a really powerful natural language processing model that was originally developed by the Google team in 2018."
"The cool thing about BERT is that it is ridiculously powerful and can be used across a whole range of natural language processing tasks."
"What do we do in NLP? What are the various applications where NLP has been used?"
"Why is NLP hard? What are some of the difficulties that we face while designing algorithms for NLP?"
"The NLP Community eventually realized that the Transformer architecture could also be leveraged to achieve breakthrough performance in other tasks like question answering, text summarization, and so on."
"Engram language modeling is a very nice technique in NLP and it's applied in many different applications."
"That's where the concept of smoothing will come into picture."
"It's great to be here giving this lecture, particularly on one of my favorite topics in deep learning for NLP."
"Transformers and pre-training form the basis for much of natural language processing today."
"It is one of the most exciting and fun areas of NLP to work on."
"By applying NLP, you can see that people are generally pretty happy about your hats."
"Natural language processing is just simply how to deal with text data."
"By using this approach of treating entire log files as documents and applying a simple NLP technique, we did manage to obtain insight into CI job runs."
"The work proof that CI lock analysis can be done successfully with NLP."
"This Transformer architecture changed the complete history of NLP."
"This is going to be a perfect project if you want to acquire skills around NLP, LLM, especially using OpenAI, and build a career around it."
"Tokenization is a process of splitting the text into meaningful segments."
"It makes things much easier for you! You don't need to have too much detailed NLP knowledge."
"One of the fundamental subtasks within natural language processing is text classification."
"There are a number of use cases in natural language processing implementations where text classification might be a fundamental part of it."
"We've seen massive advances in NLP with things like Hugging Face transformers."
"The Transformer model is relatively new... published in NIPS 2017, the title is 'Attention is all you need'."
"The cool thing about the Hugging Face Transformers pipeline is that you can actually do a whole heap of really advanced and sophisticated natural processing tasks just by importing the default pipeline."
"spaCy is a modern Python library for industrial-strength Natural Language Processing."
"Named entities are 'real world objects' that are assigned a name – for example, a person, an organization or a country."
"spaCy can compare two objects and predict how similar they are."
"Combining statistical models with rule-based systems is one of the most powerful tricks you should have in your NLP toolbox."
"Recurrent neural networks have been largely replaced for natural language processing and they have been replaced by the transformer architecture."
"So in order to use Hugging Face Transformers and natural language processing to be able to generate our blog posts, there's five key things that we need to do."
"Question Answering is one of the earliest NLP tasks, and the early systems can even date back to the 1960s."
"The Stanford Question Answering Dataset is a supervised reading comprehension dataset, which consists of 100k annotated passage and question-answer triples."
"Transfer learning has emerged as a powerful technique in NLP."
"They can take natural language and do sentiment analysis on it."
"The Transformer... follows this structure that you see here so you have inputs and those inputs get encoded into a feature layer."
"Modern NLP algorithms... give modern NLP algorithms an enormous amount of flexibility to understand new language."
"Matching and mirroring in NLP is also known as pacing."
"NLP is all about having an attitude of curiosity."
"NLP is an attitude and a methodology that leaves behind a trail of techniques."
"NLP is very much about the 'how' rather than the 'what'."
"Large language models like OpenAI ChatGPT have transformed the field of natural language processing."
"Transformers are multi-purpose neural networks but we see them very often these days in natural language applications."
"Whether we may be able to cast all NLP tasks actually as a question answering problem."
"Word Sense Disambiguation is a very important component of natural language processing systems."
"It's really like an exciting era of natural language processing, and we're moving at a rate of progress which is possibly unprecedented."
"I started learning NLP and I started seeing results immediately."
"The technology of NLP is just in its infancy as far as I'm concerned."
"The underlying premise of NLP is that people are not broken; they work perfectly."
"There's a really large interest in using NLP technology."
"There's been rapid progress in the last five years due to deep learning in NLP."
"Natural language processing applications can span all these different fields, including world understanding and perception."
"Natural language processing with pre-training on text from the internet can lead to a very, very powerful pre-trained model."
"Sentiment analysis has actually been a really common and important application in natural language processing."
"It's changing incredibly rapidly and now is a very good time to be getting very good at NLP."
"It's heavily used in natural language processing because of the potential of keeping the focus of a long distance of words."
"Natural language processing comes into play."
"Transformers have ushered in this whole new era in natural language processing."