Types of Natural Language Processing

Hadelin de Ponteves
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Deep Learning and NLP A-Z™: How to create a ChatBot

Learn the Theory and How to implement state of the art Deep Natural Language Processing models in Tensorflow and Python

11:38:44 of on-demand video • Updated April 2021

  • Why this is important
  • Types of Natural Language Processing
  • Classical vs. Deep Learning Models
  • End to End Deep Learning Models
  • Seq2Seq Architecture & Training
  • Beam Search Decoding
English [Auto] Hello and welcome back to the course on deep natural language processing and philippic talk about the types of natural language processing. So we've got two Venn diagrams here or we got a Venn diagram of two circles in it and we're going to look at the different areas of natural language processing that are going to come up in this course. So on the left of natural language processing overall and this refers to the whole circle on the left. So the reason why we've called in just this great part is because that's not overlapping part. So we know that anything here is just natural language processing. We evolved with disregard to the second circle. But natural language processing is indeed everything that is in this circle. Then we've got on the right deep learning. So these are all algorithms that have something to do with neural networks deep learning. Basically anything that's called a deeper or an algorithm falls in here. They don't have to be natural language processing. They can be classification they can be anything so they can be that's deeper here and natural language processing is any algorithm any mortal that has something to do with processing of natural language into machine terms. And then finally in the overlap we have deep and all. So these are models which have to do with natural language processing but also which are deep learning more which are neural networks. And so that's the part that we're going to be aiming for but it's also very good to have visibility of all three. Because in this course we will be talking about some models that fall just in here and then we'll be talking about those here and it'll be good to compare and see how the world has changed over time and why these models are often better than these models. And the other thing to note here is that the size of these diagrams is not reflective of the importance or the volumes of these different fields so I just said circles on the same size simply because we want a visual representation of all the overlap and that these fields exist but don't take size into account. It's not to scale at all. And finally there is a another part another part of this event diagram which is very important to us and it is this part over here a sub section of the deep and Piccola sequence to sequence so sequences sequence models of the most cutting edge the most powerful models that exist right now for natural language processing. And that's what we are going to be looking at. So as you'll see throughout this course we will make our way through the natural language processing side of things and to deepen O.P. and then we'll go into sequence to sequence. It'll be a fun and exciting journey. And the other thing that I wanted to mention is you will also notice that throughout this course even though it's focused on Chadwell so we won't be talking about just chat bots. We'll be looking at different examples of how these models from here or from here and from here can be applied to different things because the applications are huge. We think we can apply them in a natural neuro machine translation we can apply them in image captioning we can apply them in speech recognition questions and answers summarization lots and lots of models so we will be looking at different ones and they will be of different types. So this map will come in handy as we go through the course and it will be popping up here and there. So I think it was very important for us to set the foundation right so that now we're ready to proceed. And I can't wait to see you on the next tutorial and until then enjoy the deep and natural language processing.