Natural Language Processing with Deep Learning in Python

Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.In this course we are going to look at NLP (natural language processing) with deep learning.Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices.These allowed us to do some pretty cool things, like detect spam emails, write poetry, spin articles, and group together similar words.In this course I’m going to show you how to do even more awesome things. We’ll learn not just 1, but 4 new architectures in this course.First up is word2vec.In this course, I’m going to show you exactly how word2vec works, from theory to implementation, and you’ll see that it’s merely the application of skills you already know.Word2vec is interesting because it magically maps words to a vector space where you can find analogies, like:king - man = queen - womanFrance - Paris = England - LondonDecember - Novemeber = July - JuneFor those beginners who find algorithms tough and just want to use a library, we will demonstrate the use of the Gensim library to obtain pre-trained word vectors, compute similarities and analogies, and apply those word vectors to build text classifiers.We are also going to look at the GloVe method, which also finds word vectors, but uses a technique called matrix factorization, which is a popular a