Natural Language Processing with Deep Learning in Python

Complete guide on deriving and implementing word2vec, GloVe, word embeddings, and sentiment analysis with recursive nets What you'll learn: Understand and implement word2vecUnderstand the CBOW method in word2vecUnderstand the skip-gram method in word2vecUnderstand the negative sampling optimization in word2vecUnderstand and implement GloVe using gradient descent and alternating least squaresUse recurrent neural networks for parts-of-speech taggingUse recurrent neural networks for named entity recognitionUnderstand and implement recursive neural networks for sentiment analysisUnderstand and implement recursive neural tensor networks for sentiment analysisUse Gensim to obtain pretrained word vectors and compute similarities and analogiesUnderstand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion 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 isword2vec.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 - LondonDecem