Natural Language Processing From First Principles

About this course

In this course motivated beginners will learn the fundamentals of natural language processing and deep learning. Students will code their own word embedding vectors from scratch, using just Numpy and a little bit of calculus. For students who don't have the required background, a crash course in the required mathematics is included. We'll cover the fundamentals of differential calculus and linear algebra in a succinct overview, so students can easily follow all mathematical derivations. Rather than simply be presented with results, each step of the mathematical derivations is included. This is to help students foster a deeper understanding of natural language processing and artificial intelligence in general.Far from being a course where students are simply spoon fed the instructors' interpretation, students will learn to gather information directly from the source. I will show you a repeatable and easy to remember framework to read, understand, and implement deep learning research papers. You will get insight into how the verbiage in research papers maps to real world code. This is an essential skill set for all practitioners of artificial intelligence and data science, and will help you stand out from the crowd. Throughout the course, good coding practices will be stressed. Students will learn the fundamentals of writing pythonic and extensible code from the very beginning, so that they can easily transition into writing more complex code for production.By the end of the course, students will be able to answer the following questions:What is the difference between the skip-gram and continuous bag of words models?What is distributional semantics?How can we use vectors to teach computers about language?How do we derive the word2vec gradients?Why is the softmax function s