What you'll learn
How to code word embeddings from scratch
How to perform stochastic gradient descent
How to augment data for natural language processing
How to perform negative sampling
How to perform sub-sampling

Description
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 so slow in natural language processing?

How can we deal with small datasets for natural language processing?

How can we improve word embedding using negative sampling?

What is the best way to to deal with proper nouns in natural language processing?

What were some of the historical approaches to natural language processing?

What can word Descriptions teach us about how computers understand language?

There is zero fluff in this course. It is taught at a brisk pace, and is intended for motivated beginners who want deeper insights into natural language processing. Those that complete this course will learn how to implement research papers on there own; you'll never have to rely on Medium blog posts again.

Screenshots

Citación :

https://rapidgator.net/file/0ce6e9dba2d115a638021ff4ff00b40b/bi1cy.Natural.Language.Processing.From.First.Principles.part1.rar.html
https://rapidgator.net/file/41aec23898bcb0e7a0ff4c94e40cb233/bi1cy.Natural.Language.Processing.From.First.Principles.part2.rar.html


https://uploadgig.com/file/download/57008Ee1b7168F89/bi1cy.Natural.Language.Processing.From.First.Principles.part1.rar
https://uploadgig.com/file/download/66201e454d12Fd92/bi1cy.Natural.Language.Processing.From.First.Principles.part2.rar


https://nitroflare.com/view/E8F6957C53ECB4B/bi1cy.Natural.Language.Processing.From.First.Principles.part1.rar
https://nitroflare.com/view/DCB6BF5E975B50F/bi1cy.Natural.Language.Processing.From.First.Principles.part2.rar