AI4ALL: Natural Language Processing


Learn the basics of Recurrent Neural Networks and Sequential models.

What you will learn

Explore the foundational building blocks of language problems

Learn to use Tensorflow to process languages

Learn to build Recurrent Neural Network models to predict sentiment

Learn and explore more advanced NLP topics such as LSTM

Description

This course is created to follow up with the AI4ALL initiatives. The course presents coding materials at a pre-college level and introduces a fundamental pipeline for a neural network model. The course is designed for the first-time learners and the audience who only want to get a taste of a machine learning project but still uncertain whether this is the career path. We will not bored you with the unnecessary component and we will directly take you through a list of topics that are fundamental for industry practitioners and researchers to design their customized neural network model.  The course follows the previous sequence where we covered Artificial Neural Network models, Convolutional Neural Network models, and Image-to-Image models. This course focuses on some of the most basical tasks in language problems and develop the basic intuition of Recurrent Neural Networks.

This instructor team is lead by Ivy League graduate students and we have had 3+ years coaching high school students. We have seen all the ups and downs. Moreover, we want to share these roadblocks with you. This course is designed for beginner students at pre-college level who just want to have a quick taste of what AI is about and efficiently build a quick Github package to showcase some technical skills. We have other longer courses for more advanced students. However, we welcome anybody to take this course!


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English
language

Content

Introduction

Introduction
Theory of Recurrent Neural Network
Embedding Layer
Dropout
Long Short Term Memory
IMDB Movie Review Dataset
Data Processing
Simple Recurrent Neural Network Models
LSTM Models Using Tensorflow
Evaluations and Interpretations

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