Natural Language Processing in TensorFlow

Go to Course: https://www.coursera.org/learn/natural-language-processing-tensorflow

Introduction

### Course Review: Natural Language Processing in TensorFlow **Course Overview:** In the realm of artificial intelligence and machine learning, proficiency in Natural Language Processing (NLP) is becoming increasingly vital. The course "Natural Language Processing in TensorFlow," part of the deeplearning.ai TensorFlow Specialization on Coursera, is meticulously designed for software developers seeking to harness the power of AI algorithms. Deep dive into using TensorFlow, one of the most popular open-source frameworks, as you learn how to build scalable NLP systems while implementing best practices for machine learning. **Course Details:** By the end of this course, participants will gain a deep understanding of various NLP techniques and applications, including how to process and interpret text data effectively. Here’s a glance at what you can expect from the syllabus: 1. **Sentiment in Text** This module introduces you to the core concept of tokenization. You’ll explore how to convert textual data into numeric values, a fundamental step in preparing for neural network training. Utilizing TensorFlow’s Tokenizer and `pad_sequences` APIs, you will become proficient in preparing and encoding text data for subsequent analysis. 2. **Word Embeddings** Building upon the basics, this segment delves into embeddings, allowing you to represent tokens as vectors in a high-dimensional space. You’ll learn how these vectors can be fine-tuned to capture semantic relationships between words. The emphasis here is on training a neural network using movie reviews as labeled datasets, where you identify the key words driving sentiment. 3. **Sequence Models** Understanding context is key to NLP, and this module focuses on how word sequences influence meaning. You’ll explore various model formats designed to comprehend the order of words. The module teaches how context initiates a more nuanced sentiment analysis, essential for applications needing greater accuracy in text interpretation. 4. **Sequence Models and Literature** Pushing the boundaries of NLP, this unique week allows you to create a poetry generator. By implementing your prior knowledge, you’ll predict the next word in a series, training your model on the lyrical beauty of traditional Irish songs. This creative project not only reinforces technical skills but also showcases the artistic side of NLP. **Recommendation:** I highly recommend the "Natural Language Processing in TensorFlow" course for those eager to build a strong foundation in NLP while developing hands-on skills utilizing TensorFlow. The structure of the course is methodical, breaking down complex topics into digestible segments, making it accessible whether you are a beginner or someone with prior experience in machine learning. The engaging hands-on projects, particularly the poetry generator, allow for creativity and practical application of the learned concepts, ensuring that you not only comprehend theoretical aspects but also gain real-world skills. Enrolling in this course will position you well within the rapidly evolving field of AI, as NLP technologies are increasingly integrated into various applications ranging from customer service bots to advanced data analytics. **Conclusion:** In summary, the "Natural Language Processing in TensorFlow" course stands out as a comprehensive educational experience for learners interested in mastering NLP. Its focus on practical implementation alongside theoretical principles will equip you with the necessary tools to excel in the developing landscape of AI technologies. Whether you intend to enhance your career, pivot into a new field, or simply explore the fascinating world of language processing, this course is a valuable investment in your professional growth.

Syllabus

Sentiment in text

The first step in understanding sentiment in text, and in particular when training a neural network to do so is the tokenization of that text. This is the process of converting the text into numeric values, with a number representing a word or a character. This week you'll learn about the Tokenizer and pad_sequences APIs in TensorFlow and how they can be used to prepare and encode text and sentences to get them ready for training neural networks!

Last week you saw how to use the Tokenizer to prepare your text to be used by a neural network by converting words into numeric tokens, and sequencing sentences from these tokens. This week you'll learn about Embeddings, where these tokens are mapped as vectors in a high dimension space. With Embeddings and labelled examples, these vectors can then be tuned so that words with similar meaning will have a similar direction in the vector space. This will begin the process of training a neural network to understand sentiment in text -- and you'll begin by looking at movie reviews, training a neural network on texts that are labelled 'positive' or 'negative' and determining which words in a sentence drive those meanings.

In the last couple of weeks you looked first at Tokenizing words to get numeric values from them, and then using Embeddings to group words of similar meaning depending on how they were labelled. This gave you a good, but rough, sentiment analysis -- words such as 'fun' and 'entertaining' might show up in a positive movie review, and 'boring' and 'dull' might show up in a negative one. But sentiment can also be determined by the sequence in which words appear. For example, you could have 'not fun', which of course is the opposite of 'fun'. This week you'll start digging into a variety of model formats that are used in training models to understand context in sequence!

Taking everything that you've learned in training a neural network based on NLP, we thought it might be a bit of fun to turn the tables away from classification and use your knowledge for prediction. Given a body of words, you could conceivably predict the word most likely to follow a given word or phrase, and once you've done that, to do it again, and again. With that in mind, this week you'll build a poetry generator. It's trained with the lyrics from traditional Irish songs, and can be used to produce beautiful-sounding verse of it's own!