Home / Data Science / Machine Learning / Natural Language Processing in TensorFlow (Coursera) Natural Language Processing in TensorFlow (Coursera) 5.00 out of 5 (1 customer review) If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. Enroll for Free Categories: Data Science, Machine Learning Description Additional information Reviews (1) Description If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In Course 3 of the deeplearning.ai TensorFlow Specialization, you will build natural language processing systems using TensorFlow. You will learn to process text, including tokenizing and representing sentences as vectors, so that they can be input into a neural network. You’ll also learn to apply RNNs, GRUs, and LSTMs in TensorFlow. Finally, you’ll get to train an LSTM on existing text to create original poetry! The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai Natural Language Processing in TensorFlow Coursera course teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization. What you will learn Build natural language processing systems using TensorFlow Process text, including tokenization and representing sentences as vectors Apply RNNs, GRUs, and LSTMs in TensorFlow Train LSTMs on existing text to create original poetry and more Syllabus – Natural Language Processing in TensorFlow Coursera Course 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! Word Embeddings 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 labeled examples, these vectors can then be tuned so that words with similar meanings will have a similar direction in the vector space. This will begin the process of training a neural network to understand the sentiment in the text — and you’ll begin by looking at movie reviews, training a neural network on texts that are labeled ‘positive’ or ‘negative’, and determining which words in a sentence drive those meanings. Sequence models 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 labeled. 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 the context in sequence! Sequence models and literature 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, 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 verses of its own! Instructor Laurence Moroney Additional information Provider Coursera School DeepLearning.AI 1 review for Natural Language Processing in TensorFlow (Coursera) 5 out of 5 MOOC Course Editorial Staff – June 4, 2021 nice course Add a review Cancel replyYour rating * Rate… Perfect Good Average Not that bad Very poor Your review *Name * Email * Save my name, email, and website in this browser for the next time I comment.