English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 47m | 296 MB
There is a growing demand to harness the power of natural language processing (NLP) and deep learning models to be able to make sense of textual data and reduce the emotional intervention of humans in order to make better decisions. In this course, instructor Harshit Tyagi provides a complete guide to understanding NLP using recurrent neural networks (RNNs). Harshit begins by introducing you to word encodings and using TensorFlow for tokenization. He describes the important concept of word embeddings and shows you how to use TensorFlow to classify movie reviews and project vectors. Harshit discusses RNNs and long short-term memory (LSTM), then shows you how to improve the movie review classifier from earlier in the course. He concludes with a discussion of how you can train RNNs to predict the next word in a sentence, which in turn allows you to generate some original text.
Table of Contents
1 Leveraging deep learning for natural language processing
2 Projecting vectors using TensorFlow
3 Building a text classifier
4 Challenge- Text classification
5 Solution- Text classification
6 Introduction to RNNs
7 Implementing LSTMs with TensorFlow
8 Improving your movie review classifier
9 Challenge- Yelp review classifier
10 Solution- Yelp review classifier
11 Introduction to text generation
12 Introduction to natural language processing
13 Predicting the next word
14 Challenge- Generate poetry
15 Solution- Generate poetry
16 Learning more about NLP with TensorFlow
17 Introduction to word encodings
18 Tokenization using TensorFlow
19 Padding the sequences
20 Challenge- Recognizing sarcasm in the text
21 Solution- Recognizing sarcasm in the text
22 Introduction to word embeddings
23 Classifying movie reviews using TensorFlow
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