January 2018
Intermediate to advanced
474 pages
10h 23m
English
Content preview from Mastering TensorFlow 1.x
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RNN for Text Data with TensorFlow and Keras
Text data can be viewed as a sequence of characters, words, sentences or paragraphs. Recurrent neural networks (RNN) have proven highly useful neural network architecture for sequences. For the purpose of applying neural network models to Natural Language Processing (NLP) tasks, the text is viewed as a sequence of words. This has proven highly successful for NLP tasks such as:
- Question answering
- Conversational agents or chatbots
- Document classification
- Sentiment analysis
- Image caption or description text generation
- Named entity recognition
- Speech recognition and tagging
NLP with TensorFlow deep learning techniques is a vast area and difficult to capture in one chapter. Hence, we have attempted ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.