In this video I will show you how to create an input pipeline when dealing with text. We focus on TextLineDataset which is a quite general method that you can adapt to many different text data structure. In the tutorial I show you mainly demonstrating how to load the imdb dataset from a text file but I also try and give you some ideas and what to do if you're dealing with text data is differently structured, like split over multiple text files or a translation dataset split over two text files.

Download the data (IMDB) used in the video here:
https://www.kaggle.com/dataset..../ff33c576e11e20d0c3a

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OUTLINE:
0:00 - Introduction and Dataset Overview
1:39 - Load using TextLineDataset
4:13 - Filtering Dataset
8:12 - Creating Vocabulary
13:43 - Numericalizing with TokenTextEncoder
18:10 - Applying map on datasets
20:35 - Simple Model
22:30 - Dataset in Several Files
25:50 - Sketch Load Translation Dataset
29:22 - Ending

Welcome to the next tutorial covering deep learning with Python, Tensorflow, and Keras. We've been working on a cryptocurrency price movement prediction recurrent neural network, focusing mainly on the pre-processing that we've got to do. In this tutorial, we're going to be finishing up by building our model and training it.

Text tutorials and sample code: https://pythonprogramming.net/....crypto-rnn-model-dee

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