Recurrent Neural Networks with Python Quick Start Guide: Sequential learning and language modeling with TensorFlow

Price: $46.61
(as of Dec 29,2024 05:25:46 UTC – Details)

ASIN ‏ : ‎ B07L3N6P9Q
Publisher ‏ : ‎ Packt Publishing; 1st edition (November 30, 2018)
Publication date ‏ : ‎ November 30, 2018
Language ‏ : ‎ English
File size ‏ : ‎ 7287 KB
Text-to-Speech ‏ : ‎ Enabled
Screen Reader ‏ : ‎ Supported
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 124 pages
Page numbers source ISBN ‏ : ‎ 1789132339

Recurrent Neural Networks with Python Quick Start Guide: Sequential learning and language modeling with TensorFlow

In this guide, we will explore the power of Recurrent Neural Networks (RNNs) for sequential learning and language modeling using Python and TensorFlow. RNNs are a type of neural network that is well-suited for processing sequential data, such as time series data, text, and speech. They have the ability to capture dependencies in the input data over time, making them ideal for tasks like language modeling, speech recognition, and machine translation.

To get started with RNNs in Python, we will be using the TensorFlow library, which provides a high-level API for building neural networks. We will walk through the process of creating a simple RNN model for language modeling, training it on a dataset of text, and generating new text samples using the trained model.

Here are the steps we will cover in this quick start guide:

1. Installing TensorFlow and other required libraries
2. Preprocessing the text data
3. Building and training the RNN model
4. Generating new text samples with the trained model

By the end of this guide, you will have a solid understanding of how to use RNNs for sequential learning and language modeling in Python with TensorFlow. Let’s get started!
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