You're reading from Python Machine Learning, Second Edition Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow

Product type Paperback

Published in Sep 2017

Publisher Packt

ISBN-13 9781787125933

Length 622 pages

Edition 2nd Edition

Languages

Python

Tools

Scikit-learn

Concepts

Deep Learning

Authors (2):

Vahid Mirjalili

Sebastian Raschka

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Table of Contents (18) Chapters

Preface

1. Giving Computers the Ability to Learn from Data FREE CHAPTER

2. Training Simple Machine Learning Algorithms for Classification

3. A Tour of Machine Learning Classifiers Using scikit-learn

4. Building Good Training Sets – Data Preprocessing

5. Compressing Data via Dimensionality Reduction

6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning

7. Combining Different Models for Ensemble Learning

8. Applying Machine Learning to Sentiment Analysis

9. Embedding a Machine Learning Model into a Web Application

10. Predicting Continuous Target Variables with Regression Analysis

11. Working with Unlabeled Data – Clustering Analysis

12. Implementing a Multilayer Artificial Neural Network from Scratch

13. Parallelizing Neural Network Training with TensorFlow

14. Going Deeper – The Mechanics of TensorFlow

15. Classifying Images with Deep Convolutional Neural Networks

16. Modeling Sequential Data Using Recurrent Neural Networks

Index

Project two – implementing an RNN for character-level language modeling in TensorFlow

Language modeling is a fascinating application that enables machines to perform human-language-related tasks, such as generating English sentences. One of the interesting efforts in this area is the work done by Sutskever, Martens, and Hinton (Generating Text with Recurrent Neural Networks, Ilya Sutskever, James Martens, and Geoffrey E. Hinton, Proceedings of the 28th International Conference on Machine Learning (ICML-11), 2011 https://pdfs.semanticscholar.org/93c2/0e38c85b69fc2d2eb314b3c1217913f7db11.pdf).

In the model that we'll build now, the input is a text document, and our goal is to develop a model that can generate new text similar to the input document. Examples of such an input can be a book or a computer program in a specific programming language.

In character-level language modeling, the input is broken down into a sequence of characters that are fed into our network one character...

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Table of Contents (18) Chapters

Project two – implementing an RNN for character-level language modeling in TensorFlow

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Project two – implementing an RNN for character-level language modeling in TensorFlow

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Table of Contents (18) Chapters