In this chapter, we covered a lot! We learned how to use dense instead of sparse vectors to represent words, using word2vec or GloVe, although we only used GloVe. We worked with an annotated lexicon; tidy data can already bring a lot of insight! No need to bring in the heavy artillery in many cases. We saw that slightly more complicated models may not perform well (adding layers to the feed-forward neural network); surprisingly, much more complicated models can (using bidirectional LSTMs)! After that, we provided a reference for connecting to Twitter, while keeping in mind that terms of service should be respected. For this, we used previously calculated vector embeddings and models to evaluate the sentiment of new data. And, don't forget, a key point—always check your data! Remember, garbage in, garbage out. Even the best models will provide useless results if the wrong data is used.
Deep Learning with R for Beginners
By :
Deep Learning with R for Beginners
By:
Overview of this book
Deep learning has a range of practical applications in several domains, while R is the preferred language for designing and deploying deep learning models.
This Learning Path introduces you to the basics of deep learning and even teaches you to build a neural network model from scratch. As you make your way through the chapters, you’ll explore deep learning libraries and understand how to create deep learning models for a variety of challenges, right from anomaly detection to recommendation systems. The Learning Path will then help you cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud, in addition to model optimization, overfitting, and data augmentation. Through real-world projects, you’ll also get up to speed with training convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs) in R.
By the end of this Learning Path, you’ll be well-versed with deep learning and have the skills you need to implement a number of deep learning concepts in your research work or projects.
Table of Contents (23 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Free Chapter
Getting Started with Deep Learning
Training a Prediction Model
Deep Learning Fundamentals
Training Deep Prediction Models
Image Classification Using Convolutional Neural Networks
Tuning and Optimizing Models
Natural Language Processing Using Deep Learning
Deep Learning Models Using TensorFlow in R
Anomaly Detection and Recommendation Systems
Running Deep Learning Models in the Cloud
The Next Level in Deep Learning
Handwritten Digit Recognition using Convolutional Neural Networks
Traffic Signs Recognition for Intelligent Vehicles
Fraud Detection with Autoencoders
Text Generation using Recurrent Neural Networks
Sentiment Analysis with Word Embedding
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Index
Customer Reviews