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Deep Learning with R for Beginners. Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet

Deep Learning with R for Beginners. Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet

Mark Hodnett, Joshua F. Wiley, Yuxi (Hayden) Liu, Pablo Maldonado

E-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.
  • 1. Getting Started with Deep Learning
  • 2. Training a Prediction Model
  • 3. Deep Learning Fundamentals
  • 4. Training Deep Prediction Models
  • 5. Image Classification Using Convolutional Neural Networks
  • 6. Tuning and Optimizing Models
  • 7. Natural Language Processing Using Deep Learning
  • 8. Deep Learning Models Using TensorFlow in R
  • 9. Anomaly Detection and Recommendation Systems
  • 10. Running Deep Learning Models in the Cloud
  • 11. The Next Level in Deep Learning
  • 12. Handwritten Digit Recognition Using Convolutional Neural Networks
  • 13. Traffic Sign Recognition for Intelligent Vehicles
  • 14. Fraud Detection with Autoencoders
  • 15. Text Generation Using Recurrent Neural Networks
  • 16. Sentiment Analysis with Word Embeddings
  • Titel: Deep Learning with R for Beginners. Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet
  • Autor: Mark Hodnett, Joshua F. Wiley, Yuxi (Hayden) Liu, Pablo Maldonado
  • Originaler Titel: Deep Learning with R for Beginners. Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet
  • ISBN: 9781838642709, 9781838642709
  • Veröffentlichungsdatum: 2019-05-20
  • Format: E-book
  • Artikelkennung: e_2aus
  • Verleger: Packt Publishing