Advanced Deep Learning with R
This book will help readers to apply deep learning algorithms in R using advanced examples. You will cover variants of neural network models such as ANN, CNN, RNN, LSTM, and more using expert techniques. Readers will make use of popular deep learning libraries such as Keras-R, Tensorflow-R, and more to implement AI models.
Offered by
Difficulty Level
Intermediate
Completion Time
11h44m
Language
English
About Book
Who Is This Book For?
This book is for data scientists, machine learning practitioners, deep learning researchers and AI enthusiasts who want to develop their skills and knowledge to implement deep learning techniques and algorithms using the power of R. A solid understanding of machine learning and working knowledge of the R programming language are required.
Advanced Deep Learning with R
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 11h44m total length
Revisiting Deep Learning architecture and techniques
Deep Neural Networks for multiclass classification
Deep Neural Networks for regression
Image classification and recognition
Image classification using convolutional neural networks
Applying Autoencoder neural networks using Keras
Image classification for small data using transfer learning
Creating new images using generative adversarial networks
Deep network for text classification
Text classification using recurrent neural networks
Text classification using Long Short-Term Memory Network
Text classification using convolutional recurrent networks
Tips, tricks and the road ahead
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