R Deep Learning Projects: Master the techniques to design and develop neural network models in R

• Master the different deep learning paradigms and build real-world projects related to text generation, sentiment analysis, fraud detection, and more
• Get to grips with R's impressive range of Deep Learning libraries and frameworks such as deepnet, MXNetR, Tensorflow, H2O, Keras, and text2vec
• Practical projects that show you how to implement different neural networks with helpful tips, tricks, and best practices

• Instrument Deep Learning models with packages such as deepnet, MXNetR, Tensorflow, H2O, Keras, and text2vec
• Apply neural networks to perform handwritten digit recognition using MXNet
• Get the knack of CNN models, Neural Network API, Keras, and TensorFlow for traffic sign classification -Implement credit card fraud detection with Autoencoders
• Master reconstructing images using variational autoencoders
• Wade through sentiment analysis from movie reviews
• Run from past to future and vice versa with bidirectional Long Short-Term Memory (LSTM) networks
• Understand the applications of Autoencoder Neural Networks in clustering and dimensionality reduction

Machine learning professionals and data scientists looking to master deep learning by implementing practical projects in R will find this book a useful resource. A knowledge of R programming and the basic concepts of deep learning is required to get the best out of this book.