Hands-on Deep Learning with R
Deep learning enables efficient and accurate learning from a massive amount of data. This book will help you solve a number of challenges using various deep learning algorithms and architectures with R.
The book starts with a brief overview of machine learning and deep learning and how to build your first neural network. You will understand the architecture of various deep learning algorithms and their applicable fields, learn how to build deep learning models, optimize hyperparameters and evaluate model performance. You will even cover various deep learning applications in image processing, natural language processing (NLP), recommendation systems, and predictive analytics. Later chapters will show you how to tackle recognition problems such as image recognition and signal detection, programmatically summarize documents, conduct topic modeling and forecast stock market prices. Toward the end of the book, you will learn the common applications of generative adversarial networks (GANs) and how to build a face generation model using them. Finally, you’ll get to grips with using reinforcement learning and deep reinforcement learning to solve various real-world problems.
By the end of this book, you will be able to build and deploy your own deep learning applications using appropriate deep learning frameworks and algorithms.
|Course Length||6 hours 52 minutes|
|Date Of Publication||15 May 2020|