LEARNING PATH: R: Advanced Deep Learning with R

Deep learning is the next big thing. It’s a part of machine learning. Its favorable results in applications with huge and complex data is remarkable. R programming language is very popular among data miners and statisticians. Deep learning refers to artificial neural networks that are composed of many layers. Deep learning is a powerful set of techniques for finding accurate information from raw data.
This comprehensive 2-in-1 course will help you explore and create intelligent systems using deep learning techniques. You’ll understand the usage of multiple applications like Natural Language Processing, bioinformatics, recommendation engines, etc. where deep learning models are implemented. You’ll get hands on with various deep learning scenarios and get mind blowing insights from your data. You’ll be able to master the intricacies of R deep learning packages such as TensorFlow. You’ll also learn deep learning in different domains using practical examples from text, image, and speech.
Contents and Overview
This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.
The first course, Deep Learning with R, covers videos that will teach you how to leverage deep learning to make sense of your raw data by exploring various hidden layers of data. Each video in this course provides a clear and concise introduction of a key topic, one or more example of implementations of these concepts in R, and guidance for additional learning, exploration, and application of the skills learned therein. 
You’ll start by understanding the basics of deep learning and artificial neural networks and move on to exploring advanced ANN’s and RNN’s. You’ll dive deep into convolutional neural networks and unsupervised learning. You’ll also learn about the applications of deep learning in various fields and understand the practical implementations of Scalability, HPC and Feature Engineering.
Finally, starting out at a basic level, you’ll be learning how to develop and implement deep learning algorithms using R in real world scenarios.
The second course, R Deep Learning Solutions, covers powerful, independent videos to build deep learning models in different application areas using R libraries. It will help you resolve problems during the execution of different tasks in deep learning, neural networks, and advanced machine learning techniques. 
You’ll start with different packages in deep learning, neural networks, and structures. You’ll also encounter the applications in text mining and processing along with a comparison between CPU and GPU performance. Finally, you’ll explore complex deep learning algorithms and various deep learning packages and libraries in R.
By the end of this training program you’ll be able to to develop and implement deep learning algorithms using R in real world scenarios and have an understanding of different deep learning packages so you’ll have the most appropriate solutions for your problems.
About the Authors
Vincenzo Lomonaco is a Deep Learning PhD student at the University of Bologna and founder of (ContinuousAI).com an open source project aiming to connect people and reorganize resources in the context of Continuous Learning and AI. He is also the PhD students’ representative at the Department of Computer Science of Engineering (DISI) and teaching assistant of the courses “Machine Learning” and “Computer Architectures” in the same department. Previously, he was a Machine Learning software engineer at IDL in-line Devices and a Master Student at the University of Bologna where he graduated cum laude in 2015 with the dissertation “Deep Learning for Computer Vision: A comparison between CNNs and HTMs on object recognition tasks”.
Dr. PKS Prakash is a data scientist and an author. He has spent last the 12 years developing many data science solutions to solve problems from leading companies in the healthcare, manufacturing, pharmaceutical, and e-commerce domains. He currently works as data science manager at ZS Associates.  Prakash has a PhD in Industrial and System Engineering from Wisconsin-Madison, U.S. He gained his second PhD in Engineering at the University of Warwick, UK. He has a master’s degree from University of Wisconsin-Madison, U.S., and a bachelor’s degree from National Institute of Foundry and Forge Technology (NIFFT), India. He is co-founder of Warwick Analytics, which is based on his PhD work from the University of Warwick, UK. Prakash has been published widely in research areas of operational research and management, soft computing tools, and advanced algorithms in leading journals such as IEEE-Trans, EJOR, and IJPR among others. He edited an issue on “Intelligent Approaches to Complex Systems” and contributed to books such as Evolutionary Computing in Advanced Manufacturing published by Wiley and Algorithms and Data Structures using R published by Packt Publishing.
Achyutuni Sri Krishna Rao is a data scientist, a civil engineer, and an author. He has spent the last four years developing many data science solutions to solve problems from leading companies in the healthcare, pharmaceutical, and manufacturing domains. He currently works as a data science consultant at ZS Associates. Sri Krishna’s background is a master’s in Enterprise Business Analytics and Machine Learning from the National University of Singapore, Singapore. He also has a bachelor’s degree from the National Institute of Technology Warangal, India.  Sri Krishna has been published widely in the research areas of civil engineering. He contributed to the book Algorithms and Data Structures using R published by Packt Publishing.