Deep Learning with R [Video]

More Information
Learn
  • Learn the basics of Deep Learning and Artificial Neural Networks
  • Understand classification and probabilistic predictions with Single-hidden-layer Neural Networks
  • Increase your expertise by covering intermediate and advanced Artificial and Recurrent Neural Networks
  • Get to grips with Convolutional and Deep Belief Networks
  • Learn practical applications of Deep Learning
  • Learn about Feature Engineering and Multicore/Cluster Computing
About

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 tutorial will teach you how to leverage deep learning to make sense of your raw data by exploring various hidden layers of data. Each section 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 will start by understanding the basics of Deep Learning and Artificial neural Networks and move on to exploring advanced ANN’s and RNN’s. You will deep dive into Convolutional Neural Networks and Unsupervised Learning. You will also learn about the applications of Deep Learning in various fields and understand the practical implementations of Scalability, HPC and Feature Engineering.

Starting out at a basic level, users will be learning how to develop and implement Deep Learning algorithms using R in real world scenarios.

Style and Approach

This video lecture series simplifies otherwise incredibly dense topics with clear, concise explanations and reproducible, hands-on examples. No prior knowledge of deep learning is assumed, but learners gain intermediate proficiency by the end of the course.

Features
  • Explore and create intelligent systems using Deep learning techniques
  • Understand the usage of multiple applications like Natural Language Processing, Bioinformatics, Recommendation Engines, etc. where deep learning models are implemented
  • Get hands on with various Deep Learning scenarios and get mind blowing insights from your data
Course Length 4 hours 4 minutes
ISBN 9781786467416
Date Of Publication 27 Mar 2017

Authors

Vincenzo Lomonaco

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".

Find more the author at:
Website: http://vincenzolomonaco.com/
ContinuousAI: http://continuousai.com/