Getting Started with Neural Nets in R. With the help of this course you can Build and train neural network models to solve complex problems.
This course was created by Packt Publishing. It was rated 4.3 out of 5 by approx 2153 ratings. There are approx 22738 users enrolled with this course, so don’t wait to download yours now. This course also includes 2.5 hours on-demand video, 1 Downloadable Resource, Full lifetime access, Access on mobile and TV & Certificate of Completion.
What Will You Learn?
Dive into building Neural Nets from Scratch
Set up R packages for neural networks and deep learning
Understand the core concepts of artificial neural networks
Work with neurons, perceptron, bias, weights, and activation functions
Implement supervised and unsupervised machine learning in R for neural networks
Predict and classify data automatically using neural networks
Evaluate and fine-tune the models you build.
Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve a wide range of problems in different areas of AI and machine learning.
This course explains the niche aspects of neural networking and provides you with a foundation from which to get started with advanced topics by implementing them in R. This course covers an introduction to neural nets, the R language, and building neural nets from scratch- with R packages; specific worked models are applied to practical problems such as image recognition, pattern recognition, and recommender systems. At the end of the course, you will learn to implement neural network models in your applications with the help of practical examples from companies using neural nets.
About the Author
Arun Krishnaswamy has over 18 years of experience with large datasets, statistical methods, machine learning and software systems. He is one of the First Hadoop Engineers in the world, Advisor to AI Startups. He has 15+ years’ experience using R. He is also a Ph.D. in Statistics/Math with MS in CS. Expertise in Machine Learning, Neural Nets, Deep Learning. Deep Experience in AWS, Spark, Cassandra, MongoDB, SQL, NoSQL, Tableau, R, Visualization. Data Science Mentor at UC Berkeley, Stanford, Caltech.Guest Lecturer at Community Colleges. Data Science in different domains o Fintech (Lending Club), o Cybersecurity (VISA) o Advertising Technology (Yahoo / Microsoft) o Bot Technology (voicy .ai) o Retail (WRS) o IOT (GE) o ERP (SAP) o Health Care (Blue Cross).