by Arun Krishnaswamy | Released June 2018 | ISBN: 9781788390385

Build and train neural network models to solve complex problemsAbout This VideoDevelop a strong background in neural networks with R, to implement them in your applicationsLearn how to build and train neural network models to solve complex problemsImplement solutions from scratch, covering real-world case studies to illustrate the power of neural network modelsIn DetailNeural 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. Show and hide more

1. Chapter 1 : Neural Nets in R
   • The Course Overview 00:01:51
   • Introduction to Neural Nets 00:08:07
   • Neural Nets Components 00:10:10
   • Matrices and Neural Nets 00:07:05
   • Forward and Backward Propagation 00:19:26
   • MNIST Example 00:20:52
2. Chapter 2 : Neural Nets from Scratch
   • Why Neural Nets from Scratch? 00:05:48
   • Regression and Softmax Concepts 00:09:13
   • NN Demo 00:09:37
3. Chapter 3 : Training and Visualizing a Neural Network in R
   • Customer Churn Data 00:10:47
   • Neural Nets Demo 00:08:09
   • Build a 3 Layer MLP with Keras 00:08:30
   • Visualize Neural Networks 00:08:44
4. Chapter 4 : Recurrent and Convolutional Neural Networks Using Data from Netflix
   • Movie Review Data 00:04:05
   • RNN 00:05:05
   • RNN/LSTM Demo 00:07:42
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