English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 25m | 422 MB
Build and train neural network models to solve complex problems
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.
The course is a step-by-step guide to understanding Neural Networks with R; throughout the course, practical, real-world examples help you get acquainted with the various concepts of Neural Networks.
What You Will Learn
In this course we will:
- 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.
Table of Contents
The Course Overview
Introduction to Neural Nets
Neural Nets Components
Matrices and Neural Nets
Forward and Backward Propagation
MNIST Example
Why Neural Nets from Scratch?
Regression and Softmax Concepts
NN Demo
Customer Churn Data
Neural Nets Demo
Build a 3 Layer MLP with Keras
Visualize Neural Networks
Movie Review Data
RNN
RNN/LSTM Demo
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