R Deep Learning: Mastering Neural Networks and Heuristics

Name: R Deep Learning: Mastering Neural Networks and Heuristics
Rating: 4.6 (4 reviews)

From neural networks to advanced heuristics, master practical applications for data analysis and predictive modeling.

Created byEDUCBA Bridging the Gap

Last updated 4/2024

English

What you'll learn

Understand the fundamentals of deep learning and neural networks. Learn how to manipulate datasets and create dataframes in R.
Gain proficiency in running neural network code and generating outputs. Explore advanced techniques such as heuristics for data analysis.
Master descriptive statistics generation and linear regression modeling.
Apply deep learning principles to analyze datasets related to cryptocurrencies, energy sectors, and financial markets.
Develop practical skills in data manipulation, visualization, and interpretation.
Gain insights into complex relationships between variables and make informed decisions based on data analysis.
Build confidence in leveraging R programming for deep learning applications in various domains.

Course content

2 sections • 37 lectures • 6h 10m total length

Reviewing Dataset14:04
Explore building a single-layer perceptron in R on a wheat production dataset, and master data preparation from csv loading to numeric conversion and data frame creation.
Creating Dataframes9:22
Create data frames and generate neural network outputs using a single-layer perceptron, exporting results as jpeg images, while comparing steps, errors, and data type handling for Safari and pistachios datasets.
Generating Output11:43
Build and train a simple neural network with one hidden layer, assign the net, compute the sum of squared error, and export plots as PNG images.
Running Neural Network Code10:59
Run neural network code in R by importing data, setting the working directory, and building mlp networks for date fruit and pistachios using neural network packages, then compare outputs.
Importing Dataset9:02
Import the pistachio dataset, set the working directory, and import data while preparing numeric features such as eccentricity, solidity, roundness, and compactness for neural networks.
Neural Network Plots for Hidden Layer 18:17
Generate plots for hidden layer 1 outputs, run and refine code, and troubleshoot weight calculations to visualize solidity, eccentricity, and compactness in saved images.
Syntax and Commands for MLP11:21
Explore the syntax and commands of the multi-layer perceptron, including hidden layers and step max, and learn how to prepare and inspect data frames for neural networks in R.
Running the Code8:00
Install and run the neural network package, build a three-hidden-layer model for ABC data in a data frame, and generate, test, and interpret weights and outputs.
Testing for Dataframes13:21
Build and test a data frame for a multilayer perceptron, generate predictions from the test set, and convert probabilities into binary results in R.
Predict Results8:19
Build and evaluate neural networks in R using x, y, z inputs, testing data frames, and predicting class probabilities across A, B, C and x, y, z datasets.
Creating R Folder13:49
Create an R folder and working directory, load data into a training data frame, configure a neural network with hidden layers and step max, and generate test predictions.
Generating Output Plot11:47
Explore generating output plots from neural networks, comparing logistic regression and multiple regression, examining probability thresholds, predicted values, and the impact of hidden layers on model outputs.
Testing and Predicting the Outputs15:58
Learn to test and predict neural network outputs in R by importing a CSV, building a data frame, configuring neuralnet with multiple hidden layers, and evaluating probabilities for classification.

Course Contents8:19
Explore deep learning for heuristics with r by building a project workspace, setting a working directory, importing the agriculture dataset, creating data frames, and generating descriptive outputs.
Creating Dataframes8:31
Create and save a data frame from a csv, assign date and pistachios variables, and run descriptives using mean and median while handling missing values.
Generating Descriptive11:18
Create a data subset in R to generate descriptive statistics, including means, minimum and maximum values, and standard deviations for numeric variables.
Generating Descriptive Continued9:46
Generate means, standard deviations, and medians for all variables, troubleshoot data issues, and export the descriptors outputs from the code.
Setting Directory and Environment10:37
Master deep learning heuristics by analyzing cryptocurrency price data, generating descriptors, computing Pearson correlations, and visualizing regression with a line of fit in R.
Assigning Variables9:16
Generate means, standard deviations, medians, and min and max values for four bitcoin variables, then compute the Pearson correlation matrix.
Syntax and Command Part 19:17
Generate scatter plots and a line of fit to analyze correlations in Bitcoin price data with regression. Save and export plots while applying standard heuristics in R.
Syntax and Command Part 29:53
Learn data preparation for neural networks by importing bits.csv, cleaning and converting data to numeric, and generating descriptive statistics and standard deviations for reliable model inputs.
Syntax and Command Part 37:29
Explore generating correlations across an entire data frame and visualize relationships with scatter plots, highlighting negative correlations between entropy and substratum and saving results.
Setting Directory and Environment-Cryptocurrencies10:37
Set up the working directory and environment in r, import cryptocurrency data, and generate descriptors, pearson correlation matrices, and scatter plots with a line of fit to illustrate regression modeling.
Spearman Techniques8:12
Explore spearman techniques to compute correlation coefficients for returns and price data, compare with pearson, and visualize relationships with scatter plots and line graphs.
Generating Line Graphs8:48
Generate line graphs to visualize cryptocurrency volatility and returns price data across dates, using x and y axes, and compute Pearson and Spearman correlations with scatterplots.
Generating Scatter Plots9:56
Generate and visualize two-dimensional scatter plots in R, plotting cortex versus emeralds, evaluate correlation with a line of fit, and explore multiple plots using pairs.
Generating Multiple Scatter Plots10:28
Generate multiple scatter plots from returns price data using x, y, z in a single data frame, and label cortex, emeralds, and helium with text boxes.
Understanding Regression Modeling Theory9:59
Learn to implement linear regression in R, generate and interpret estimates, and visualize results with scatter plots and regression lines using the energy sector dataset.
Implementing Linear Regression Modeling10:04
Implement linear regression in R by defining GDP as the dependent variable and nuclear fuel as the predictor, assign variables from the data frame, and run and summarize the model.
Syntax and Commands9:47
Explore building and interpreting a regression model linking nuclear fuel to GDP, including the equation form, intercept, coefficients, r-squared, significance, and scatter plots.
Generating Scatter Plots-Energy Sector9:06
Create scatter plots of nuclear fuel versus GDP with labeled axes and a line of fit, noting data points and correlation. Explore pairwise plots for variables in the energy sector.
Multiple Scatter Plots8:38
Examine the correlation matrix and multiple scatter plots to analyze GDP versus nuclear energy and solar's impact on per capita electricity, with a line of fit and regression insights.
Creating Dataframes-Financial Markets8:17
Learn to build a multiple regression model in R, using p/e and price-to-sales ratios as independent variables to predict market cap and 52-week high/low, with data frame setup.
Understanding Multiple9:01
Explore multiple linear regression in R by building a model predicting market cap with ratios as predictor variables. Interpret the intercept, coefficients, and r-squared to assess model fit.
Implementing Multiple Regression Model in R10:03
Explore implementing a multiple regression in R, predicting 52-week high from price and EPS, compare with ratios against market cap, and assess R-squared, coefficients, and collinearity.
Plot and Draw Line of Fit10:02
Explore how eps and price relate to the 52-week high using regression. Interpret the coefficients and r-squared, and generate scatter plots with lines of fit.
Multiple Scatter Plots in a Graphical Frame7:04
Explore generating multiple scatter plots in a single graphical frame to examine EPS, price, ratios, and 52-week high, then apply multiple regression and interpret the regression equation and r-squared.

Requirements

Basic understanding of programming concepts. Familiarity with the R programming language. Knowledge of fundamental statistical concepts. Understanding of data manipulation and analysis techniques.
Interest in deep learning and neural networks. Access to a computer with R installed for hands-on exercises and practice. Eagerness to learn and explore advanced topics in data science.

Description

Welcome to our comprehensive course on Deep Learning with R! This course is designed to provide you with a thorough understanding of deep learning principles and their practical implementation using the R programming language.

In this course, you will embark on a journey into the fascinating world of neural networks and heuristics, gaining the skills and knowledge necessary to leverage the power of deep learning for various applications. Whether you're a beginner or an experienced data scientist, this course offers something for everyone.

Section 1: Deep Learning: Neural Networks With R

In the first section, you will dive into the fundamentals of deep learning using neural networks. Starting with dataset review and dataframe creation, you'll learn how to manipulate data effectively for analysis. Through practical exercises, you'll gain hands-on experience in running neural network code and generating outputs from datasets. By the end of this section, you'll be equipped with the foundational skills needed to build and train neural networks using R.

Section 2: Deep Learning: Heuristics Using R

In the second section, you'll explore advanced techniques in deep learning, focusing on the application of heuristics using R. From descriptive statistics generation to linear regression modeling, you'll learn how to analyze datasets related to cryptocurrencies, energy sectors, and financial markets. Through a series of practical examples, you'll master the art of data manipulation and visualization, gaining insights into complex relationships between variables.

By the end of this course, you'll have a solid understanding of deep learning principles and the ability to apply them confidently in real-world scenarios using R. Whether you're interested in predictive modeling, pattern recognition, or data analysis, this course will empower you to unlock the full potential of deep learning with R. Let's dive in and explore the exciting world of neural networks and heuristics together!

Who this course is for:

Data Scientists: Professionals seeking to expand their skills in deep learning and neural networks using R for advanced data analysis and modeling.
Data Analysts: Individuals looking to enhance their expertise in data manipulation, visualization, and interpretation through practical applications of deep learning techniques.
Programmers: Those interested in leveraging R programming for deep learning applications across various domains, such as finance, healthcare, and technology.
Students: Graduates and undergraduates studying data science, computer science, or related fields who wish to deepen their understanding of deep learning principles and their practical implementation in R.
Professionals in Finance, Healthcare, and Technology: Individuals working in these industries who want to apply deep learning techniques to analyze complex datasets and make data-driven decisions.
Anyone with a keen interest in deep learning: Enthusiasts who are curious about exploring the capabilities of neural networks and heuristics for data analysis and predictive modeling using R.

R Deep Learning: Mastering Neural Networks and Heuristics

What you'll learn

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Course content

Deep Learning: Neural Networks With R13 lectures • 2hr 26min

Deep Learning: Heuristics Using R24 lectures • 3hr 44min

Requirements

Description

Who this course is for: