Classification Example with Keras CNN (Conv1D) model in Python

   The convolutional layer learns local patterns of given data in convolutional neural networks. It helps to extract the features of input data to provide the output. In this tutorial, you'll learn how to implement a convolutional layer to classify the Iris dataset in a simple way. We'll use the Conv1D layer of Keras API. The tutorial covers:

1. Preparing the data
2. Defining and fitting the model
3. Predicting and accuracy check
4. Source code listing

We'll start by loading the required libraries for this tutorial.

from keras.models import Sequential
from keras.layers import Dense, Conv1D, Flatten, MaxPooling1D
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.datasets import load_iris
from numpy import unique

Multi-output Sequential Data Prediction with Keras RNN model in R

   Recurrent neural networks are used to analyze sequential data. It creates the recurrent connection between the hidden units and predicts the output after learning the sequence.

   In this tutorial, we'll briefly learn how to fit and predict multi-output sequential data with the Keras RNN model in R. You can apply the same method for time-series data too. We'll use Keras R interface to implement Keras neural network API in R. The tutorial covers:

1. Preparing the data
2. Defining the model
3. Predicting and visualizing the result
4. Source code listing

We 'll start by loading the required packages of R.

library(keras)
library(caret)

Multi-output Regression Example with Keras LSTM Network in R

   This tutorial is about how to fit and predict the multi-output regression data with LSTM Network in R. As you may already know, the LSTM ( Long Short-Term Memory) network is a type of recurrent neural network and used to analyze the sequence data. We'll use Keras R interface to implement keras neural network API in R. The tutorial covers:

1. Preparing the data
2. Defining the model
3. Predicting and visualizing the result
4. Source code listing

We 'll start by loading the required packages of R.

library(keras)
library(caret)

How to Fit Regression Data with CNN Model in R

   CNN (Convolutional Neural Networks) models are mainly useful when we apply them for training a multi-dimensional type of data such as an image. But they are not limited to this purpose only, we can also implement the CNN model for regression data analysis. We saw the CNN model regression with Python in the previous post and in this tutorial, we'll implement the same method in R.
   We use a 1-dimensional convolutional function to apply the CNN model. We need Keras R interface to use the Keras neural network API in R. You need to install it if it is not available on your development environment. The tutorial covers:

1. Preparing the data
2. Defining and fitting the model
3. Predicting and visualizing the results
4. Source code listing

We'll start by loading the required libraries for this tutorial.

library(keras)
library(caret)

Multi-output Regression Example with Keras Sequential Model in R

   We saw a multi-output regression prediction with Python in the previous post. The same analysis can be done with R too. In this tutorial, we'll learn how to fit and predict multi-output regression data with keras neural networks API in R. We can use Keras R interface to implement keras neural network API in R.
   Multi-output data contains more than one output for a given input data. By setting the appropriate input and output dimensions into the model, we can train and predict the test data with keras deep learning API in R. This tutorial explains how to implement it in the following steps:

1. Preparing the data
2. Defining the model
3. Predicting and visualizing the result
4. Source code listing

We'll start by loading the required packages of R.

library(keras)
library(caret)

Multi-output Multi-step Regression Example with Keras SimpleRNN in Python

   In previous posts, we saw the multi-output regression data analysis with CNN and LSTM methods. In this tutorial, we'll learn how to implement multi-output and multi-step regression data with Keras SimpleRNN class in Python. This method can be applied to time-series data too. Multi-output data contains more than one output value for a given dataset. To predict data we'll use multiple steps to train the output data. The tutorial covers:

1. Preparing the data
2. Defining and fitting the model
3. Predicting and visualizing the results
4. Source code listing

We'll start by loading the required libraries of Python and Keras API for this tutorial.

from keras.models import Sequential
from keras.layers import Dense, SimpleRNN
from numpy import array, sqrt, array
from numpy.random import uniform
from numpy import hstack
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error