🔵 Intellipaat Artificial Intelligence Master's Course: https://intellipaat.com/artificial-intelligence-masters-training-course/🔵 In this video on Keras, ...
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Learn the Features of Keras that make it worth to learn keras like backend support, the basis of innovation, and the simplicity of Keras API etc.
Learn Python keras applications - Learn when, wherre and how to use keras for you various applications using keras pretrained models.
Learn what is Keras and OpenCV with their applications. See Keras vs OpenCv to understand differences between OpenCv and keras for proper understanding.
Turning all the intricacies of Attention to one elegant line in Keras
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Deep convolutional generative adversarial networks The deep convolutional generative adversarial networks (DCGAN) are introduced in the paper: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, by … - Selection from Deep Learning with Keras [Book]
The aim of this tutorial is to show the use of TensorFlow with KERAS for classification and prediction in Time Series Analysis. The latter just implement a Long Short Term Memory (LSTM) model (an instance of a Recurrent Neural Network which avoids the vanishing gradient problem). Introduction The code below has the aim to quick introduce Deep Learning analysis with TensorFlow using the Keras back-end in R environment. Keras is a high-level neural networks API, developed with a focus on enabling fast experimentation and not for final products. Keras and in particular the keras R package allows to perform computations using also the GPU if the installation environment allows for it. Installing KERAS and TensorFlow in Windows … otherwise it will be more simple Install Anaconda: https://www.anaconda.com/download/ Install Rtools(34): https://cran.r-project.org/bin/windows/Rtools/ GPU-TensorFlow To use the option GPU-TensorFlow, you need CUDA Toolkit that matches the version of your GCC compiler: https://developer.nvidia.com/cuda-toolkit If you have Python (i.e. Anaconda) just install.packages("keras") library(keras) install_keras() and this will install the Google Tensorflow module in Python. If you want it working on GPU and you have a suitable CUDA version, you can install it with tensorflow = "gpu" option install_keras(tensorflow = "gpu") Simple check library(keras) to_categorical(0:3) ## [,1] [,2] [,3] [,4] ## [1,] 1 0 0 0 ## [2,] 0 1 0 0 ## [3,] 0 0 1 0 ## [4,] 0 0 0 1 Background on Neural Networks Example old faithful IRIS data Consider the well-known IRIS data set rm(list=ls()) data(iris) plot(iris$Petal.Length, iris$Petal.Width, col = iris$Species) head(iris) ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## 1 5.1 3.5 1.4 0.2 setosa ## 2 4.9 3.0 1.4 0.2 setosa ## 3 4.7 3.2 1.3 0.2 setosa ## 4 4.6 3.1 1.5 0.2 setosa ## 5 5.0 3.6 1.4 0.2 setosa ## 6 5.4 3.9 1.7 0.4 setosa We want to build an iris specie classifier based on the observed four iris dimensions. This is the usual classification (prediction) problem so we have to consider a training sample and evaluate the classifier on a test sample. Data in TensorFlow Data are Matrices ```matrix´´´ of doubles. Categorical variables need to be codified in dummies: one hot encoding. onehot.species = to_categorical(as.numeric(iris$Species) - 1) iris = as.matrix(iris[, 1:4]) iris = cbind(iris, onehot.species) Training and Test Data Sets Define training and test set.seed(17) ind
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