Understanding LSTM Networks -- colah's blog

🔥Edureka Tensorflow Training: https://www.edureka.co/ai-deep-learning-with-tensorflowThis Edureka LSTM Explained video will help you in understanding why we...

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

We are going to talk about text generation using LSTM with end-to-end examples and the related concepts as a quick revision.

In this article, you are going to learn about the special type of Neural Network known as “Long Short Term Memory” or LSTMs. This article is divided into 4

Understanding architecture of LSTM cell from scratch with RNN.Video By - Deepranjan Kumar Gupta [email protected]

In forecasting spatially-determined phenomena (the weather, say, or the next frame in a movie), we want to model temporal evolution, ideally using recurrence relations. At the same time, we'd like to efficiently extract spatial features, something that is normally done with convolutional filters. Ideally then, we'd have at our disposal an architecture that is both recurrent and convolutional. In this post, we build a convolutional LSTM with torch.

Explore the architecture and applications of Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) units in machine learning.

I’m not better at explaining LSTM, I want to write this down as a way to remember it myself. I think the above blog post written by Christopher Olah is the best LSTM material you would find. Please…

Recurrent Neural Networks (RNN) differ from feedforward neural networks by the presence of feedback connections where the flow of information occurs between neurons of the same layer or from higher…

The main difference between RNN and LSTM is in terms of which one maintain information in the memory for the long period of time. Here LSTM has advantage over RNN as LSTM can handle the information in memory for the long period of time as compare to RNN. But the question is what is different…

The purpose of this tutorial is to help anybody write their first RNN LSTM model without much background in Artificial Neural Networks or Machine Learning. The discussion is not centered on the…

Time series analysis refers to the analysis of change in the trend of the data over a period of time. Time series analysis has a variety of applications. One s...

Given the standard illustrative feed-forward neural net model, with the dots as neurons and the lines as neuron-to-neuron connection, what part is the (unfold) LSTM cell (see picture)? Is it a neur...

A quick look at the different neural network architectures, their advantages and disadvantages.

The LSTM cell is one of the most interesting architecture on the Recurrent Neural Networks study field on Deep Learning: Not only it enables the model to learn from long sequences, but it also…

This article covers automatic Image Captioning. It also explains how to solve the image captioning problem using deep learning along with an implementation.

LSTM or long short term memory is a special type of RNN that solves traditional RNN's short term memory problem. In this video I will give a very simple expl...

Over the last few years, the utilization of LSTM-based models for the purposes of text classification has increased dramatically. From 2013 to 2020, there has been a massive push towards the field of…

Over the last years, recurrent neural networks in general and LSTM in particular, have become very popular among many data science practitioners. Despite the fact that the major application of LSTMs…