LSTM autoencoder is an encoder that is used to compress data using an encoder and decode it to retain original structure using a decoder.

The deep learning frameworks allows researchers and developers to achieve the state-of-art compactly and robustly.

Simple Neural Network is feed-forward wherein info information ventures just in one direction.i.e. the information passes from input layers to hidden layers finally to the output layers. Recurrent Neural Network is the advanced type to the traditional Neural Network. It makes use of sequential information. Unlike conventional networks, the output and input layers are dependent on each other. RNNs are called recurrent because they play out a similar undertaking for each component of an arrangement, with the yield being relied upon the past calculations.LSTM or Long Short Term Memory are a type of RNNs that is useful in learning order dependence in sequence prediction problems. In this article, we will cover a simple Long Short Term Memory autoencoder with the help of Keras and python. Introduction to LSTM Autoencoder using Keras

Now, to convert our TensorFlow model to the TensorFlow Lite model, we first need to build and train a TensorFlow model. Here, we will train our language translation model then finally we will convert our model to TensorFlow Lite so that we can utilize it for our mobile devices.

PyTorch for Deep Learning — LSTM for Sequence Data. Time-series data changes with time. In this article, we'll be using PyTorch to analyze time-series data and predict future values using deep learning.

Generating Short Star Wars Text With LSTM’s. “The ability to speak does not make you intelligent” — Qui-Gon Jinn, Jedi Master.

Do you want to try some other methods to solve your forecasting problem rather than traditional regression? There are many neural network architectures, which are frequently applied in NLP field, can be used for time series as well. In this article, we are going to build two Seq2Seq Models in Keras, the simple Seq2Seq LSTM Model, and the Seq2Seq LSTM Model with Luong Attention, and compare their forecasting accuracy. Building Seq2Seq LSTM with Luong Attention in Keras for Time Series Forecasting

In this post, I will explain long short-term memory (LSTM) networks. I find that the best way to learn a topic is to read many different explanations and so I will link some other resources I found particularly helpful, at the end of this article.

Controlling the Temperature Control Lab with an LSTM. Well, here’s the “so what” part. Here’s where we finally get to implement this LSTM neural network to emulate the behavior of the PID controller.

This article throws light on the performance of Long Short-Term Memory (LSTM) and Transformer networks. We’ll start with taking cognizance of information on LSTM ’s and Transformers and move on to internal mechanisms with which they work.

Training a Long Short-term Memory neural network in Keras to emulate a PID controller using the Temperature Control Lab

Here, we have listed the top 10 open-source projects on Recurrent Neural Networks (RNNs) that one must try their hands on.

Depth-Gated LSTM: From A to Z! Recurrent Neural Networks(RNN) suffer from short-term memory. This means that if there is a long sequence, an RNN will have a problem in carrying information from earlier time steps to later ones.

Emulating a PID Controller with Long Short-term Memory. Using the Temperature Control Lab to create Proportional-Integral-Derivative controller data

In this article, I’ll walk you through my experience to code a model that will learn some Ed Sheeran songs and try to create some first sentences for a song.

Pragmatic Deep Learning Model for Forex Forecasting. Using LSTM and TensorFlow on the GBPUSD Time Series for multi-step prediction

Building an algorithmic bot, in a commercial platform, to trade based on a model’s prediction. In this tutorial, you'll see How to Use a TensorFlow Deep Learning Model for Forex Trading

In this piece, we’ll see how we can prepare textual data using TensorFlow. Eventually, we’ll build a bidirectional long short term memory model to classify text data.

This article assumes a very basic conceptual familiarity with the concept of Neural Networks in general. If you spot something that’s inconsistent with your understanding, please feel free to drop a comment / correct me!

How to make a prediction with time-series data using machine learning. I made an LSTM neural network model that uses 30+ years of weather and streamflow data to quite accurately predict what the streamflow will be tomorrow.