# Lstm underfitting jobs

I am interested to develop a tool which can predict stock prices with an accuracy >80%.
There are some ideas out e.g. **LSTM**, MLP classifier incl. sentiment analysis (Twitter, Google, Facebook, Newspaper) etc.
What I am looking for is an Python developer which creates this tool together with me, do proper testing of the different possibilities (deep learning

Hi, I am looking for senior Machine Learning developer for my project.
We use **LSTM** or RNN to build the models.
I have a vietnamese customer review dataset. We will work on this dataset.
Please contact with me for more information.
Thank you for your attention!

...experience of Classifier model. Prediction of the action category output. ( 0, 1, 2)
Have you created Tensorflow models before?
Have you experience with sequence to sequence. **LSTM** or HMM models... these approaches may have stronger results.
Do you think this is something you can acheive?
> 75% on accuracy?
please see SIDDeval.... this is the accuracy

I'm trying to sense a spectrum using **LSTM** (machine learning )( a time series prediction problem) technique. I need to add a block to the transmitter and receiver on the GNU Radio using Python(as the machine learning model code, I already have is in python) .I need someone to embed the code properly to make a block in GNU Radio and that's it.

I need someone to embed the code properly to make a block in GNU Radio and help me .

I tried both regular **LSTM** and hybrid **LSTM** for election twitter datasets,both of which gave 70% scores. In literature Reinforced **LSTM** Deep Learning is stated as much more successful candidate;

I'm trying to sense a spectrum using **LSTM** (machine learning )( a time series prediction problem) technique. I need to add a block to the transmitter and receiver on the GNU Radio using Python(as the machine learning model code, I already have is in python) .I need someone to embed the code properly to make a block in GNU Radio and that's it.

I have a small dataset (5 text files) and need to run a simple Keras model on them.

...classifying accelerometer data into various road conditions. I have a labeled dataset for various road conditions. I am presently using **LSTM** to perform the time series classification
Working Code:
I have a working **LSTM** classification code which runs perfectly on a different dataset.
# The code directory name is
--Description of the working project

Looking for experienced data scientist having history in stock prediction
We prefer Tensorflow for modelling and training.
We expect your experienced model for stock predic...in stock prediction
We prefer Tensorflow for modelling and training.
We expect your experienced model for stock prediction.
My favorite model is Multi Linear Regression and **LSTM**.

Need help in explaining a code which has the following Python, RNN, **LSTM**, Time Series data, Pytorch

I have a basic Keras/tensorflow **LSTM** model, for some time series data.
Fairly simple, I need someone to:
1. export the model to Google ML Engine. I can give you my model in Juypter or you can create your own example.
2. create a Cloud function to call and return a prediction from this (eg given last 5 data points, what will happen next). There are a

Looking for experienced data scientist having history in stock prediction
We prefer Tensorflow for modelling and training.
We expect your experienced model for stock predic...in stock prediction
We prefer Tensorflow for modelling and training.
We expect your experienced model for stock prediction.
My favorite model is Multi Linear Regression and **LSTM**.

I want to build an **lstm** model to predict price fluctuation based on twitter comments.
Comments are grouped for each hour and prices are collected for each hour. Accuracy of the model must be good.
Besides the model we need to extract those terms which are affecting any price changes for each hour
Programming language needed to be used in Python

I am looking for an expert, even a Ph.D. student in ML to help me with a classification/**LSTM** problem
The model is now able to predict one step ahead, but I want it to predict more than one step ahead.

Hello, I have a **LSTM** code implemented in R/tensorflow and need it to provide a prediction for a new datapoint completely outside the dataset. Unsure of the final line of code needed to make it happen. Can you help?

Hello, I have a **LSTM** code implemented in R/tensorflow and need it to provide a prediction for a new datapoint completely outside the dataset. Unsure of the final line of code needed to make it happen. Can you help?

Hello all, I have a sample deep learning (keras/**LSTM**) code that applies to time series data. I need it to forecast/predict T+ 1,2, or 3 periods forward from whatever data has been provided (note that an optimal model is trained, validated and tested).
Need an experienced R coder to add this potentially single line of code in the code for me.

Hello all, I have a sample deep learning (keras/**LSTM**) code that applies to time series data. I need it to forecast/predict T+ 1,2, or 3 periods forward from whatever data has been provided (note that an optimal model is trained, validated and tested).
Need an experienced R coder to add this potentially single line of code in the code for me (budget

Hi. I've a Deep Learning/Machine Learning Project.
If you can train a **LSTM**/RNN model in python for classification of images, Please message for details.

The project is almost finished. All I need is forecasting function, something is wrong with the script. I'll send the .ipynb after the discussion.

perform RNN (recurrent neural network) on dataset in python using **LSTM** pr GRU

**LSTM** model is already created with good errors.
Already written in python.
**
All it needs a good forecasting function. (According to the error ) We will discuss it.
**
You can find the data below in the attachment Section. Feel free to look into it!

I'm getting "nan" as loss value while training my **LSTM** model. Im have a dataset of 3648 rows.
Number of **LSTM** layer : 4
Number of neurons in one **LSTM** : 50
Dropout : 0.2
Optimizer : Adam
Loss : mean_squared_error
Epochs : 100
batch size : 32. Please find attached the source code and the dataset.

Building 3 simple models and using the mean absolute percentage error (MAPE) compare models and visualize prediction prices and accuracy. Goal - Predict bitcoin price using 3 models from above. - Aggregate timestamp to 10 min. - Predict every next 10 min. - Visualize prediction between 1 SEP 2018 - 30 SEP 2018 Requirement - Using Jupyter Notebook (.ipynb) - Python (Keras) Neural Network should n...

...parametrs:
- optimizer (nag is default) i need to run "adam" - trainign works but transaltion gives errors
- arch (fconv is default) i need to run: fconv_lm, transformer_lm, **lstm** - training gives error, transformer - trains but wrongly transaltes some random text repeated all the time.
I changed all in accordance to [login to view URL]

i am a researcher , need to implement deep learning algorithm based Convolutional Neural Network (CNN) and Long Short Term Memory (**LSTM**) to detect changes between two images

**LSTM** Hybrid with 2 D RNN
**LSTM** Hybrid with 2 D Recurrent Neural Network

**LSTM** Hybrid with 2 D RNN
**LSTM** Hybrid with 2 D Recurrent Neural Network

...the Notebook is about:
**LSTM**-stocks-prediction: Contains a **LSTM** model written from scratch in Tensorflow - this illustrates how **LSTM** cells work - it is my opinion that someone more versed than me will easily be able to convert this model to a DGM.
**LSTM**-Neural-Network-for-Time-Series-Prediction: Contains a more sophisticated **LSTM** model
PDE_DGM: Contains

Hello, I need a function written in c++ (qt, ubuntu) that takes input 2 vector<double> and outputs which one of the 2 vectors is behind (lagging) and by how much. The solution has to implement reinforcement-learning (RT) techniques (Q-Learning?) on a RNN (recurent neural net) using your choice library (propose, explain why the choice). The NeuralNetwork will be "default" trained ...

...the Notebook is about:
**LSTM**-stocks-prediction: Contains a **LSTM** model written from scratch in Tensorflow - this illustrates how **LSTM** cells work - it is my opinion that someone more versed than me will easily be able to convert this model to a DGM.
**LSTM**-Neural-Network-for-Time-Series-Prediction: Contains a more sophisticated **LSTM** model
PDE_DGM: Contains

To Build an **LSTM** model based on a document term matrix provided by my end. To be done in tensorflow and briefly explained

I am surveying on a topic Software Cost Estimations with Machine Learning models. And I came to know that **LSTM** can be used to predict time series/schedule for a software project from an open dataset. Can anyone please explain to me how this can be achieved?

I need someone to train an **LSTM** model for me for crypto market price predication. The model should be able to predict the price/trend of the price in the next x time (e.g. 30 minutes) using the data for the past y time (e.g. 6 hours). Getting a probability of price movement would be nice as well. The model should be multivariant and be able to accept

Feature selection via [login to view URL],
Then compare all machine learning algorithms like NB, SVM, ANN with **LSTM** performance.
All implementations must be coded in Phyton via Keras platform

We are running a sales prediction model by external variables, such as weather and etc. Currently, we are using Time Series, XG Boost Model and **LSTM** - RNN Models. We only know how to run the model from api on amazon aws sagemaker (Python). But we are lacking of experience on tunning the model and selection of correct algorithms. Right now, we need someone

Feature selection via word2vect tool; [login to view URL],
Then experiments with **lstm** nw structure to find the most enhanced version

Currently, we are building an RNN(**LSTM**) Model to predict sales. But model is not working well, and we are trying to add modules to improve the forecast accuracy. We need an expert on machine learning to give us some advices. We prefer to talk with you over the phone directly. Thank you!

...knowledge in machine learning to help me set up an algorithm for stock price prediction and predict if a stock will go Up or Down.
Technology:
Python using Sklearn module, RNN, **LSTM** or similar ( Preferred )
Experience using hyper parameters - like Adam Optimizer.
H2o (automl) ( [login to view URL] )
I will

...looking for an expert who has some deep knowledge in machine learning to help me set up an algorithm for stock price prediction.
Technology:
Python using Sklearn module, RNN, **LSTM** or similar ( Preferred )
I will deliver :
Sample Code to Download Data
Technical Indicators Code Calculation.
The person should be able:
Setup whole machine learning

...looking for an expert who has some deep knowledge in machine learning to help me set up an algorithm for stock price prediction.
Technology:
Python using Sklearn module, RNN, **LSTM** or similar ( Preferred )
I will deliver :
Sample Code to Download Data
Technical Indicators Code Calculation.
The person should be able:
Setup whole machine learning

I am getting an error in keras for a stacked **lstm** model.I am attaching the file below please see the error once before bidding once accepted you will have to work with me on teamviewer to resolve the error.

Hybrid **Lstm** with feature selection is achieved by SVM/ANN

...Classification. The main description can be found in this StackExchange Post actually: [login to view URL] You can also find a previous version of it in this Jupyter Notebook: [login to view URL] If you can point me towards

...but they don't seem to work either).
I expect the problem to be easy for someone more or less experienced, and so would be really really thankful if the final code had an **LSTM** architecture instead of the normal one I'm using now. I have some other Deep Learning projects I need help with also, so if you can nail what I'm doing wrong here, be sure that

Needs:
* Do another version of my work with AT-**LSTM** (I already used **LSTM** with 3 layers)
Use the existing data prep.
* Do a report to compare both approach (my existing model and the new one).
-- Code done with python (Keras lib)
-- I have datasets (train - test set) and my actuel accuracy is 67%
-- I'm using Jupyter notebook.