End to complete Multivariate Time frame Group Representing using LSTM

#datascience #deeplearning #LSTM

Entire Time Series Course – https://www.youtube.com/playlist?list=PL3N9eeOlCrP5cK0QRQxeJd6GrQvhAtpBK

In this video we will see how we can build a multi variate time series model using Deep learning LSTM sequence model. We will see end to end time series model building process in this video
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Python: Real-time Automated Long Short Term Memory (LSTM) Short-term Load Forecasting & Plotting

TABLE OF CONTENT
Introduction 00:00:00
Introduction of LSTM 00:00:52
Introduction of RNN 00:13:03
From RNN to LSTM 00:22:56
How to build a LSTM 00:31:41
* How to build a Neural Network https://www.youtube.com/watch?v=UJ38TToVJgM
Programming Exercise 00:42:59
Details of short-term load forecasting problem 00:43:02
Python
– Data Preparation 00:44:00
– Developing LSTM 01:03:57
– Real-time Model Prediction 01:18:19
– Real-time Plotting 1:28:10

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Multivariate Time Series Prediction with LSTM and Multiple features (Predict Google Stock Price)

Build a Artificial Neural Network (ANN) with Long-Short Term Memory (LSTM) to predict value which can be impacted by multiple different features.

In this video I demonstrate how to use LSTM to predict Google Stock price (you can use any other case) by taking into consideration multiple predictors (features). Let’s say, the final stock price can be predicted by finding importance of such features as historical low price, high price, volume, adj. price, etc.

Link to github notebook: https://github.com/vb100/multivariate-lstm/blob/master/LSTM_model_stocks.ipynb

The video has 3 parts:
– Part 01. Data pre-processing (4:11)
| Step 01: Read data.
| Step 02: Data pre-processing (shaping and transformations).

– Part 02. Create a LSTM model and train it. (10:39)
| Step 03: Building-up the LSTM based Neural Network.
| Step 04: Start training.

– Part 03. Make future predictions. (13:50)
| Step 05: Make predictions for future date.
| Step 06: Visualize the predictions.

In this tutorial I used Tensorflow 1.15.0 and Keras 2.3.1

Download data from: https://finance.yahoo.com/quote/GOOG/history (check 1:59 in video).

This is real life Python code example for demonstration purposes, so the model is not very accuracy and of course could be improved or tuned.

My goal of this Python tutorial is to demonstrate how to perform LSTM predictions with multiple features (complex dataset).

Hoping it will help to undersant the way it could be implemented in real Data Science or Data Analysis projects. TIme Series forecasting with LSTM is the good choice if you want to manipulate with multiple different data features and see which ones has impact to predictions and which ones do not.

If you are interested how to run Tensorboard on this LSTM Keras model, check this tutorial: https://youtu.be/-9-Hy5dWKLE

Sorry for video quality. There were some unexpected issues with resolution.
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Keras Tutorial #4 that in fact LSTM Content Era

This video is about building a model that can generate text using Keras. We are using an LSTM network to generate the text.

Please subscribe. That would make me happy and encourage me to keep making my content better and better.

The code for this video:
https://github.com/TannerGilbert/Tutorials/blob/master/Keras-Tutorials/4.%20LSTM%20Text%20Generation/Keras%20LSTM%20Text%20Generation.ipynb

Twitter: https://twitter.com/Tanner__Gilbert
Github: https://github.com/TannerGilbert
Website: https://gilberttanner.com/
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Time series data, in today’s age, is ubiquitous. With the emerge of sensors, IOT devices it is spanning over all the modern aspects of life from basic household devices to self-driving cars affecting all for lives. Thus classification of time series is of unique importance in current time. With the advent of deep learning techniques , there have been influx of focus on Recurrent Neural Nets (RNN) in solving tasks related with sequence and rightly so. In this talk, I would attempt to describe the reason for success of RNN’s in sequence data. Eventually we would divert towards other techniques which should be looked into when working on such problems. I will phrase examples from healthcare domain and delve into some of the other usefull techniques that can be used from Deep Learning Domain and their usefullness.

Aditya Patel is the head of data science at Stasis and has 7+ years of experience spanning over the fields of Machine Learning and Signal Processing. He graduated with Dual Master’s degree in Biomedical and Electrical Engineering from University of Southern California. He has presented his work in Machine learning at multiple peer reviewed conferences concerning healthcare domain, across the geography. He also contributed to first generation “Artificial Pancreas” project in Medtronic, Los Angeles. In his current role he is leading the advent of smart hospitals in Indian healthcare.
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Continual Neural Networks like twitter (LSTM / RNN) Application by having Keras is that Python

#RNN #LSTM #RecurrentNeuralNetworks #Keras #Python #DeepLearning

In this tutorial, we implement Recurrent Neural Networks with LSTM as example with keras and Tensorflow backend. The same procedure can be followed for a Simple RNN.

We implement Multi layer RNN, visualize the convergence and results. We then implement for variable sized inputs.

Recurrent Neural Networks RNN / LSTM / GRU are a very popular type of Neural Networks which captures features from time series or sequential data. It has amazing results with text and even Image Captioning.

In this example we try to predict the next digit given a sequence of digits. Same concept can be extended to text images and even music.

Find the codes here
GitHub : https://github.com/shreyans29/thesemicolon
Good Reads : http://karpathy.github.io/

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