Seq2seq Product on Time-series Data: Exercising and training and Having by using TensorFlow that in fact Masood Krohy

Masood Krohy at April 9, 2019 event of montrealml.dev

Title: Seq2seq Model on Time-series Data: Training and Serving with TensorFlow

Summary: Seq2seq models are a class of Deep Learning models that have provided state-of-the-art solutions to language problems recently. They also perform very well on numerical, time-series data which is of particular interest in finance and IoT, among others. In this hands-on demo/code walkthrough, we explain the model development and optimization with TensorFlow (its low-level API). We then serve the model with TensorFlow Serving and show how to write a client to communicate with TF Serving over the network and use/plot the received predictions.

Code on GitHub: https://github.com/patternedscience/time-series-tf-serving

Bio: Masood Krohy is a Data Science Platform Architect/Advisor and most recently acted as the Chief Architect of UniAnalytica, an advanced data science platform with wide, out-of-the-box support for time-series and geospatial use cases. He has worked with several corporations in different industries in the past few years to design, implement and productionize Deep Learning and Big Data products. He holds a Ph.D. in computer engineering.

This video is a production of PatternedScience Inc.
Website: https://www.patterned.science
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TensorFlow 2(two).0 Class for Beginners fifteen how to Google Financial estimates Prediction Using RNN that LSTM

Download the working file: https://github.com/laxmimerit/Google-Stock-Price-Prediction-Using-RNN—LSTM

Recurrent Neural Networks can Memorize/remember previous inputs in-memory When a huge set of Sequential data is given to it.

These loops make recurrent neural networks seem kind of mysterious. However, if you think a bit more, it turns out that they aren’t all that different than a normal neural network. A recurrent neural network can be thought of as multiple copies of the same network, each passing a message to a successor.

Different types of Recurrent Neural Networks.

Image Classification
Sequence output (e.g. image captioning takes an image and outputs a sentence of words).
Sequence input (e.g. sentiment analysis where a given sentence is classified as expressing a positive or negative sentiment).
Sequence input and sequence output (e.g. Machine Translation: an RNN reads a sentence in English and then outputs a sentence in French).
Synced sequence input and output (e.g. video classification where we wish to label each frame of the video)

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Coding LSTM which have Keras and TensorFlow (12.2 or more)

Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU) are two layer types commonly used to build recurrent neural networks in Keras. This video introduces these two network types as a foundation towards Natural Language Processing (NLP) and time series prediction.

Code for This Video:
https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_10_2_lstm.ipynb
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In this lesson, you will learn a multi-step time series prediction using RNN LSTM for household power consumption prediction. We will predict the power consumption of the coming week based on the power consumption of past weeks.

Download the working file: https://github.com/laxmimerit/Multi-Step-Time-Series-Prediction-using-RNN-LSTM-for-household-power-consumption

Learn Complete Data Science with these 5 video series.
1. Python for Beginners

2. Machine Learning for Beginners

3. Feature Selection in Machine Learning

4. Deep Learning with TensorFlow 2.0 and Keras

5. Natural Language Processing (NLP) Tutorials
https://www.youtube.com/watch?v=eqG34mp-R3A&list=PLc2rvfiptPSQgsORc7iuv7UxhbRJox-pW

The working code is given in the video description of each video. You can download the Jupyter notebook from GitHub.

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Valuable time Group Speculation with TensorFlow | Figures Opinion New york '19

Time Series Prediction with TensorFlow | Data Council SF '19

Download Slides: https://www.datacouncil.ai/talks/time-series-prediction-with-tensorflow

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ABOUT THE TALK

RNNs and LSTMs have enjoyed great success in text generation algorithms, but their use in other fields has not been as widely studied. We will discuss our experiences and progress using Recurrent Neural Networks to make predictions on arbitrary multivariate time series data. Our first study used weather data from the JFK terminal over several years using the TensorFlow framework. We will discuss the issues related to tuning and validating this model, as well as how we migrated this model into the Model Asset Exchange, which is an IBM hosted API for making predictions on data using pre-trained neural network models.

Our insight into tuning this model allowed us to provide another API via Watson Machine Learning, which is a hosted service that allows user defined data and models to be uploaded, trained, and tuned on GPU accelerated on demand hardware using simple remote API calls. We will discuss examples from the financial sector, weather prediction, and other important time series prediction use cases.

ABOUT THE SPEAKER

Jerome Nilmeier is a Data Scientist and Developer Advocate at the IBM Center for Open Source Data and Artificial Intelligence Technology (CODAIT). His duties include enablement and advocacy for IBM clients and the community at large, which includes teaching, community outreach, consulting and technical support for open source AI projects which includes Apache Spark, Tensorflow and others.Jerome has been with IBM as a data scientist since 2015. He has a BS in Chemical Engineering from UC Berkeley, a PhD in Computational Biophysics from UC San Francisco, and has carried out postdoctoral research in biophysics and bioinformatics at UC Berkeley, Lawrence Berkeley and Livermore Laboratories, and at Stanford as an OpenMM Fellow. Just prior to joining IBM, he completed the Insight Data Engineering program in late 2014.

Cryptocurrency-predicting RNN release that Intense Learning w/ Python, TensorFlow and Keras p.8

Welcome to part 8 of the Deep Learning with Python, Keras, and Tensorflow series. In this tutorial, we’re going to work on using a recurrent neural network to predict against a time-series dataset, which is going to be cryptocurrency prices.

Text tutorials and sample code: https://pythonprogramming.net/cryptocurrency-recurrent-neural-network-deep-learning-python-tensorflow-keras/

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The benefits of using Tensorflow promptly Group (Real time)

We’re going to use Tensorflow to predict the next event in a time series dataset. This can be applied to any kind of sequential data.

Code for this video:
https://github.com/llSourcell/rnn_tutorial

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More learning resources:
https://github.com/tgjeon/TensorFlow-Tutorials-for-Time-Series
https://cloud.google.com/solutions/machine-learning-with-financial-time-series-data
https://www.reddit.com/r/MachineLearning/comments/4ervmf/tensorflow_rnn_time_series_prediction/
https://danijar.com/introduction-to-recurrent-networks-in-tensorflow/
http://nbviewer.jupyter.org/github/jsseely/tensorflow-rnn-tutorial/blob/master/TensorFlow%20RNN%20tutorial.ipynb

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