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.

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

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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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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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LSTM Signals – The mathematics of Information (Long weekend 8)

Recurrent Networks can be improved to remember long range dependencies by using whats called a Long-Short Term Memory (LSTM) Cell. Let’s build one using just numpy! I’ll go over the cell components as well as the forward and backward pass logic.

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

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More learning resources:


Recurrent Neural Networks Tutorial, Part 1 – Introduction to RNNs


https://iamtrask.github.io/2015/11/15/anyone-can-code-lstm/

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