I was able to undersand the basic idea behind RNN after working through an example from the book by Antonio Gulli.

I was puzzled with the way LSTMs were used to do sentiment analysis. Finally the book by Antonio Gulli helped me understand the mechanics of the LSTM.

In this article, I will explain the way you can code a simple RNN that tracks a simple shift in the pattern, i.e a value from a normal distribution. As compared to previous implementations where we had used OutputProjectionWrapper, this code does away with that component and does it more efficiently Create Training and Validation Data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 import numpy as np import re from sklearn.

In this article, I will explain the way you can code a simple RNN that tracks a simple shift in the pattern Create Training and Validation Data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 import numpy as np import re from sklearn.model_selection import train_test_split import tensorflow as tf input_seed = 1234 time_steps = 24 n_samples = 100000 X = np.random.randint(1,30,n_samples*time_steps).reshape(n_samples, time_steps) Y = np.

In this article, I will explain the way you can code a simple RNN that tracks cumulative sums of a sequence Create Training and Validation Data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 import numpy as np import re from sklearn.model_selection import train_test_split import tensorflow as tf input_seed = 1234 time_steps = 24 n_samples = 100000 X = np.random.randint(1,30,n_samples*time_steps).reshape(n_samples, time_steps) Y = np.