diff --git a/extras/min-char-rnn/RNNCell_min_char_rnn.py b/extras/min-char-rnn/RNNCell_min_char_rnn.py
new file mode 100644
index 0000000..1a5ae41
--- /dev/null
+++ b/extras/min-char-rnn/RNNCell_min_char_rnn.py
@@ -0,0 +1,158 @@
+"""
+Adapted from Karpathy's min-char-rnn.py
+https://gist.github.com/karpathy/d4dee566867f8291f086
+Requires tensorflow>=1.0
+BSD License
+
+Modified by: Marianne Linhares (@mari-linhares)
+"""
+
+import numpy as np
+import tensorflow as tf
+from tensorflow.contrib import layers
+
+print(tf.__version__)
+
+# data I/O
+
+# should be simple plain text file
+data = open('about_tensorflow.txt', 'r').read()
+chars = list(set(data))
+
+DATA_SIZE, VOCAB_SIZE = len(data), len(chars)
+print('data has %d characters, %d unique.' % (DATA_SIZE, VOCAB_SIZE))
+
+char_to_ix = {ch: i for i, ch in enumerate(chars)}
+ix_to_char = {i: ch for i, ch in enumerate(chars)}
+
+# hyperparameters
+HIDDEN_SIZE = 100    # hidden layer's size
+SEQ_LENGTH = 25      # number of steps to unroll
+LEARNING_RATE = 0.1  # size of step for Gradient Descent
+BATCH_SIZE = 1       # number of sequences evaluated at each train step
+
+# TensorFlow graph definition
+
+# Placeholders
+inputs = tf.placeholder(shape=[None, None], dtype=tf.int32, name='inputs')
+targets = tf.placeholder(shape=[None, None], dtype=tf.int32, name='targets')
+init_state = tf.placeholder(shape=[None, HIDDEN_SIZE], dtype=tf.float32,
+                            name='state')
+
+# RNN
+
+# [ BATCH_SIZE, SEQ_LEN, VOCAB_SIZE ]
+input_x = tf.one_hot(inputs, VOCAB_SIZE)
+input_y = tf.one_hot(targets, VOCAB_SIZE)
+
+# creating a RNN cell
+rnn_cell = tf.nn.rnn_cell.BasicRNNCell(HIDDEN_SIZE)
+# you can use a GRUCell by changing one line of code
+'''
+rnn_cell = tf.nn.rnn_cell.GRUCell(HIDDEN_SIZE)
+'''
+
+# run RNN
+# rnn_outputs: [BATCH_SIZE, SEQ_LEN, HIDDEN_SIZE]
+# final_state: [BATCH_SIZE, HIDDEN_SIZE]
+rnn_outputs, final_state = tf.nn.dynamic_rnn(rnn_cell, input_x,
+                                             initial_state=init_state,
+                                             dtype=tf.float32)
+
+# add dense layer on top of the RNN outputs
+rnn_outputs_flat = tf.reshape(rnn_outputs, [-1, HIDDEN_SIZE])
+dense_layer = layers.linear(rnn_outputs_flat, VOCAB_SIZE)
+labels = tf.reshape(input_y, [-1, VOCAB_SIZE])
+output_softmax = tf.nn.softmax(dense_layer)
+
+# Loss
+loss = tf.reduce_sum(
+    tf.nn.softmax_cross_entropy_with_logits(logits=dense_layer,
+                                            labels=labels))
+
+# Minimizer
+optimizer = tf.train.AdagradOptimizer(learning_rate=LEARNING_RATE)
+minimizer = optimizer.minimize(loss)
+
+# Gradient clipping
+# In this code we're not using gradient clipping, just to make things simpler
+# but if you want to use it, the code below implements it.
+'''
+grads_and_vars = minimizer.compute_gradients(loss)
+
+grad_clipping = tf.constant(5.0, name='grad_clipping')
+clipped_grads_and_vars = []
+for grad, var in grads_and_vars:
+    clipped_grad = tf.clip_by_value(grad, -grad_clipping, grad_clipping)
+    clipped_grads_and_vars.append((clipped_grad, var))
+
+updates = minimizer.apply_gradients(clipped_grads_and_vars)
+'''
+# Create session and initialize variables
+sess = tf.Session()
+init = tf.global_variables_initializer()
+sess.run(init)
+
+# Training
+
+# n: counts the number of steps, p: pointer to position in sequence
+n, p = 0, 0
+
+# this var will be used for sampling
+hprev_val = np.zeros([1, HIDDEN_SIZE])
+
+# loss at iteration 0
+smooth_loss = -np.log(1.0 / VOCAB_SIZE) * SEQ_LENGTH
+
+while True:
+  # prepare inputs (we're sweeping from left to right in steps SEQ_LENGTH long)
+  if p + SEQ_LENGTH + 1 >= len(data) or n == 0:
+    hprev_val = np.zeros([BATCH_SIZE, HIDDEN_SIZE])  # reset RNN memory
+    p = 0  # go from start of data
+
+  start_index = p
+  end_index = p + SEQ_LENGTH
+
+  input_vals = [char_to_ix[ch] for ch in data[start_index: end_index]]
+  input_vals = np.reshape(input_vals, (-1, SEQ_LENGTH))
+
+  target_vals = [char_to_ix[ch] for ch in data[start_index + 1: end_index+ 1]]
+  target_vals = np.reshape(target_vals, (-1, SEQ_LENGTH))
+
+  # sampling
+  if n % 1000 == 0:
+    sample_length = 200
+    hprev_sample = np.copy(hprev_val)
+
+    # start from the first letter from the input
+    first_char = char_to_ix[data[p]]
+    x = np.asarray([[first_char]])
+    # stores predictions
+    sample_ix = []
+
+    for t in range(sample_length):
+      pred, hprev_sample = sess.run([output_softmax, final_state],
+                                    feed_dict={inputs: x,
+                                               init_state: hprev_sample})
+
+      # generates next letter
+      ix = np.random.choice(range(VOCAB_SIZE), p=pred.ravel())
+      # update next char with the prediction
+      x = np.asarray([[ix]])
+      sample_ix.append(ix)
+
+    txt = ''.join(ix_to_char[ix] for ix in sample_ix)
+    print('----\n%s\n----' % (txt))
+
+  # running the graph
+  hprev_val, loss_val, _ = sess.run([final_state, loss, minimizer],
+                                    feed_dict={inputs: input_vals,
+                                               targets: target_vals,
+                                               init_state: hprev_val})
+
+  # print progress
+  smooth_loss = smooth_loss * 0.999 + loss_val * 0.001
+  if n % 100 == 0: print('iter %d, loss: %f' % (n, smooth_loss))
+
+  p += SEQ_LENGTH
+  n += 1
diff --git a/extras/min-char-rnn/about_tensorflow.txt b/extras/min-char-rnn/about_tensorflow.txt
new file mode 100644
index 0000000..e5deb27
--- /dev/null
+++ b/extras/min-char-rnn/about_tensorflow.txt
@@ -0,0 +1 @@
+tensorflow is an open source software library for numerical computation using data flow graphs nodes in the graph represent mathematical operations while the graph edges represent the multidimensional data arrays (tensors) communicated between them the flexible architecture allows you to deploy computation to one or more cpus or gpus in a desktop server or mobile device with a single api tensorflow was originally developed by researchers and engineers working on the google brain team within googles machine intelligence research organization for the purposes of conducting machine learning and deep neural networks research but the system is general enough to be applicable in a wide variety of other domains as well