This document provides an introduction and overview of natural language processing with TensorFlow. It discusses representing text as tensors, representing words with embeddings, capturing patterns with recurrent neural networks, and generating text with recurrent networks. The key steps of natural language processing are also summarized: tokenization, sequencing text into numerical representations, and training models like recurrent neural networks and long short-term memory networks to recognize sentiment in text.

1. Introduction to
Natural Language
Processing
with
TensorFlow

2. What will you need ?
● Basic Knowledge of Python
● Basic Machine Learning Understanding
● Basic Maths

3. pip install Tensorflow
pip install numpy
pip install pandas
pip install matplotlib
python3 -c "import tensorflow as tf;
print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
Cmd :

4. Today’s Agenda
● Introduction to natural language processing with TensorFlow
● Representing text as Tensors
● Represent words with embeddings
● Capture patterns with recurrent neural networks
● Generate text with recurrent networks

6. What is TensorFlow ?
Tensorflow is an open source machine
learning framework for all developers which is
use for implementing machine learning and
deep learning application.

8. Tensor
way of representing data in deeplearning.ng

9. Basic data structure of TensorFlow
Rank Shape Type
A rank of a tensor
is the order or
n-dimensions of a
tensor defined
Number of
columns and rows
together define the
shape of the
Tensor.
Type describes
the data type
assigned to
Tesnor’s
elements.

10. WHAT IS NEURAL NETWORk??

11. Weight
equation
Activation
function
Input layer
NEURONS
Output
layer
Hidden
layer
Y= ax1+bx2+....nxn

13. What is Natural Language Processing?
Branch of computer science and more specifically
the branch of AI concerned with giving
computers the ability to understand text or
spoken words in much the same way human
beings can.

14. CONVERTING LETTERS INTO THE ASCII VALUES
L I S T E N
S I L E N T
001 005 006
003 002 001 005 006 004
003 004
002

15. STEP 1 TOKENIZATION
A tokenizer breaks unstructured data and natural language text into
chunks of information that can be considered as discrete elements.
The token occurrences in a document can be used directly as a vector
representing that document.
The process of Numbering / encoding the letters is called Tokenization

17. STEP 2 SEQUENCING
Padding : Makes all the words of same length by appending 0
at the msb
Passing set of sentences to the ‘text_to_sequence()’ method
converts the sentences to their labelle equivalent base on the
corpus of words passe to it.
Turning sentences into data

18. STEP 3 Training the model to recognize the sentiment
in the text
Recurrent Neural Network
Long Short term memory

19. Recurrent Neural Network
Shradha loves watching POKEMON
1 0 0 1
shradha
shradha loves
loves Watching
watching
Pokemon

20. Recurrent Neural Network

22. Exception

24. Long Short Term Memory (LSTM)
In these type of networks ,in addition to the context being passed like
an RNN .
The LSTM have an additional pipeline of context called Cell State which
passes through the network