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Deep Learning and NLP A-Z™: How to create a ChatBot

Learn the Theory and How to implement state of the art Deep Natural Language Processing models in Tensorflow and Python

Instructor:

Hadelin de Ponteves

12,960 students enrolled

English [Auto-generated] More

Why this is important

Types of Natural Language Processing

Classical vs. Deep Learning Models

End to End Deep Learning Models

Seq2Seq Architecture & Training

Beam Search Decoding

We’ve talked about, speculated and often seen different applications for Artificial Intelligence – But what about one piece of technology that will not only gather relevant information, better customer service and could even differentiate your business from the crowd?

ChatBots are here, and they came change and shape-shift how we’ve been conducting online business. Fortunately technology has advanced enough to make this a valuable tool something accessible that almost anybody can learn how to implement.

If you want to learn one of the most attractive, customizable and cutting edge pieces of technology available, then this course is just for you!

Welcome to the course!

Get Excited!

Applications

BONUS: Learning Paths

Some Additional Resources!!

This PDF resource will help you a lot!

Deep NLP Intuition

What You'll Need For This Module

Updates on Udemy Reviews

Plan of Attack

Types of Natural Language Processing

Classical vs Deep Learning Models

End-to-end Deep Learning Models

Bag-of-words model

Seq2Seq Architecture (Part 1)

Seq2Seq Architecture (Part 2)

Seq2Seq Training

Beam Search Decoding

Attention Mechanisms (Part 1)

Attention Mechanisms (Part 2)

Building a ChatBot with Deep NLP

ChatBot - Step 1

ChatBot - Step 2

ChatBot - Step 3

---------- PART 1 - DATA PREPROCESSING ----------

Welcome to Part 1 - Data Preprocessing

ChatBot - Step 4

ChatBot - Step 5

ChatBot - Step 6

ChatBot - Step 7

ChatBot - Step 8

ChatBot - Step 9

ChatBot - Step 10

ChatBot - Step 11

ChatBot - Step 12

ChatBot - Step 13

ChatBot - Step 14

ChatBot - Step 15

ChatBot - Step 16

ChatBot - Step 17

Checkpoint!

---------- PART 2 - BUILDING THE SEQ2SEQ MODEL ----------

What You'll Need For This Module

Welcome to Part 2 - Building the Seq2Seq Model

ChatBot - Step 18

The TensorFlow placeholder function:

https://www.tensorflow.org/api_docs/python/tf/placeholder

ChatBot - Step 19

Most important tools used:

https://www.tensorflow.org/api_docs/python/tf/fill

https://www.tensorflow.org/api_docs/python/tf/strided_slice

https://www.tensorflow.org/api_docs/python/tf/concat

ChatBot - Step 20

Most important tools used:

https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/BasicLSTMCell

https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/DropoutWrapper

https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/MultiRNNCell

https://www.tensorflow.org/api_docs/python/tf/nn/bidirectional_dynamic_rnn

ChatBot - Step 21

Most important tools used:

https://www.tensorflow.org/programmers_guide/embedding

https://www.tensorflow.org/api_docs/python/tf/variable_scope

https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/prepare_attention

https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/attention_decoder_fn_train

https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/dynamic_rnn_decoder

https://www.tensorflow.org/api_docs/python/tf/nn/dropout

ChatBot - Step 22

Most important tools used:

https://www.tensorflow.org/programmers_guide/embedding

http://web.stanford.edu/class/cs20si/lectures/notes_04.pdf

https://www.tensorflow.org/api_docs/python/tf/variable_scope

https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/prepare_attention

https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/attention_decoder_fn_inference

https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/dynamic_rnn_decoder

https://www.tensorflow.org/api_docs/python/tf/nn/dropout

ChatBot - Step 23

Most important tools used:

https://www.tensorflow.org/api_docs/python/tf/variable_scope

https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/BasicLSTMCell

https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/DropoutWrapper

https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/MultiRNNCell

https://www.tensorflow.org/api_docs/python/tf/truncated_normal_initializer

https://www.tensorflow.org/api_docs/python/tf/zeros_initializer

https://www.tensorflow.org/api_docs/python/tf/contrib/layers/fully_connected

ChatBot - Step 24

Most important tools used:

https://www.tensorflow.org/api_docs/python/tf/contrib/layers/embed_sequence

https://www.tensorflow.org/api_docs/python/tf/Variable

https://www.tensorflow.org/api_docs/python/tf/random_uniform

https://www.tensorflow.org/api_docs/python/tf/nn/embedding_lookup

Checkpoint!

---------- PART 3 - TRAINING THE SEQ2SEQ MODEL ----------

What You'll Need For This Module

Welcome to Part 3 - Training the Seq2Seq Model

ChatBot - Step 25

Geoffrey Hinton's paper:

https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf

ChatBot - Step 26

Most important tools used:

https://www.tensorflow.org/api_docs/python/tf/reset_default_graph

https://www.tensorflow.org/api_docs/python/tf/InteractiveSession

ChatBot - Step 27

ChatBot - Step 28

The TensorFlow placeholder_with_default function:

https://www.tensorflow.org/versions/r0.12/api_docs/python/io_ops/placeholders#placeholder_with_default

ChatBot - Step 29

The TensorFlow shape function:

https://www.tensorflow.org/api_docs/python/tf/shape

ChatBot - Step 30

The TensorFlow reverse function:

https://www.tensorflow.org/api_docs/python/tf/reverse

https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.reshape.html

ChatBot - Step 31

Most important tools used:

https://www.tensorflow.org/api_docs/python/tf/name_scope

https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/sequence_loss

https://www.tensorflow.org/api_docs/python/tf/ones

https://www.tensorflow.org/api_docs/python/tf/train/AdamOptimizer

https://www.tensorflow.org/versions/r0.12/api_docs/python/train/gradient_clipping

https://www.tensorflow.org/api_docs/python/tf/clip_by_value

ChatBot - Step 32

ChatBot - Step 33

Difference between return and yield:

http://www.geeksforgeeks.org/use-yield-keyword-instead-return-keyword-python/

ChatBot - Step 34

ChatBot - Step 35

Most important tools used:

https://www.tensorflow.org/api_docs/python/tf/global_variables_initializer

https://pyformat.info/

ChatBot - Step 36

Checkpoint!

---------- PART 4 - TESTING THE SEQ2SEQ MODEL ----------

What You'll Need For This Module

Welcome to Part 4 - Testing the Seq2Seq Model

ChatBot - Step 37

Most important tools used:

https://www.tensorflow.org/api_docs/python/tf/InteractiveSession

https://www.tensorflow.org/api_docs/python/tf/global_variables_initializer

https://www.tensorflow.org/api_docs/python/tf/train/Saver

ChatBot - Step 38

ChatBot - Step 39

ChatBot - Step 40

Checkpoint!

Training the ChatBot on Google Colab with GPU

Learn how to run the Chatbot in a Google Colab notebook with GPU training!

---------- PART 5 - IMPROVING & TUNING THE SEQ2SEQ MODEL ----------

ChatBot - Step 41: Improving & Tuning the ChatBot

ChatBot - Step 42: Introduction to a new model & setup

ChatBot - Step 43: Chatbot model discussion

ChatBot - Step 44: Tensorboard

ChatBot - Step 45: Run the new chatbot model

Other ChatBot Implementations

What You'll Need For This Module

The Best ChatBot

Intuition and Code resources for The Best ChatBot:

http://suriyadeepan.github.io/2016-06-28-easy-seq2seq/

http://suriyadeepan.github.io/2016-12-31-practical-seq2seq/

https://github.com/suriyadeepan/practical_seq2seq

A ChatBot Implementation in TensorFlow 1.4

A ChatBot Implementation in PyTorch

THANK YOU bonus video

Annex 1: Artificial Neural Networks

Plan of Attack

The Neuron

The Activation Function

How do Neural Networks work?

How do Neural Networks learn?

Gradient Descent

Stochastic Gradient Descent

Backpropagation

Annex 2: Recurrent Neural Networks

Plan of Attack

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!

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Deep Learning and NLP A-Z™: How to create a ChatBot

Welcome to the course!

Deep NLP Intuition

Building a ChatBot with Deep NLP

---------- PART 1 - DATA PREPROCESSING ----------

---------- PART 2 - BUILDING THE SEQ2SEQ MODEL ----------

---------- PART 3 - TRAINING THE SEQ2SEQ MODEL ----------

---------- PART 4 - TESTING THE SEQ2SEQ MODEL ----------

---------- PART 5 - IMPROVING & TUNING THE SEQ2SEQ MODEL ----------

Other ChatBot Implementations

Annex 1: Artificial Neural Networks

Annex 2: Recurrent Neural Networks

How long do I have access to the course materials?

Can I take my courses with me wherever I go?

Detailed Rating

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