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Deep Learning and NLP A-Z™: How to create a ChatBot
4.2 (3,405 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
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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
Bestseller
4.2 (3,405 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
21,743 students enrolled
Created by Hadelin de Ponteves, Kirill Eremenko, SuperDataScience Team, Zillion Hand Team
Last updated 8/2020
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Deep Learning and NLP A-Z™: How to create a ChatBot
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This course includes
  • 11.5 hours on-demand video
  • 19 articles
  • 8 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • 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
Course content
Expand all 96 lectures 12:09:06
+ – Welcome to the course!
6 lectures 14:32
Get Excited!
Preview 05:12
Applications
Preview 05:56
BONUS: Learning Paths
00:33
Some Additional Resources!!
00:14
This PDF resource will help you a lot!
00:32
Your Shortcut To Becoming A Better Data Scientist!
02:05
+ – Deep NLP Intuition
13 lectures 02:04:55
What You'll Need For This Module
00:10
Updates on Udemy Reviews
01:09
Plan of Attack
04:00
Types of Natural Language Processing
Preview 04:11
Classical vs Deep Learning Models
11:22
End-to-end Deep Learning Models
15:33
Bag-of-words model
Preview 17:05
Seq2Seq Architecture (Part 1)
12:11
Seq2Seq Architecture (Part 2)
11:57
Seq2Seq Training
11:27
Beam Search Decoding
09:33
Attention Mechanisms (Part 1)
15:59
Attention Mechanisms (Part 2)
10:18
+ – Building a ChatBot with Deep NLP
3 lectures 23:41
ChatBot - Step 1
05:07
ChatBot - Step 2
10:42
ChatBot - Step 3
07:52
+ – ---------- PART 1 - DATA PREPROCESSING ----------
16 lectures 01:54:22
Welcome to Part 1 - Data Preprocessing
00:23
ChatBot - Step 4
02:37
ChatBot - Step 5
05:23
ChatBot - Step 6
08:42
ChatBot - Step 7
10:28
ChatBot - Step 8
10:08
ChatBot - Step 9
09:00
ChatBot - Step 10
05:54
ChatBot - Step 11
06:40
ChatBot - Step 12
11:26
ChatBot - Step 13
06:55
ChatBot - Step 14
05:00
ChatBot - Step 15
05:13
ChatBot - Step 16
09:57
ChatBot - Step 17
14:42
Checkpoint!
01:54
+ – ---------- PART 2 - BUILDING THE SEQ2SEQ MODEL ----------
10 lectures 01:59:56
What You'll Need For This Module
00:10
Welcome to Part 2 - Building the Seq2Seq Model
00:23

The TensorFlow placeholder function:

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

ChatBot - Step 18
08:51

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 19
14:13

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 20
16:00

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 21
20:27

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 22
17:41

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 23
19:37

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

ChatBot - Step 24
19:18
Checkpoint!
03:16
+ – ---------- PART 3 - TRAINING THE SEQ2SEQ MODEL ----------
15 lectures 01:44:46
What You'll Need For This Module
00:10
Welcome to Part 3 - Training the Seq2Seq Model
00:23

Geoffrey Hinton's paper:

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

ChatBot - Step 25
11:16

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 26
01:47
ChatBot - Step 27
02:00

The TensorFlow placeholder_with_default function:

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

ChatBot - Step 28
03:32

The TensorFlow shape function:

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

ChatBot - Step 29
02:42

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 30
07:10

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 31
12:56
ChatBot - Step 32
09:24

Difference between return and yield:

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

ChatBot - Step 33
12:41
ChatBot - Step 34
07:16

Most important tools used:

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

https://pyformat.info/

ChatBot - Step 35
20:34
ChatBot - Step 36
07:56
Checkpoint!
04:59
+ – ---------- PART 4 - TESTING THE SEQ2SEQ MODEL ----------
8 lectures 37:37
What You'll Need For This Module
00:10
Welcome to Part 4 - Testing the Seq2Seq Model
00:23

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 37
04:35
ChatBot - Step 38
03:13
ChatBot - Step 39
15:08
ChatBot - Step 40
07:21
Checkpoint!
05:36

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

Training the ChatBot on Google Colab with GPU
01:11
+ – ---------- PART 5 - IMPROVING & TUNING THE SEQ2SEQ MODEL ----------
5 lectures 20:10
ChatBot - Step 41: Improving & Tuning the ChatBot
00:53
ChatBot - Step 42: Introduction to a new model & setup
03:12
ChatBot - Step 43: Chatbot model discussion
07:58
ChatBot - Step 44: Tensorboard
04:05
ChatBot - Step 45: Run the new chatbot model
04:02
+ – Other ChatBot Implementations
5 lectures 19:25
What You'll Need For This Module
00:10

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

The Best ChatBot
16:08
A ChatBot Implementation in TensorFlow 1.4
00:10
A ChatBot Implementation in PyTorch
00:16
THANK YOU bonus video
02:40
+ – Annex 1: Artificial Neural Networks
8 lectures 01:17:37
Plan of Attack
02:51
The Neuron
16:15
The Activation Function
08:29
How do Neural Networks work?
12:47
How do Neural Networks learn?
12:58
Gradient Descent
10:12
Stochastic Gradient Descent
08:44
Backpropagation
05:21
Requirements
  • Just some high school mathematics level
  • Basic Python programming knowledge
Description

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!

Who this course is for:
  • Any students in college who want to start a career in Data Science
  • Any Data Science enthusiast
  • Anyone interested in creating their own ChatBot
  • Anyone interested in Artificial Intelligence, Machine Learning or Deep Learning and its applications
Featured review
Mohammad Ali Aghabeigiha
Mohammad Ali Aghabeigiha ( 9 courses, 3 reviews )
a year ago
These guys are awesome... I only have 1 thing to add, hope my comment will be seen by them.. I wish there was more correlation between intuition and the practical example, for example, when you explain the gradient descent method in intuition, it would be great during the coding section when you choose optimizer = 'adam' , just give a very quick flashback to intuition. I'm not saying you don't do, but I am asking to kindly do it more... love you guys, ali