English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 11.5 Hours | 5.93 GB
Learn the Theory and How to implement state of the art Deep Natural Language Processing models in Tensorflow and Python
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!
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
Table of Contents
Welcome to the course!
1 Get Excited!
2 Applications
3 Some Additional Resources!!
Deep NLP Intuition
4 What You’ll Need For This Module
5 Seq2Seq Training
6 Beam Search Decoding
7 Attention Mechanisms (Part 1)
8 Attention Mechanisms (Part 2)
9 Updates on Udemy Reviews
10 Plan of Attack
11 Types of Natural Language Processing
12 Classical vs Deep Learning Models
13 End-to-end Deep Learning Models
14 Bag-of-words model
15 Seq2Seq Architecture (Part 1)
16 Seq2Seq Architecture (Part 2)
Building a ChatBot with Deep NLP
17 ChatBot – Step 1
18 ChatBot – Step 2
19 ChatBot – Step 3
PART 1 – DATA PREPROCESSING ———-
20 Welcome to Part 1 – Data Preprocessing
21 ChatBot – Step 12
22 ChatBot – Step 13
23 ChatBot – Step 14
24 ChatBot – Step 15
25 ChatBot – Step 16
26 ChatBot – Step 17
27 Checkpoint!
28 ChatBot – Step 4
29 ChatBot – Step 5
30 ChatBot – Step 6
31 ChatBot – Step 7
32 ChatBot – Step 8
33 ChatBot – Step 9
34 ChatBot – Step 10
35 ChatBot – Step 11
PART 2 – BUILDING THE SEQ2SEQ MODEL ———-
36 What You’ll Need For This Module
37 Checkpoint!
38 Welcome to Part 2 – Building the Seq2Seq Model
39 ChatBot – Step 18
40 ChatBot – Step 19
41 ChatBot – Step 20
42 ChatBot – Step 21
43 ChatBot – Step 22
44 ChatBot – Step 23
45 ChatBot – Step 24
PART 3 – TRAINING THE SEQ2SEQ MODEL ———-
46 What You’ll Need For This Module
47 ChatBot – Step 32
48 ChatBot – Step 33
49 ChatBot – Step 34
50 ChatBot – Step 35
51 ChatBot – Step 36
52 Checkpoint!
53 Welcome to Part 3 – Training the Seq2Seq Model
54 ChatBot – Step 25
55 ChatBot – Step 26
56 ChatBot – Step 27
57 ChatBot – Step 28
58 ChatBot – Step 29
59 ChatBot – Step 30
60 ChatBot – Step 31
PART 4 – TESTING THE SEQ2SEQ MODEL ———-
61 What You’ll Need For This Module
62 Welcome to Part 4 – Testing the Seq2Seq Model
63 ChatBot – Step 37
64 ChatBot – Step 38
65 ChatBot – Step 39
66 ChatBot – Step 40
67 Checkpoint!
PART 5 – IMPROVING & TUNING THE SEQ2SEQ MODEL ———-
68 ChatBot – Step 41 Improving & Tuning the ChatBot
69 ChatBot – Step 42 Introduction to a new model & setup
70 ChatBot – Step 43 Chatbot model discussion
71 ChatBot – Step 44 Tensorboard
72 ChatBot – Step 45 Run the new chatbot model
Other ChatBot Implementations
73 What You’ll Need For This Module
74 The Best ChatBot
75 A ChatBot Implementation in TensorFlow 1.4
76 A ChatBot Implementation in PyTorch
Annex 1 Artificial Neural Networks
77 Plan of Attack
78 The Neuron
79 The Activation Function
80 How do Neural Networks work
81 How do Neural Networks learn
82 Gradient Descent
83 Stochastic Gradient Descent
84 Backpropagation
Annex 2 Recurrent Neural Networks
85 Plan of Attack
86 What are Recurrent Neural Networks
87 Vanishing Gradient Problems for RNNs
88 Long Short Term Memory
89 Practical Intuition
90 Long Short Term Memory Variations
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