English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 8 Hours | 3.00 GB
Natural Language Processing with Sequence-to-sequence (seq2seq), Attention, CNNs, RNNs, and Memory Networks!
It’s hard to believe it’s been been over a year since I released my first course on Deep Learning with NLP (natural language processing).
A lot of cool stuff has happened since then, and I’ve been deep in the trenches learning, researching, and accumulating the best and most useful ideas to bring them back to you.
So what is this course all about, and how have things changed since then?
In previous courses, you learned about some of the fundamental building blocks of Deep NLP. We looked at RNNs (recurrent neural networks), CNNs (convolutional neural networks), and word embedding algorithms such as word2vec and GloVe.
This course takes you to a higher systems level of thinking.
Since you know how these things work, it’s time to build systems using these components.
At the end of this course, you’ll be able to build applications for problems like:
- text classification (examples are sentiment analysis and spam detection)
- neural machine translation
- question answering
We’ll take a brief look chatbots and as you’ll learn in this course, this problem is actually no different from machine translation and question answering.
To solve these problems, we’re going to look at some advanced Deep NLP techniques, such as:
- bidirectional RNNs
- seq2seq (sequence-to-sequence)
- attention
- memory networks
All of the materials of this course can be downloaded and installed for FREE. We will do most of our work in Python libraries such as Keras, Numpy, Tensorflow, and Matpotlib to make things super easy and focus on the high-level concepts. I am always available to answer your questions and help you along your data science journey.
This course focuses on “how to build and understand”, not just “how to use”. Anyone can learn to use an API in 15 minutes after reading some documentation. It’s not about “remembering facts”, it’s about “seeing for yourself” via experimentation. It will teach you how to visualize what’s happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.
What you’ll learn
- Build a text classification system (can be used for spam detection, sentiment analysis, and similar problems)
- Build a neural machine translation system (can also be used for chatbots and question answering)
- Build a sequence-to-sequence (seq2seq) model
- Build an attention model
- Build a memory network (for question answering based on stories)
Table of Contents
Welcome
1 Introduction
2 Outline
3 Where to get the code
4 How to Succeed in this Course
Review
5 Review Section Introduction
6 Different Types of RNN Tasks
7 A Simple RNN Experiment
8 RNN Code
9 Review Section Summary
10 What is a word embedding
11 Using word embeddings
12 What is a CNN
13 Where to get the data
14 CNN Code (part 1)
15 CNN Code (part 2)
16 What is an RNN
17 GRUs and LSTMs
Bidirectional RNNs
18 Bidirectional RNNs Motivation
19 Bidirectional RNN Experiment
20 Bidirectional RNN Code
21 Image Classification with Bidirectional RNNs
22 Image Classification Code
23 Bidirectional RNNs Section Summary
Sequence-to-sequence models (Seq2Seq)
24 Seq2Seq Theory
25 Seq2Seq Applications
26 Decoding in Detail and Teacher Forcing
27 Poetry Revisited
28 Poetry Revisited Code 1
29 Poetry Revisited Code 2
30 Seq2Seq in Code 1
31 Seq2Seq in Code 2
32 Seq2Seq Section Summary
Attention
33 Attention Section Introduction
34 Attention Theory
35 Teacher Forcing
36 Helpful Implementation Details
37 Attention Code 1
38 Attention Code 2
39 Visualizing Attention
40 Building a Chatbot without any more Code
41 Attention Section Summary
Memory Networks
42 Memory Networks Section Introduction
43 Memory Networks Theory
44 Memory Networks Code 1
45 Memory Networks Code 2
46 Memory Networks Code 3
47 Memory Networks Section Summary
Basics Review
48 (Review) Keras Discussion
49 (Review) Keras Neural Network in Code
50 (Review) Keras Functional API
Appendix
51 What is the Appendix
52 What order should I take your courses in (part 2)
53 Python 2 vs Python 3
54 BONUS Where to get discount coupons and FREE deep learning material
55 Windows-Focused Environment Setup 2018
56 How to How to install Numpy, Theano, Tensorflow, etc…
57 Is this for Beginners or Experts Academic or Practical Fast or slow-paced
58 How to Succeed in this Course (Long Version)
59 How to Code by Yourself (part 1)
60 How to Code by Yourself (part 2)
61 Proof that using Jupyter Notebook is the same as not using it
62 What order should I take your courses in (part 1)
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