Deep Learning: Advanced NLP and RNNs

Natural Language Processing with Sequence-to-sequence (seq2seq), Attention, CNNs, RNNs, and Memory Networks!

Generative AI
4.7/5
$29.99
$199.99
85% OFF!
  • All levels
  • 77 Lectures
  • 9h 20m
  • English
  • Lifetime access, certificate of completion (shareable on LinkedIn, Facebook, and Twitter), Q&A forum, subtitles in English
Login or signup to
register for this course

Course Description

It’s hard to believe it's 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


In the bonus section, we'll be looking at speech recognition using Deep Learning.

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.

See you in class!



Suggested Prerequisites:

  • Decent Python coding skills
  • Understand RNNs, CNNs, and word embeddings
  • Know how to build, train, and evaluate a neural network in Keras


Tips for success:

  • Use the video speed changer! Personally, I like to watch at 2x.
  • Take handwritten notes. This will drastically increase your ability to retain the information.
  • Write down the equations. If you don't, I guarantee it will just look like gibberish.
  • Ask lots of questions on the discussion board. The more the better!
  • Don't get discouraged if you can't solve every exercise right away. Sometimes it'll take hours, days, or maybe weeks!
  • Write code yourself, this is an applied course! Don't be a "couch potato".

Lectures

  • 14 sections
  • 77 lectures
  • 9h 20m total length
Introduction
Preview
02:51
Outline
04:09
Where to get the code
04:45
How to Succeed in this Course
03:04
Review Section Introduction
04:24
How to Open Files for Windows Users
02:18
What is a word embedding?
15:10
Using word embeddings
04:33
What is a CNN?
13:36
Where to get the data
05:06
CNN Code (part 1)
15:08
CNN Code (part 2)
06:14
What is an RNN?
13:11
GRUs and LSTMs
10:47
Different Types of RNN Tasks
12:27
A Simple RNN Experiment
06:29
RNN Code
03:25
Review Section Summary
04:49
Suggestion Box
03:10
Bidirectional RNNs Motivation
08:31
Bidirectional RNN Experiment
05:09
Bidirectional RNN Code
02:33
Image Classification with Bidirectional RNNs
06:12
Image Classification Code
05:45
Bidirectional RNNs Section Summary
02:36
Seq2Seq Theory
07:29
Seq2Seq Applications
03:27
Decoding in Detail and Teacher Forcing
06:47
Poetry Revisited
03:28
Poetry Revisited Code 1
08:29
Poetry Revisited Code 2
06:58
Seq2Seq in Code 1
07:55
Seq2Seq in Code 2
05:14
Seq2Seq Section Summary
03:04
Attention Section Introduction
02:28
Attention Theory
18:04
Teacher Forcing
02:09
Helpful Implementation Details
11:21
Attention Code 1
09:48
Attention Code 2
03:50
Visualizing Attention
02:26
Building a Chatbot without any more Code
10:31
Attention Section Summary
03:33
Memory Networks Section Introduction
09:19
Memory Networks Theory
08:55
Memory Networks Code 1
07:55
Memory Networks Code 2
05:05
Memory Networks Code 3
05:41
Memory Networks Section Summary
03:50
Stock Predictions Section Introduction
04:51
Making the Dataset
05:19
Forecasting
07:29
A Simple Time Series
09:58
Naive Forecast
08:27
Stock Prediction (pt 1)
03:45
Stock Prediction (pt 2)
06:04
Stock Prediction (pt 3)
04:50
Stock Prediction (pt 4)
02:02
Stock Predictions Section Summary
05:35
What to Learn Next
03:59
Keras Discussion
06:49
Keras Neural Network in Code
06:38
Keras Functional API
04:27
How to easily convert Keras into Tensorflow 2.0 code
01:49
What is the Appendix?
03:47
Pre-Installation Check
04:13
Anaconda Environment Setup
20:21
How to install Numpy, Scipy, Matplotlib, Pandas, PyTorch, and TensorFlow
17:33
How to Code Yourself (part 1)
15:55
How to Code Yourself (part 2)
09:24
Proof that using Jupyter Notebook is the same as not using it
12:29
Python 2 vs Python 3
04:38
How to Succeed in this Course (Long Version)
10:25
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
22:05
What order should I take your courses in? (part 1)
11:19
What order should I take your courses in? (part 2)
16:07
Where to get discount coupons and FREE AI tutorials
05:49
Speech Recognition
Stock Prediction Notebooks

Reviews

4.7

38 reviews for this course

5 Stars
(57%)
4 Stars
(35%)
3 Stars
(6%)
2 Stars
(1%)
1 Stars
(1%)

Testimonials and Success Stories

student-avatar

H. Z.

Machine Learning Research Scientist
flag-usa
United States

β€œI am one of your students. Yesterday, I presented my paper at ICCV 2019. You have a significant part in this, so I want to sincerely thank you for your in-depth guidance to the puzzle of deep learning. Please keep making awesome courses that teach us!”

5.0
student-avatar

Wade J.

Data Scientist
flag-usa
United States

β€œI just watched your short video on β€œPredicting Stock Prices with LSTMs: One Mistake Everyone Makes.” Giggled with delight.

You probably already know this, but some of us really and truly appreciate you. BTW, I spent a reasonable amount of time making a learning roadmap based on your courses and have started the journey.

Looking forward to your new stuff.”

5.0
student-avatar

Kris M.

Data Scientist
flag-usa
United States

β€œThank you for doing this! I wish everyone who call’s themselves a Data Scientist would take the time to do this either as a refresher or learn the material. I have had to work with so many people in prior roles that wanted to jump right into machine learning on my teams and didn’t even understand the first thing about the basics you have in here!!

I am signing up so that I have the easy refresh when needed and the see what you consider important, as well as to support your great work, thank you.”

5.0
student-avatar

Steve M.

Machine Learning Research Scientist
flag-usa
United States

β€œI have been intending to send you an email expressing my gratitude for the work that you have done to create all of these data science courses in Machine Learning and Artificial Intelligence. I have been looking long and hard for courses that have mathematical rigor relative to the application of the ML & AI algorithms as opposed to just exhibit some 'canned routine' and then viola here is your neural network or logistical regression.

Your courses are just what I have been seeking. I am a retired mathematician, statistician and Supply Chain executive from a large Fortune 500 company in Ohio. I also taught mathematics, statistics and operations research courses at a couple of universities in Northern Ohio.

I have taken many courses and have enjoyed the journey, I am not going to be critical of any of the organizations from whom I have taken courses. However, when I read a review about one of your courses in which the student was complaining that one would need a PhD in Mathematics to understand it, I knew this was the course (or series of courses) that I wanted. (Having advanced degrees in mathematics, I knew that it was highly unlikely that a PhD would actually be required.)”

5.0
student-avatar

Saurabh W.

Data Scientist
flag-india
India

β€œHi Sir I am a student from India. I've been wanting to write a note to thank you for the courses that you've made because they have changed my career. I wanted to work in the field of data science but I was not having proper guidance but then I stumbled upon your "Logistic Regression" course in March and since then, there's been no looking back. I learned ANNs, CNNs, RNNs, Tensorflow, NLP and whatnot by going through your lectures. The knowledge that I gained enabled me to get a job as a Business Technology Analyst at one of my dream firms even in the midst of this pandemic. For that, I shall always be grateful to you. Please keep making more courses with the level of detail that you do in low-level libraries like Theano.”

5.0
student-avatar

David P.

Financial Analyst
flag-usa
United States

β€œI just wanted to reach out and thank you for your most excellent course that I am nearing finishing.

And, I couldn't agree more with some of your "rants", and found myself nodding vigorously!

You are an excellent teacher, and a rare breed.

And, your courses are frankly, more digestible and teach a student far more than some of the top-tier courses from ivy leagues I have taken in the past.

(I plan to go through many more courses, one by one!)

I know you must be deluged with complaints in spite of the best content around That's just human nature.

Also, satisfied people rarely take the time to write, so I thought I will write in for a change. :)”

5.0
student-avatar

P. C.

Deep Learning Research Scientist
flag-china
China

β€œHello, Lazy Programmer!

In the process of completing my Master’s at Hunan University, China, I am writing this feedback to you in order to express my deep gratitude for all the knowledge and skills I have obtained studying your courses and following your recommendations.

The first course of yours I took was on Convolutional Neural Networks (β€œDeep Learning p.5”, as far as I remember). Answering one of my questions on the Q&A board, you suggested I should start from the beginning – the Linear and Logistic Regression courses. Despite that I assumed I had already known many basic things at that time, I overcame my β€œpride” and decided to start my journey in Deep Learning from scratch.

Course by course, I was renewing the basics and the prerequisites. Thus, in several months, after every day studying under your guidance, I was able to gain enough intuitions and practical skills in order to begin progressing in my research. Having a solid background, it was just a pleasure to read all the relevant papers in the field as well as to make all the experiments needed for achieving my goal – creating a high-performance CNN for offline HCCR.

I believe, the professionalism of any teacher can be estimated by the feedback received from their students, and it’s of the utmost importance for me to thank you, Lazy Programmer!

I want you to know, in spite, that we have never actually met and you haven’t taught me privately, I consider you one of my greatest Teachers.

The most important things I have learned from you (some in the hard way, though) beside many exciting modern Deep Learning/AI techniques and algorithms are:

1) If one doesn’t know how to program something, one doesn’t understand it completely.

2) If one is not honest with oneself about one’s prior knowledge, one will never succeed in studying more advanced things.

3) Developing skills in BOTH Math and Programming is what makes one a good student of this major.

I am still studying your courses, and am certain I will ask you more than just a few technical questions regarding their content, but I already would like to say, that I will remember your contribution to my adventure in the Deep Learning field, and consider it as big as one of such great scientists’ as Andrew Ng, Geoffrey Hinton, and my supervisor.

Thank you, Lazy Programmer! ιžεΈΈζ„Ÿθ°’ζ‚¨οΌŒLazy θ€εΈˆοΌ

If you are interested, you can find my first paper’s preprint here:

https://arxiv.org/abs/xxx”

5.0
student-avatar

Dima K.

Data Scientist
flag-ukraine
Ukraine

β€œBy the way, if you are interested to hear. I used the HMM classification, as it was in your course (95% of the script, I had little adjustments there), for the Customer-Care department in a big known fintech company. to predict who will call them, so they can call him before the rush hours, and improve the service. Instead of a poem, I Had a sequence of the last 24 hours' events that the customer had, like: "Loaded money", "Usage in the food service", "Entering the app", "Trying to change the password", etc... the label was called or didn't call. The outcome was great. They use it for their VIP customers. Our data science department and I got a lot of praise.”

5.0
student-avatar

Andres Lopez C.

Data Engineer
flag-usa
United States

β€œThis course is exactly what I was looking for. The instructor does an impressive job making students understand they need to work hard in order to learned. The examples are clear, and the explanations of the theory is very interesting.”

5.0
student-avatar

Mohammed K.

Machine Learning Engineer
flag-germany
Germany

β€œThank you, I think you have opened my eyes. I was using API to implement Deep learning algorithms and each time I felt I was messing out on some things. So thank you very much.”

5.0
student-avatar

Tom P.

Machine Learning Engineer
flag-usa
United States

β€œI have now taken a few classes from some well-known AI profs at Stanford (Andrew Ng, Christopher Manning, …) with an overall average mark in the mid-90s. Just so you know, you are as good as any of them. But I hope that you already know that.

I wish you a happy and safe holiday season. I am glad you chose to share your knowledge with the rest of us.”

5.0
Start learning today

Join 30 day bootcamp for free

4.7/5 from β€” 600k+ learners