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Development Data Science Deep Learning

Deep Learning: Advanced NLP and RNNs

Natural Language Processing with Sequence-to-sequence (seq2seq), Attention, CNNs, RNNs, and Memory Networks!
Rating: 4.6 out of 54.6 (3,069 ratings)
19,164 students
Created by Lazy Programmer Inc.
Last updated 1/2021
English
English [Auto], Indonesian [Auto], 
30-Day Money-Back Guarantee

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)
Curated for the Udemy for Business collection

Course content

11 sections • 65 lectures • 8h 21m total length

  • Preview02:51
  • Preview04:09
  • Preview04:45
  • Anyone Can Succeed in this Course
    12:42

  • 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
    03:03

  • 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

  • (Review) Keras Discussion
    06:48
  • (Review) Keras Neural Network in Code
    06:37
  • (Review) Keras Functional API
    04:26
  • (Review) How to easily convert Keras into Tensorflow 2.0 code
    01:49

  • Windows-Focused Environment Setup 2018
    20:20
  • How to How to install Numpy, Theano, Tensorflow, etc...
    17:30

  • How to Code by Yourself (part 1)
    15:54
  • How to Code by Yourself (part 2)
    09:23
  • 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)
    10:24
  • Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
    22:04
  • Machine Learning and AI Prerequisite Roadmap (pt 1)
    11:18
  • Machine Learning and AI Prerequisite Roadmap (pt 2)
    16:07

Requirements

  • Understand what deep learning is for and how it is used
  • Decent Python coding skills, especially tools for data science (Numpy, Matplotlib)
  • Preferable to have experience with RNNs, LSTMs, and GRUs
  • Preferable to have experience with Keras
  • Preferable to understand word embeddings

Description

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.

See you in class!


"If you can't implement it, you don't understand it"

  • Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

  • My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

  • Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

  • After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...


Suggested Prerequisites:

  • Decent Python coding skills

  • Understand RNNs, CNNs, and word embeddings

  • Know how to build, train, and evaluate a neural network in Keras


WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

  • Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

Who this course is for:

  • Students in machine learning, deep learning, artificial intelligence, and data science
  • Professionals in machine learning, deep learning, artificial intelligence, and data science
  • Anyone interested in state-of-the-art natural language processing

Featured review

Zack Williams
Zack Williams
46 courses
8 reviews
Rating: 5.0 out of 5a year ago
As a very passionate practitioner and learner of NLP and Deep Learning, this is THE BEST NLP COURSE ON THE CURRENT WEB!!! Lazy Programmer did an awesome job here especially with the intuition to code mapping. He takes everything out of the black box. Because of this, I purchased other courses of his as well.

Instructor

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer
Lazy Programmer Inc.
  • 4.6 Instructor Rating
  • 108,419 Reviews
  • 423,064 Students
  • 28 Courses

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.

I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.

Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.

I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.

My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. 

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

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