Udemy
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
Development
Web Development Data Science Mobile Development Programming Languages Game Development Database Design & Development Software Testing Software Engineering Development Tools No-Code Development
Business
Entrepreneurship Communications Management Sales Business Strategy Operations Project Management Business Law Business Analytics & Intelligence Human Resources Industry E-Commerce Media Real Estate Other Business
Finance & Accounting
Accounting & Bookkeeping Compliance Cryptocurrency & Blockchain Economics Finance Finance Cert & Exam Prep Financial Modeling & Analysis Investing & Trading Money Management Tools Taxes Other Finance & Accounting
IT & Software
IT Certification Network & Security Hardware Operating Systems Other IT & Software
Office Productivity
Microsoft Apple Google SAP Oracle Other Office Productivity
Personal Development
Personal Transformation Personal Productivity Leadership Career Development Parenting & Relationships Happiness Esoteric Practices Religion & Spirituality Personal Brand Building Creativity Influence Self Esteem & Confidence Stress Management Memory & Study Skills Motivation Other Personal Development
Design
Web Design Graphic Design & Illustration Design Tools User Experience Design Game Design Design Thinking 3D & Animation Fashion Design Architectural Design Interior Design Other Design
Marketing
Digital Marketing Search Engine Optimization Social Media Marketing Branding Marketing Fundamentals Marketing Analytics & Automation Public Relations Advertising Video & Mobile Marketing Content Marketing Growth Hacking Affiliate Marketing Product Marketing Other Marketing
Lifestyle
Arts & Crafts Beauty & Makeup Esoteric Practices Food & Beverage Gaming Home Improvement Pet Care & Training Travel Other Lifestyle
Photography & Video
Digital Photography Photography Portrait Photography Photography Tools Commercial Photography Video Design Other Photography & Video
Health & Fitness
Fitness General Health Sports Nutrition Yoga Mental Health Dieting Self Defense Safety & First Aid Dance Meditation Other Health & Fitness
Music
Instruments Music Production Music Fundamentals Vocal Music Techniques Music Software Other Music
Teaching & Academics
Engineering Humanities Math Science Online Education Social Science Language Teacher Training Test Prep Other Teaching & Academics
AWS Certification Microsoft Certification AWS Certified Solutions Architect - Associate AWS Certified Cloud Practitioner CompTIA A+ Cisco CCNA Amazon AWS CompTIA Security+ AWS Certified Developer - Associate
Graphic Design Photoshop Adobe Illustrator Drawing Digital Painting InDesign Character Design Canva Figure Drawing
Life Coach Training Neuro-Linguistic Programming Mindfulness Personal Development Personal Transformation Meditation Life Purpose Emotional Intelligence Neuroscience
Web Development JavaScript React CSS Angular PHP WordPress Node.Js Python
Google Flutter Android Development iOS Development Swift React Native Dart Programming Language Mobile Development Kotlin SwiftUI
Digital Marketing Google Ads (Adwords) Social Media Marketing Google Ads (AdWords) Certification Marketing Strategy Internet Marketing YouTube Marketing Email Marketing Retargeting
SQL Microsoft Power BI Tableau Business Analysis Business Intelligence MySQL Data Modeling Data Analysis Big Data
Business Fundamentals Entrepreneurship Fundamentals Business Strategy Online Business Startup Business Plan Freelancing Blogging Home Business
Unity Game Development Fundamentals Unreal Engine C# 3D Game Development C++ 2D Game Development Unreal Engine Blueprints Blender
30-Day Money-Back Guarantee
Development Data Science Python

Natural Language Processing with Deep Learning in Python

Complete guide on deriving and implementing word2vec, GloVe, word embeddings, and sentiment analysis with recursive nets
Highest Rated
Rating: 4.7 out of 54.7 (6,411 ratings)
39,708 students
Created by Lazy Programmer Team, Lazy Programmer Inc.
Last updated 1/2021
English
English [Auto], French [Auto], 
30-Day Money-Back Guarantee

What you'll learn

  • Understand and implement word2vec
  • Understand the CBOW method in word2vec
  • Understand the skip-gram method in word2vec
  • Understand the negative sampling optimization in word2vec
  • Understand and implement GloVe using gradient descent and alternating least squares
  • Use recurrent neural networks for parts-of-speech tagging
  • Use recurrent neural networks for named entity recognition
  • Understand and implement recursive neural networks for sentiment analysis
  • Understand and implement recursive neural tensor networks for sentiment analysis
  • Use Gensim to obtain pretrained word vectors and compute similarities and analogies

Course content

13 sections • 95 lectures • 12h 6m total length

  • Introduction, Outline, and Review
    Preview05:35
  • Anyone Can Succeed in this Course
    12:42
  • Where to get the code / data for this course
    09:17
  • Preprocessed Wikipedia Data
    03:03
  • How to Open Files for Windows Users
    02:18

  • Preview07:56
  • What is a word analogy?
    07:58
  • Trying to find and assess word vectors using TF-IDF and t-SNE
    07:42
  • Pretrained word vectors from GloVe
    11:05
  • Pretrained word vectors from word2vec
    06:31
  • Text Classification with word vectors
    04:24
  • Text Classification in Code
    06:14
  • Using pretrained vectors later in the course
    03:32
  • Suggestion Box
    03:03

  • Review Section Intro
    03:13
  • Bigrams and Language Models
    14:47
  • Bigrams in Code
    14:19
  • Neural Bigram Model
    07:56
  • Neural Bigram Model in Code
    06:48
  • Neural Network Bigram Model
    09:13
  • Neural Network Bigram Model in Code
    03:31
  • Improving Efficiency
    14:35
  • Improving Efficiency in Code
    04:52
  • Review Section Summary
    03:26

  • Return of the Bigram
    03:07
  • CBOW
    07:39
  • Skip-Gram
    04:00
  • Hierarchical Softmax
    08:22
  • Negative Sampling
    14:11
  • Negative Sampling - Important Details
    05:09
  • Why do I have 2 word embedding matrices and what do I do with them?
    02:16
  • Word2Vec implementation tricks
    04:49
  • Word2Vec implementation outline
    04:09
  • Word2Vec in Code with Numpy
    10:47
  • Tensorflow or Theano - Your Choice!
    04:09
  • Word2Vec Tensorflow Implementation Details
    03:58
  • Word2Vec Tensorflow in Code
    04:06
  • Alternative to Wikipedia Data: Brown Corpus
    06:03

  • GloVe Section Introduction
    02:19
  • Matrix Factorization for Recommender Systems - Basic Concepts
    21:08
  • Matrix Factorization Training
    08:11
  • Expanding the Matrix Factorization Model
    09:23
  • Regularization for Matrix Factorization
    06:18
  • GloVe - Global Vectors for Word Representation
    04:12
  • Recap of ways to train GloVe
    02:31
  • GloVe in Code - Numpy Gradient Descent
    16:48
  • GloVe in Code - Alternating Least Squares
    04:42
  • GloVe in Tensorflow with Gradient Descent
    07:03
  • Visualizing country analogies with t-SNE
    04:24
  • Hyperparameter Challenge
    02:19
  • Training GloVe with SVD (Singular Value Decomposition)
    10:38

  • Pointwise Mutual Information - Word2Vec as Matrix Factorization
    12:06
  • PMI in Code
    07:21

  • Parts-of-Speech (POS) Tagging
    05:00
  • How can neural networks be used to solve POS tagging?
    04:08
  • Parts-of-Speech Tagging Baseline
    15:18
  • Parts-of-Speech Tagging Recurrent Neural Network in Theano
    13:05
  • Parts-of-Speech Tagging Recurrent Neural Network in Tensorflow
    12:17
  • How does an HMM solve POS tagging?
    07:57
  • Parts-of-Speech Tagging Hidden Markov Model (HMM)
    05:58
  • Named Entity Recognition (NER)
    03:01
  • Comparing NER and POS tagging
    02:01
  • Named Entity Recognition Baseline
    05:54
  • Named Entity Recognition RNN in Theano
    02:19
  • Named Entity Recognition RNN in Tensorflow
    02:13
  • Hyperparameter Challenge II
    02:13

  • Recursive Neural Networks Section Introduction
    07:14
  • Sentences as Trees
    Preview05:29
  • Data Description for Recursive Neural Networks
    06:52
  • What are Recursive Neural Networks / Tree Neural Networks (TNNs)?
    05:41
  • Building a TNN with Recursion
    04:47
  • Trees to Sequences
    06:38
  • Recursive Neural Tensor Networks
    06:22
  • RNTN in Tensorflow (Tips)
    12:19
  • RNTN in Tensorflow (Code)
    11:19
  • Recursive Neural Network in TensorFlow with Recursion
    04:12

  • (Review) Theano Basics
    07:47
  • (Review) Theano Neural Network in Code
    09:17
  • (Review) Tensorflow Basics
    07:27
  • (Review) Tensorflow Neural Network in Code
    09:43

  • Windows-Focused Environment Setup 2018
    20:20
  • How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
    17:32

Requirements

  • Install Numpy, Matplotlib, Sci-Kit Learn, and Theano or TensorFlow (should be extremely easy by now)
  • Understand backpropagation and gradient descent, be able to derive and code the equations on your own
  • Code a recurrent neural network from basic primitives in Theano (or Tensorflow), especially the scan function
  • Code a feedforward neural network in Theano (or Tensorflow)
  • Helpful to have experience with tree algorithms

Description

In this course we are going to look at NLP (natural language processing) with deep learning.

Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices.

These allowed us to do some pretty cool things, like detect spam emails, write poetry, spin articles, and group together similar words.

In this course I’m going to show you how to do even more awesome things. We’ll learn not just 1, but 4 new architectures in this course.

First up is word2vec.

In this course, I’m going to show you exactly how word2vec works, from theory to implementation, and you’ll see that it’s merely the application of skills you already know.

Word2vec is interesting because it magically maps words to a vector space where you can find analogies, like:

  • king - man = queen - woman

  • France - Paris = England - London

  • December - Novemeber = July - June

For those beginners who find algorithms tough and just want to use a library, we will demonstrate the use of the Gensim library to obtain pre-trained word vectors, compute similarities and analogies, and apply those word vectors to build text classifiers.


We are also going to look at the GloVe method, which also finds word vectors, but uses a technique called matrix factorization, which is a popular algorithm for recommender systems.

Amazingly, the word vectors produced by GLoVe are just as good as the ones produced by word2vec, and it’s way easier to train.

We will also look at some classical NLP problems, like parts-of-speech tagging and named entity recognition, and use recurrent neural networks to solve them. You’ll see that just about any problem can be solved using neural networks, but you’ll also learn the dangers of having too much complexity.

Lastly, you’ll learn about recursive neural networks, which finally help us solve the problem of negation in sentiment analysis. Recursive neural networks exploit the fact that sentences have a tree structure, and we can finally get away from naively using bag-of-words.

All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Theano. 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:

  • calculus (taking derivatives)

  • matrix addition, multiplication

  • probability (conditional and joint distributions)

  • Python coding: if/else, loops, lists, dicts, sets

  • Numpy coding: matrix and vector operations, loading a CSV file

  • neural networks and backpropagation, be able to derive and code gradient descent algorithms on your own

  • Can write a feedforward neural network in Theano or TensorFlow

  • Can write a recurrent neural network / LSTM / GRU in Theano or TensorFlow from basic primitives, especially the scan function

  • Helpful to have experience with tree algorithms


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 and professionals who want to create word vector representations for various NLP tasks
  • Students and professionals who are interested in state-of-the-art neural network architectures like recursive neural networks
  • SHOULD NOT: Anyone who is not comfortable with the prerequisites.

Instructors

Lazy Programmer Team
Artificial Intelligence and Machine Learning Engineer
Lazy Programmer Team
  • 4.6 Instructor Rating
  • 40,699 Reviews
  • 148,448 Students
  • 14 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.

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer
Lazy Programmer Inc.
  • 4.6 Instructor Rating
  • 108,547 Reviews
  • 423,229 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.

  • Udemy for Business
  • Teach on Udemy
  • Get the app
  • About us
  • Contact us
  • Careers
  • Blog
  • Help and Support
  • Affiliate
  • Impressum Kontakt
  • Terms
  • Privacy policy
  • Cookie settings
  • Sitemap
  • Featured courses
Udemy
© 2021 Udemy, Inc.