Natural Language Processing with Deep Learning in Python

Natural Language Processing with Deep Learning in Python, available at $64.99, has an average rating of 4.68, with 110 lectures, based on 8380 reviews, and has 48471 subscribers.

You will learn about 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 Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion This course is ideal for individuals who are Students and professionals who want to create word vector representations for various NLP tasks or Students and professionals who are interested in state-of-the-art neural network architectures like recursive neural networks or SHOULD NOT: Anyone who is not comfortable with the prerequisites. It is particularly useful for Students and professionals who want to create word vector representations for various NLP tasks or Students and professionals who are interested in state-of-the-art neural network architectures like recursive neural networks or SHOULD NOT: Anyone who is not comfortable with the prerequisites.

Enroll now: Natural Language Processing with Deep Learning in Python

Summary

Title: Natural Language Processing with Deep Learning in Python

Price: $64.99

Average Rating: 4.68

Number of Lectures: 110

Number of Published Lectures: 96

Number of Curriculum Items: 110

Number of Published Curriculum Objects: 96

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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
Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion

Who Should Attend

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.

Target Audiences

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.

Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.

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)

UNIQUE FEATURES

Every line of code explained in detail – email me any time if you disagree
No wasted time “typing” on the keyboard like other courses – let’s be honest, nobody can really write code worth learning about in just 20 minutes from scratch
Not afraid of university-level math – get important details about algorithms that other courses leave out

Course Curriculum

Chapter 1: Outline, Review, and Logistical Things

Lecture 1: Introduction, Outline, and Review

Lecture 2: How to Succeed in this Course

Lecture 3: Where to get the code / data for this course

Lecture 4: Preprocessed Wikipedia Data

Lecture 5: How to Open Files for Windows Users

Chapter 2: Beginner's Corner: Working with Word Vectors

Lecture 1: What are vectors?

Lecture 2: What is a word analogy?

Lecture 3: Trying to find and assess word vectors using TF-IDF and t-SNE

Lecture 4: Pretrained word vectors from GloVe

Lecture 5: Pretrained word vectors from word2vec

Lecture 6: Text Classification with word vectors

Lecture 7: Text Classification in Code

Lecture 8: Using pretrained vectors later in the course

Lecture 9: Suggestion Box

Chapter 3: Review of Language Modeling and Neural Networks

Lecture 1: Review Section Intro

Lecture 2: Bigrams and Language Models

Lecture 3: Bigrams in Code

Lecture 4: Neural Bigram Model

Lecture 5: Neural Bigram Model in Code

Lecture 6: Neural Network Bigram Model

Lecture 7: Neural Network Bigram Model in Code

Lecture 8: Improving Efficiency

Lecture 9: Improving Efficiency in Code

Lecture 10: Review Section Summary

Chapter 4: Word Embeddings and Word2Vec

Lecture 1: Return of the Bigram

Lecture 2: CBOW

Lecture 3: Skip-Gram

Lecture 4: Hierarchical Softmax

Lecture 5: Negative Sampling

Lecture 6: Negative Sampling – Important Details

Lecture 7: Why do I have 2 word embedding matrices and what do I do with them?

Lecture 8: Word2Vec implementation tricks

Lecture 9: Word2Vec implementation outline

Lecture 10: Word2Vec in Code with Numpy

Lecture 11: Tensorflow or Theano – Your Choice!

Lecture 12: Word2Vec Tensorflow Implementation Details

Lecture 13: Word2Vec Tensorflow in Code

Lecture 14: Alternative to Wikipedia Data: Brown Corpus

Chapter 5: Word Embeddings using GloVe

Lecture 1: GloVe Section Introduction

Lecture 2: Matrix Factorization for Recommender Systems – Basic Concepts

Lecture 3: Matrix Factorization Training

Lecture 4: Expanding the Matrix Factorization Model

Lecture 5: Regularization for Matrix Factorization

Lecture 6: GloVe – Global Vectors for Word Representation

Lecture 7: Recap of ways to train GloVe

Lecture 8: GloVe in Code – Numpy Gradient Descent

Lecture 9: GloVe in Code – Alternating Least Squares

Lecture 10: GloVe in Tensorflow with Gradient Descent

Lecture 11: Visualizing country analogies with t-SNE

Lecture 12: Hyperparameter Challenge

Lecture 13: Training GloVe with SVD (Singular Value Decomposition)

Chapter 6: Unifying Word2Vec and GloVe

Lecture 1: Pointwise Mutual Information – Word2Vec as Matrix Factorization

Lecture 2: PMI in Code

Chapter 7: Using Neural Networks to Solve NLP Problems

Lecture 1: Parts-of-Speech (POS) Tagging

Lecture 2: How can neural networks be used to solve POS tagging?

Lecture 3: Parts-of-Speech Tagging Baseline

Lecture 4: Parts-of-Speech Tagging Recurrent Neural Network in Theano

Lecture 5: Parts-of-Speech Tagging Recurrent Neural Network in Tensorflow

Lecture 6: How does an HMM solve POS tagging?

Lecture 7: Parts-of-Speech Tagging Hidden Markov Model (HMM)

Lecture 8: Named Entity Recognition (NER)

Lecture 9: Comparing NER and POS tagging

Lecture 10: Named Entity Recognition Baseline

Lecture 11: Named Entity Recognition RNN in Theano

Lecture 12: Named Entity Recognition RNN in Tensorflow

Lecture 13: Hyperparameter Challenge II

Chapter 8: Recursive Neural Networks (Tree Neural Networks)

Lecture 1: Recursive Neural Networks Section Introduction

Lecture 2: Sentences as Trees

Lecture 3: Data Description for Recursive Neural Networks

Lecture 4: What are Recursive Neural Networks / Tree Neural Networks (TNNs)?

Lecture 5: Building a TNN with Recursion

Lecture 6: Trees to Sequences

Lecture 7: Recursive Neural Tensor Networks

Lecture 8: RNTN in Tensorflow (Tips)

Lecture 9: RNTN in Tensorflow (Code)

Lecture 10: Recursive Neural Network in TensorFlow with Recursion

Chapter 9: Theano and Tensorflow Basics Review

Lecture 1: (Review) Theano Basics

Lecture 2: (Review) Theano Neural Network in Code

Lecture 3: (Review) Tensorflow Basics

Lecture 4: (Review) Tensorflow Neural Network in Code

Chapter 10: Setting Up Your Environment (FAQ by Student Request)

Lecture 1: Pre-Installation Check

Lecture 2: Anaconda Environment Setup

Lecture 3: How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

Chapter 11: Extra Help With Python Coding for Beginners (FAQ by Student Request)

Lecture 1: How to install wp2txt or WikiExtractor.py

Lecture 2: How to Uncompress a .tar.gz file

Lecture 3: How to Code by Yourself (part 1)

Lecture 4: How to Code by Yourself (part 2)

Lecture 5: Proof that using Jupyter Notebook is the same as not using it

Lecture 6: Python 2 vs Python 3

Instructors

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer

Rating Distribution

1 stars: 126 votes
2 stars: 127 votes
3 stars: 377 votes
4 stars: 2938 votes
5 stars: 4811 votes

Frequently Asked Questions

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