Deep Learning for Natural Language Processing
Develop Deep Learning Models for your Natural Language Problems
Deep learning methods are achieving state-of-the-art results on challenging machine learning problems such as describing photos and translating text from one language to another.
In this new laser-focused Ebook written in the friendly Machine Learning Mastery style that you’re used to, finally cut through the math, research papers and patchwork descriptions about natural language processing.
Using clear explanations, standard Python libraries and step-by-step tutorial lessons you will discover what natural language processing is, the promise of deep learning in the field, how to clean and prepare text data for modeling, and how to develop deep learning models for your own natural language processing projects.
About the Ebook:
- PDF format Ebook.
- 8 parts, 30 step-by-step lessons, 414 pages.
- 6 end-to-end tutorial projects.
- 99 Python (.py) code files included.
Clear and Complete Examples.
No Math. Nothing Hidden.
Click to jump straight to the packages.
Working with Text is… important, under-discussed, and HARD
We are awash with text, from books, papers, blogs, tweets, news, and increasingly text from spoken utterances.
Every day, I get questions asking how to develop machine learning models for text data.
Working with text is hard as it requires drawing upon knowledge from diverse domains such as linguistics, machine learning, statistical natural language processing, and these days, deep learning.
The Problem with Text
The problem with modeling text is that it is messy, and machine learning algorithms prefer well defined fixed-length inputs and outputs.
Machine learning algorithms cannot work with raw text directly; the text must be converted into numbers. Specifically, vectors of numbers.
This is called feature extraction or feature encoding and this is one of the key areas where deep learning is really shaking things up.
UNLOCK Natural Language Processing with Deep Learning
Classical linguistic methods for natural language processing required experts in language defining rules to cover specific cases. These worked in narrow cases but turned out to be fragile.
Statistical methods improve upon classical linguistic methods by learning rules and models from data rather than requiring them to be specified in a top-down manner. They result in much better performance, but must still be complemented with hand-crafted augmentations by language experts in order to achieve useful results.
Often, a pipeline of statistical methods are required to achieve a single modeling outcome, such as in the case of machine translation.
Deep learning methods are starting to out-compete the statistical methods on some challenging natural language processing problems with singular and simpler models.
The Promise of Deep Neural Networks for NLP
Deep learning methods are popular, primarily because they are delivering on their promise.
That is not to say that there is no hype around the technology, but that the hype is based on very real results that are being demonstrated across a suite of very challenging artificial intelligence problems from computer vision and natural language processing.
Some of the first large demonstrations of the power of deep learning were in natural language processing, specifically speech recognition. More recently in machine translation.
The 5 promises of deep learning for natural language processing are as follows:
- The Promise of Drop-in Replacement Models. That is, deep learning methods can be dropped into existing natural language systems as replacement models that can achieve commensurate or better performance.
- The Promise of New NLP Models. That is, deep learning methods offer the opportunity of new modeling approaches to challenging natural language problems like sequence-to-sequence prediction.
- The Promise of Feature Learning. That is, that deep learning methods can learn the features from natural language required by the model, rather than requiring that the features be specified and extracted by an expert.
- The Promise of Continued Improvement. That is, that the performance of deep learning in natural language processing is based on real results and that the improvements appear to be continuing and perhaps speeding up.
- The Promise of End-to-End Models. That is, that large end-to-end deep learning models can be fit on natural language problems offering a more general and better-performing approach.
Impressive Applications of Deep Learning
Natural language processing is not “solved“, but deep learning is required to get you to the state-of-the-art on many challenging problems in the field.
Let’s look at 3 examples to give you a snapshot of the results that deep learning is capable of achieving in the field of natural language processing:
1) Automatic Image Caption Generation
Automatic image captioning is the task where, given a photograph, the system must generate a caption that describes the contents of the image.
2) Automatic Translation of Text
Automatic text translation is the task where you are given sentences of text in one language and must translate them into text in another language.
3) Automatic Text Classification
Automatic text classification is the task of assigning a class label given a text document such as a review, tweet, or email.
You can see that developing systems capable of these tasks would be valuable in a wide range of domains and industries.
So, how can you get started and get good at using deep learning for natural language processing fast?
Introducing my new Ebook:
“Deep Learning for Natural Language Processing“
This is the book I wish I had when I was getting started with Deep Learning for NLP.
This book was born out of one thought:
How can I get you proficient with deep learning for NLP as fast as possible?
The Machine Learning Mastery method suggests that the best way of learning this material is by doing. This means the focus of the book is hands-on projects and tutorials. This also means not covering some topics, even topics covered by “everyone else“, like language theory or modeling math.
This book was designed to teach you step-by-step how to bring modern deep learning methods to your natural language processing projects.
You will be led along the critical path from a practitioner interested in natural language processing, to a practitioner that can confidently apply deep learning methods to natural language processing problems.
I want you to get proficient with deep learning for NLP as quickly as you can. I want you using these methods on your project.
Who Is This Book For?
…so is this book right for YOU?
Let’s make sure you are in the right place.
This book is for developers that know some applied machine learning and some deep learning.
Maybe you want or need to start using deep learning for text on your research project or on a project at work. This book was written to help you do that quickly and efficiently by compressing years worth of knowledge and experience into a laser-focused course of hands-on tutorials.
This guide was written in the top-down and results-first style that you’re used to from Machine Learning Mastery.
The lessons in this book assume a few things about you.
You need to know:
- You need to know your way around basic Python and NumPy.
- You need to know your way around basic scikit-learn.
- You need to know your way around basic Keras for deep learning.
You do NOT need to know:
- You do not need to be a math wiz!
- You do not need to be a deep learning expert!
- You do not need to be a master of natural language!
About Your Outcomes
…so what will YOU know after reading it?
This book will teach you how to get results.
After reading and working through this book,
you will know:
- What natural language processing is and why it is challenging.
- What deep learning is and how it is different from other machine learning methods, specifically how it is best understood by deep learning experts.
- The promise of deep learning methods for natural language processing problems as defined by experts in the field.
- How to prepare text data for modeling by hand and using best-of-breed Python libraries such as the natural language toolkit or NLTK.
- How to develop and plot distributed representations of text using word embedding models with the Gensim library.
- How to develop a bag-of-words model, a representation technique that can be used for machine learning and deep learning methods.
- How to develop a neural sentiment analysis model for automatically predicting the class label for a text document.
- How to develop a neural language model, required for any text generating neural network.
- How to develop a photo captioning system to automatically generate textual descriptions of photographs.
- How to develop a neural machine translation system for translating text from one language to another.
This book will NOT teach you how to be a research scientist and all the theory behind why specific methods work. It will teach you how to get results and deliver value on your natural language processing projects.
Exactly What You Need to Know
…30 carefully designed lessons to take you from NLP Beginner to Practitioner
This book was designed to be a practitioners crash course into deep learning for natural language processing.
There are a lot of things you could learn about NLP, from theory to applications to Keras API. My goal is to take you straight to getting results with 30 laser-focused lessons.
I designed the lessons to focus on key skills such as data cleaning, data preparation and modeling required on every single natural language processing project.
Each of the lessons are designed to take you about one hour to read through and complete, excluding the extensions and further reading.
You can choose to work through the lessons one per day, one per week, or at your own pace. I think momentum is critically important, and this book was intended to be read and used, not to sit idle. I would recommend picking a schedule and sticking to it.
The tutorials are divided into eight parts:
- Part 1: Foundations. Discover a gentle introduction to natural language processing, deep learning, and the promise of combining the two, as well as tutorials on how to get started with Keras.
- Part 2: Data Preparation: Discover tutorials that show how to clean, prepare and encode text ready for modeling with neural networks.
- Part 3: Bag-of-Words. Discover the bag-of-words model, a staple representation for machine learning and a good starting point for neural networks for sentiment analysis.
- Part 4: Word Embeddings. Discover a more powerful word representation in word embeddings, how to develop them as standalone models, and how to learn them as part of neural network models.
- Part 5: Text Classification. Discover how to leverage word embeddings and convolutional neural networks to learn spatial invariant models of text for sentiment analysis, a successor to the bag-of-words model.
- Part 6: Language Modeling. Discover how to develop character-based and word-based language models, a technique that is required as part of any modern text generating model.
- Part 7: Image Captioning. Discover how to combine a pre-trained object recognition model with a language model to automatically caption images.
- Part 8: Machine Translation. Discover how to combine two language models to automatically translate text from one language to another.
Below is an overview of the 30 step-by-step tutorial lessons you will complete:
Each lesson was designed to be completed in about 30-to-60 minutes by the average developer.
Part I. Foundations
- Lesson 01: Natural Language Processing
- Lesson 02: Deep Learning
- Lesson 03: Promise of Deep Learning for Natural Language
- Lesson 04: Now to Develop Deep Learning Models With Keras
Part II. Data Preparation
- Lesson 05: How to Clean Text Manually and with NLTK
- Lesson 06: How to Prepare Text Data with scikit-learn
- Lesson 07: How to Prepare Text Data With Keras
Part III. Bag-of-Words
- Lesson 08: The Bag-of-Words Model
- Lesson 09: Prepare Movie Review Data for Sentiment Analysis
- Lesson 10: Neural Bag-of-Words Model for Sentiment Analysis
Part IV. Word Embeddings
- Lesson 11: The Word Embedding Model
- Lesson 12: How to Develop Word Embeddings with Gensim
- Lesson 13: How to Learn and Load Word Embeddings in Keras
Part V. Text Classification
- Lesson 14: Neural Models for Document Classification
- Lesson 15: Develop an Embedding + CNN Model
- Lesson 16: Develop an n-gram CNN Model for Sentiment Analysis
Part VI: Language Modeling
- Lesson 17: Neural Language Modeling
- Lesson 18: Develop a Character-Based Neural Language Model
- Lesson 19: How to Develop a Word-Based Neural Language Model
- Lesson 20: Develop a Neural Language Model for Text Generation
Part VII: Image Captioning
- Lesson 21: Neural Image Caption Generation
- Lesson 22: Neural Network Models for Caption Generation
- Lesson 23: Load and Use a Pre-Trained Object Recognition Model
- Lesson 24: How to Evaluate Generated Text With the BLEU Score
- Lesson 25: How to Prepare a Photo Caption Dataset For Modeling
- Lesson 26: Develop a Neural Image Caption Generation Model
Part VIII: Neural Machine Translation
- Lesson 27: Neural Machine Translation
- Lesson 28: Encoder-Decoder Models for NMT
- Lesson 29: Configure Encoder-Decoder Models for NMT
- Lesson 30: How to Develop a Neural Machine Translation Model
You can see that each part targets a specific learning outcome, and so does each tutorial within each part. This acts as a filter to ensure you are only focused on the things you need to know to get to a specific result and do not get bogged down in the math or near-infinite number of configuration parameters.
The tutorials were not designed to teach you everything there is to know about each of the techniques or natural language processing problems. They were designed to give you an understanding of how they work, how to use them on your projects the fastest way I know how: to learn by doing.
Table of Contents
The screenshot below was taken from the PDF Ebook. It provides you a full overview of the table of contents from the book.
Develop Practical Skills for NLP That You Can Immediately Apply
Discover 4 Different NLP Applications
You will work through 4 different neural natural language processing applications.
- Neural Text Classification. Develop a deep learning model to classify the sentiment of movie reviews as either positive or negative.
- Neural Language Modeling. Develop a neural language model on the text of Plato in order to generate new tracts of text with the same style and flavor as the original.
- Neural Photo Captioning. Develop a model to automatically generate a concise description of ad hoc photographs.
- Neural Machine Translation. Develop a model to translate sentences of text in German to English.
Discover 4 Different Neural Network Models
You will develop 4 different types of neural natural language processing models.
- Neural Bag-of-Words. Develop neural network models that model text as a bag-of-words where word order is ignored.
- Neural Word Embedding. Develop neural network models that model text using a distributed representation.
- Embedding + CNN. Develop deep learning models that combine word embedding representations with convolutional neural networks.
- Encoder-Decoder RNN. Develop recurrent neural networks that use the encoder-decoder architecture.
Take a Sneak Peek Inside The Ebook
Click image to Enlarge.
BONUS: Deep Learning NLP Code Recipes
…you also get 99 fully working Python scripts
Sample Code Recipes
Each recipe presented in the book is standalone, meaning that you can copy and paste it into your project and use it immediately.
- You get one Python script (.py) for each example provided in the book.
This means that you can follow along and compare your answers to a known working implementation of each example in the provided Python files.
This helps a lot to speed up your progress when working through the details of a specific task, such as:
- Cleaning text data.
- Framing a problem.
- Developing a model.
- Evaluating a model.
- Making a prediction.
The provided code was developed in a text editor and intended to be run on the command line. No special IDE or notebooks are required.
All code examples were tested with Python 3 and Keras 2.
All code examples will run on modest and modern computer hardware and were executed on a CPU. No GPUs are required to run the presented examples, although a GPU would make the code run faster.
Python Technical Details
This section provides some technical details about the code provided with the book.
- Python Version: You can use Python 3.
- SciPy: You will use NumPy, Pandas and scikit-learn.
- Keras: You will need Keras version 2 with either a Theano or TensorFlow backend.
- Operating System: You can use Windows, Linux or Mac OS X.
- Hardware: A standard modern workstation will do, no GPUs required.
- Editor: You can use a text editor and run the example from the command line.
Don’t have a Python environment?
The appendix contains step-by-step tutorials showing you exactly how to setup a Python deep learning environment.
About The Author
Hi, I'm Jason Brownlee.
I live in Australia with my wife and son and love to write and code.
I have a computer science background as well as a Masters and Ph.D. degree in Artificial Intelligence.
I’ve written books on algorithms, won and ranked in the top 10% in machine learning competitions, consulted for startups and spent a long time working on systems for forecasting tropical cyclones. (yes I have written tons of code that runs operationally)
I get a lot of satisfaction helping developers get started and get really good at machine learning.
I teach an unconventional top-down and results-first approach to machine learning where we start by working through tutorials and problems, then later wade into theory as we need it.
I'm here to help if you ever have any questions. I want you to be awesome at machine learning.
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The book “Machine Learning Algorithms From Scratch” is for programmers that learn by writing code to understand. It provides step-by-step tutorials on how to implement top algorithms as well as how to load data, evaluate models and more. It has less on how the algorithms work, instead focusing exclusively on how to implement each in code.
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My books are playbooks. Not textbooks.
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My books are not for everyone, they are carefully designed for practitioners that need to get results, fast.
Ebooks can be purchased from my website directly.
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Nevertheless, one suggested order for reading the books is as follows:
- Linear Algebra for Machine Learning
- Master Machine Learning Algorithms
- Machine Learning Algorithms From Scratch
- Machine Learning Mastery With Weka
- Machine Learning Mastery With Python
- Machine Learning Mastery With R
- Time Series Forecasting With Python
- XGBoost With Python
- Deep Learning With Python
- Long Short-Term Memory Networks with Python
- Deep Learning for Natural Language Processing
I hope that helps.
Sorry, I do not have a license to purchase my books or bundles for libraries.
The books are for individual use only.
Multi-seat licenses create a bit of a maintenance nightmare for me, sorry. It takes time away from reading, writing and helping my readers.
If you have a big order, such as for a class of students or a large team, please contact me and we will work something out.
My best advice is to start with a book on a topic that you can use immediately.
Baring that, pick a topic that interests you the most.
If you are unsure, perhaps try working through some of the free tutorials to see what area that you gravitate towards.
Generally, I recommend focusing on the process of working through a predictive modeling problem end-to-end:
I have three books that show you how to do this, with three top open source platforms:
- Master Machine Learning With Weka (no programming)
- Master Machine Learning With R (caret)
- Master Machine Learning With Python (pandas and scikit-learn)
These are great places to start.
You can always circle back and pick-up a book on algorithms later to learn more about how specific methods work in greater detail.
Thanks for your interest.
You can see the full catalog of my books and bundles here:
Thanks for asking.
I try not to plan my books too far into the future. I try to write about the topics that I am asked about the most or topics where I see the most misunderstanding.
If you would like me to write more about a topic, I would love to know.
Contact me directly and let me know the topic and even the types of tutorials you would love for me to write.
Contact me and let me know the email address (or email addresses) that you think you used to make purchases.
I can look up what purchases you have made and resend purchase receipts to you so that you can redownload your books and bundles.
All prices are in US Dollars (USD).
All currency conversion is handled by PayPal for PayPal purchases, or by Stripe and your bank for credit card purchases.
It is possible that your link to download your purchase will expire after a few days.
This is a security precaution.
Please contact me and I will resend you purchase receipt with an updated download link.
The book “Deep Learning With Python” could be a prerequisite to”Long Short-Term Memory Networks with Python“. It teaches you how to get started with Keras and how to develop your first MLP, CNN and LSTM.
The book “Long Short-Term Memory Networks with Python” goes deep on LSTMs and teaches you how to prepare data, how to develop a suite of different LSTM architectures, parameter tuning, updating models and more.
The book “Long Short-Term Memory Networks With Python” focuses on how to implement different types of LSTM models.
The book “Deep Learning for Natural Language Processing” focuses on how to use a variety of different networks (including LSTMs) for text prediction problems.
The LSTM book can support the NLP book, but it is not a prerequisite.
Linux, MacOS, and Windows.
There are no code examples in “Master Machine Learning Algorithms“, therefore no programming language is used.
Algorithms are described and their working is summarized using basic arithmetic. The algorithm behavior is also demonstrated in excel spreadsheets, that are available with the book.
It is a great book for learning how algorithms work, without getting side-tracked with theory or programming syntax.
If you are interested in learning about machine learning algorithms by coding them from scratch (using the Python programming language), I would recommend a different book:
All of the books, except one, have been tested and work with Python 3 (e.g. 3.5 or 3.6).
Most of the books have also been tested and work with Python 2.7.
There is one book that required Python 2.7, that is:
This book will be updated for Python 3 soon.
Where possible, I recommend using the latest version of Python 3.
I do test my tutorials and projects on the blog first. It’s like the early access to ideas, and many of them do not make it to my training.
Much of the material in the books appeared in some form on my blog first and is later refined, improved and repackaged into a chapter format. I find this helps greatly with quality and bug fixing.
The books provide a more convenient packaging of the material, including source code, datasets and PDF format. They also include updates for new APIs, new chapters, bug and typo fixing, and direct access to me for all the support and help I can provide.
I believe my books offer thousands of dollars of education for tens of dollars each.
They are months if not years of experience distilled into a few hundred pages of carefully crafted and well-tested tutorials.
I think they are a bargain for professional developers looking to rapidly build skills in applied machine learning or use machine learning on a project.
Also, what are skills in machine learning worth to you? to your next project? and you’re current or next employer?
Nevertheless, the price of my books may appear expensive if you are a student or if you are not used to the high salaries for developers in North America, Australia, UK and similar parts of the world. For that, I am sorry.
I do offer discounts to students, teachers and retirees.
Please contact me to find out more.
I offer a ton of free content on my blog, you can get started with my best free material here:
About my Books
My books are playbooks.
They are intended for developers who want to know how to use a specific library to actually solve problems and deliver value at work.
- My books guide you only through the elements you need to know in order to get results.
- My books are in PDF format and come with code and datasets, specifically designed for you to read and work-through on your computer.
- My books give you direct access to me via email (what other books offer that?)
- My books are a tiny business expense for a professional developer that can be charged to the company and is tax deductible in most regions.
Very few training materials on machine learning are focused on how to get results.
The vast majority are about repeating the same math and theory and ignore the one thing you really care about: how to use the methods on a project.
Comparison to Other Options
Let me provide some context for you on the pricing of the books:
There are free videos on youtube and tutorials on blogs.
- Great, I encourage you to use them, including my own free tutorials.
There are very cheap video courses that teach you one or two tricks with an API.
- My books teach you how to use a library to work through a project end-to-end and deliver value, not just a few tricks
A textbook on machine learning can cost $50 to $100.
- All of my books are cheaper than the average machine learning textbook, and I expect you may be more productive, sooner.
A bootcamp or other in-person training can cost $1000+ dollars and last for days to weeks.
- A bundle of all of my books is far cheaper than this, they allow you to work at your own pace, and the bundle covers more content than the average bootcamp.
Sorry, my books are not available on websites like Amazon.com.
I carefully decided to not put my books on Amazon for a number of reasons:
- Amazon takes 65% of the sale price of self-published books, which would put me out of business.
- Amazon offers very little control over the sales page and shopping cart experience.
- Amazon does not allow me to contact my customers via email and offer direct support and updates.
- Amazon does not allow me to deliver my book to customers as a PDF, the preferred format for my customers to read on the screen.
I hope that helps you understand my rationale.
I am sorry to hear that you’re having difficulty purchasing a book or bundle.
I use Stripe and PayPal services to support secure and encrypted payment processing on my website.
Some common problems when customers have a problem include:
- Perhaps you can double check that your details are correct, just in case of a typo?
- Perhaps you could try a different payment method, such as PayPal or Credit Card?
- Perhaps you’re able to talk to your bank, just in case they blocked the transaction?
I often see customers trying to purchase with a domestic credit card that does not allow international purchases. This is easy to overcome by talking to your bank.
If you’re still having difficulty, please contact me and I can help investigate further.
I give away a lot of content for free. Most of it in fact.
It is important to me to help students and practitioners that are not well off, hence the enormous amount of free content that I provide.
You can access the free content:
I have thought very hard about this and I sell machine learning Ebooks for a few important reasons:
- I use the revenue to support the site and all the non-paying customers.
- I use the revenue to support my family so that I can continue to create content.
- Practitioners that pay for tutorials are far more likely to work through them and learn something.
- I target my books towards working professionals that are more likely to afford the materials.
All updates to the book or books in your purchase are free.
Books are usually updated once every few months to fix bugs, typos and keep abreast of API changes.
Contact me anytime and check if there have been updates. Let me know what version of the book you have (version is listed on the copyright page).
Please contact me anytime with questions about machine learning or the books.
One question at a time please.
Also, each book has a final chapter on getting more help and further reading and points to resources that you can use to get more help.
Yes, the books can help you get a job, but indirectly.
Getting a job is up to you.
It is a matching problem between an organization looking for someone to fill a role and you with your skills and background.
That being said, there are companies that are more interested in the value that you can provide to the business than the degrees that you have. Often, these are smaller companies and start-ups.
You can focus on providing value with machine learning by learning and getting very good at working through predictive modeling problems end-to-end. You can show this skill by developing a machine learning portfolio of completed projects.
My books are specifically designed to help you toward these ends. They teach you exactly how to use open source tools and libraries to get results in a predictive modeling project.
Do you have another question?