Long Short-Term Memory Networks With Python
Develop Deep Learning Models for your Sequence Prediction Problems
The Long Short-Term Memory network, or LSTM for short, is a type of recurrent neural network that achieves state-of-the-art results on challenging prediction problems.
In this 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 LSTMs.
Using clear explanations, standard Python libraries and step-by-step tutorial lessons you will discover what LSTMs are, and how to develop a suite of LSTM models to get the most out of the method on your sequence prediction problems.
About the Ebook:
- PDF format Ebook.
- 3 parts, 14 step-by-step tutorial lessons, 246 pages.
- 6 LSTM model architectures.
- 45 Python (.py) files.
Clear and Complete Examples.
No Math. Nothing Hidden.
Click to jump straight to the packages.
Sequence Prediction is…important, overlooked, and HARD
Sequence prediction is different to other types of supervised learning problems.
The sequence imposes an order on the observations that must be preserved when training models and making predictions.
There are 4 main types of sequence prediction problems:
1. Sequence Prediction
Given an input sequence, predict the next value in the sequence.
- Weather Forecasting
- Stock Market Prediction
- Product Recommendation
2. Sequence Classification
Given an input sequence, classify the sequence.
- DNA Sequence Classification
- Anomaly Detection
- Sentiment Analysis
3. Sequence Generation
Given an observation, generate an output sequence.
- Text Generation
- Music Generation
- Image Captioning
4. Sequence-to-Sequence Prediction
Given an input sequence, generate an output sequence.
- Multi-Step Time Series Forecasting
- Text Summarization
- Language Translation
Long Short-Term Memory Networks
UNLOCK Sequence Prediction for Deep Learning
Classical neural networks called Multilayer Perceptrons, or MLPs for short, can be applied to sequence prediction problems.
The application of MLPs to sequence prediction requires that the input sequence be divided into smaller overlapping subsequences called windows that are shown to the network in order to generate a prediction.
This can work well on some problems but suffers some critical limitations such as being stateless and having a fixed number of inputs and outputs.
Promise of Recurrent Neural Networks
The Long Short-Term Memory, or LSTM, network is a type of Recurrent Neural Network (RNN) designed for sequence problems.
Given a standard feedforward MLP network, an RNN can be thought of as the addition of loops to the architecture. The recurrent connections add state or memory to the network and allow it to learn and harness the ordered nature of observations within input sequences.
The internal memory means outputs of the network are conditional on the recent context in the input sequence, not what has just been presented as input to the network.
In a sense, this capability unlocks sequence prediction for neural networks and deep learning.
Impressive Applications of LSTMs
We are interested in LSTMs for the elegant solutions they can provide to challenging sequence prediction problems.
Let’s look at 3 examples to give you a snapshot of the results that LSTMs are capable of achieving.
Automatic Image Caption Generation
Automatic image captioning is the task where, given an image, the system must generate a caption that describes the contents of the image.
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.
Automatic Handwriting Generation
This is a task where, given a corpus of handwriting examples, new handwriting for a given word or phrase is generated.
There are a number of RNNs, but it is the LSTM that delivers on the promise of RNNs for sequence prediction. It is why there is so much buzz and application of LSTMs at the moment.
So, how can you get started and get good at using LSTMs fast?
Introducing my new Ebook:
“Long Short-Term Memory Networks With Python“
This is the book I wish I had when I was getting started with LSTMs.
This book was born out of one thought:
What would I teach if I had to get a machine learning practitioner proficient with LSTMs in two weeks?
I had been researching and applying LSTMs for some time and wanted to write something on the topic, but struggled for months on how exactly to present it. The above question crystallized it for me and this whole book came together.
The above motivating question for this book is clarifying. It means that the lessons that I teach are focused only on the topics that you need to know in order to understand (1) what LSTMs are, (2) why we need LSTMs and (3) how to develop LSTM models in Python.
I developed a program to take you on the critical path:
From…a practitioner interested in LSTMs (e.g. you right now).
To…a practitioner that can confidently apply LSTMs (e.g. you after reading the book).
I want you to get proficient with LSTMs as quickly as you can. I want you using LSTMs on your project.
This also means not covering some topics, even topics covered by “everyone else“, like LSTM math.
This book is not for everyone
…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 need to get good at LSTMs fast.
Maybe you want or need to start using LSTMs on your research project or on a project at work. This guide was written to help you do that quickly and efficiently by compressing years worth of knowledge and experience into a laser-focused course of 14 lessons.
The lessons in this book assume a few things about you, such as:
- You know your way around basic Python.
- You know your way around basic NumPy.
- You know your way around basic scikit-learn.
For some bonus points, perhaps some of the below points apply to you (don’t panic if they don’t).
- You may know how to work through a predictive modeling problem.
- You may know a little bit of deep learning.
- You may know a little bit of Keras.
This guide was written in the top-down and results-first machine learning style that you’re used to from Machine Learning Mastery.
This book is not a panacea
…so what will YOU know after reading it?
This book will teach you how to get results as a machine learning practitioner interested in using LSTMs on your project.
After reading and working through this book, you will know:
- What LSTMs are.
- Why LSTMs are important.
- How LSTMs work.
- How to develop a suite of LSTM architectures.
- How to get the most out of your LSTM models.
This book will NOT teach you how to be a research scientist and all the theory behind why LSTMs work. For that, I would recommend good research papers and textbooks. See the Further Reading section at the end of the first lesson for a good starting point.
Exactly What You Need to Know
…14 carefully designed lessons to take you from Beginner to Practitioner
This book was designed to be a 14-day crash course into LSTMs for machine learning practitioners.
There are a lot of things you could learn about LSTMs, from theory to applications to Keras API. My goal is to take you straight to getting results with LSTMs in Keras with 14 laser-focused lessons.
I designed the lessons to focus on the LSTM models and their implementation in the Keras deep learning library. They give you the tools to both rapidly understand each model and apply them to your own sequence prediction problems.
Each of the 14 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 lessons are divided into three parts:
- Part 1: Foundations. The lessons in this section are designed to give you an understanding of how LSTMs work, how to prepare data, and the life-cycle of LSTM models in the Keras library.
- Part 2: Models. The lessons in this section are designed to teach you about the different types of LSTM architectures and how to implement them in Keras.
- Part 3: Advanced. The lessons in this section are designed to teach you how to get the most from your LSTM models.
You can see that these parts provide a theme for the lessons with focus on the different types of LSTM models.
Here is an overview of the 14 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: What are LSTMs.
- Lesson 02: How to Train LSTMs.
- Lesson 03: How to Prepare Data for LSTMs.
- Lesson 04: How to Develop LSTMs in Keras.
- Lesson 05: Models for Sequence Prediction.
Part II. Models
- Lesson 06: How to Develop Vanilla LSTMs.
- Lesson 07: How to Develop Stacked LSTMs.
- Lesson 08: How to Develop CNN LSTMs.
- Lesson 09: How to Develop Encoder-Decoder LSTMs.
- Lesson 10: How to Develop Bidirectional LSTMs.
- Lesson 11: How to Develop Generative LSTMs.
Part III. Advanced
- Lesson 12: How to Diagnose and Tune LSTMs.
- Lesson 13: How to Make Predictions with LSTMs.
- Lesson 14: How to Update LSTM Models.
You can see that each lesson has a targeted learning outcome. 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 not get bogged down in the math or near-infinite number of configuration parameters.
These lessons were not designed to teach you everything there is to know about each of the LSTM models. 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.
Discover 4 Different Sequence Prediction Models
There are 4 main types of sequence prediction models that you need to know.
Each of these model types are presented in the book with code examples showing you how to implement them in Python.
1. One-to-One Model
2. One-to-Many Model
3. Many-to-One Model
4. Many-to-Many Model
Discover 6 Different LSTM Architectures
The LSTM network is the starting point. What you are really interested in is how to use the LSTM to address sequence prediction problems.
The way that the LSTM network is used as layers in sophisticated network architectures. The way that you will get good at applying LSTMs is by knowing about the different useful LSTM networks and how to use them.
The whole middle section of this book focuses on teaching you about the different LSTM architectures.
1. Vanilla LSTM
Memory cells of a single LSTM layer are used in a simple network structure.
2. Stacked LSTM
LSTM layers are stacked one on top of another into deep recurrent neural networks.
3. CNN LSTM
A Convolutional Neural Network is used to learn features in spatial input and the LSTM is used to support a sequence of inputs (e.g. video of images).
4. Encoder-Decoder LSTM
One LSTM network encodes input sequences and a separate LSTM network decodes the encoding into an output sequence.
5. Bidirectional LSTM
Input sequences are presented and learned both forward and backward.
6. Generative LSTM
LSTMs learn the structure relationship in input sequences so well that they can generate new plausible sequences.
Take a Sneak Peek Inside The Ebook
Click image to Enlarge.
BONUS: LSTM RNN Code Recipes
…you also get 45 fully working LSTM 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.
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 2 and Python 3 with 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 2 or 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 run this site and I wrote and published this book.
I live in Australia with my wife and sons. I love to read books, write tutorials, and develop systems.
I have a computer science and software engineering background as well as Masters and PhD degrees in Artificial Intelligence with a focus on stochastic optimization.
I've written books on algorithms, won and ranked well in competitions, consulted for startups, and spent years in industry. (Yes, I have spend a long time building and maintaining REAL operational systems!)
I get a lot of satisfaction helping developers get started and get really good at applied 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 “Long Short-Term Memory Networks with Python” is not focused on time series forecasting, instead, it is focused on the LSTM method for a suite of sequence prediction problems.
The book “Deep Learning for Time Series Forecasting” shows you how to develop MLP, CNN and LSTM models for univariate, multivariate and multi-step time series forecasting problems.
Mini-courses are free courses offered on a range of machine learning topics and made available via email, PDF and blog posts.
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- Terse, typically giving one tip or code snippet per lesson.
- Limited, typically narrow in scope to a few related areas.
Ebooks are provided on many of the same topics providing full training courses on the topics.
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- Broad, covering all of the topics required on the topic to get productive quickly and bring the techniques to your own projects.
The mini-courses are designed for you to get a quick result. If you would like more information or fuller code examples on the topic then you can purchase the related Ebook.
The book “Master Machine Learning Algorithms” is for programmers and non-programmers alike. It teaches you how 10 top machine learning algorithms work, with worked examples in arithmetic, and spreadsheets, not code. The focus is on an understanding on how each model learns and makes predictions.
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.
The two books can support each other.
The books are a concentrated and more convenient version of what I put on the blog.
I design my books to be a combination of lessons and projects to teach you how to use a specific machine learning tool or library and then apply it to real predictive modeling problems.
The books get updated with bug fixes, updates for API changes and the addition of new chapters, and these updates are totally free.
I do put some of the book chapters on the blog as examples, but they are not tied to the surrounding chapters or the narrative that a book offers and do not offer the standalone code files.
With each book, you also get all of the source code files used in the book that you can use as recipes to jump-start your own predictive modeling problems.
My books are playbooks. Not textbooks.
They have no deep explanations of theory, just working examples that are laser-focused on the information that you need to know to bring machine learning to your project.
There is little math, no theory or derivations.
My readers really appreciate the top-down, rather than bottom-up approach used in my material. It is the one aspect I get the most feedback about.
My books are not for everyone, they are carefully designed for practitioners that need to get results, fast.
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Generally, I would recommend starting with the book or topic that most interests you.
Nevertheless, one suggested order for reading the books is as follows:
- Linear Algebra for Machine Learning
- Statistical Methods 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
- Deep Learning for Computer Vision
- Deep Learning for Time Series Forecasting
- Better Deep Learning
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.
There are no physical books, therefore no delivery is required.
All books are Ebooks in PDF format that you can download immediately after you complete your purchase.
You will receive an email with a link to download your purchase. You can also contact me any time to get a new download link.
I support purchases from any country via PayPal or Credit Card.
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.
Both books focus on deep learning in Python using the Keras library.
The book “Long Short-Term Memory Networks in Python” focuses on how to develop a suite of different LSTM networks for sequence prediction, in general.
The book “Deep Learning for Time Series Forecasting” focuses on how to use a suite of different deep learning models (MLPs, CNNs, LSTMs, and hybrids) to address a suite of different time series forecasting problems (univariate, multivariate, multistep and combinations).
The LSTM book teaches LSTMs only and does not focus on time series. The Deep Learning for Time Series book focuses on time series and teaches how to use many different models including LSTMs.
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.
You may need a business or corporate tax number for “Machine Learning Mastery“, the company, for your own tax purposes. This is common in EU companies for example.
The Machine Learning Mastery company is registered and operated out of Australia.
As such, the company does not have a VAT identification number for the EU or similar for your country or regional area.
The company does have an Australian Company Number or ACN. The details are as follows:
- Trading Name: Machine Learning Mastery Pty Ltd
- ACN: 626 223 336
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:
I write the content for the books (words and code) using a text editor, specifically sublime.
I typeset the books and create a PDF using LaTeX.
All of the books 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.
Where possible, I recommend using the latest version of Python 3.
After you fill in the order form and submit it, two things will happen:
- You will be redirected to a webpage where you can download your purchase.
- You will be sent an email (to the email address used in the order form) with a link to download your purchase.
The redirect in the browser and the email will happen immediately after you complete the purchase.
You can download your purchase from either the webpage or the email.
If you cannot find the email, perhaps check other email folders, such as the “spam” folder?
If you have any concerns, contact me and I can resend your purchase receipt email with the download link.
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?