Building Transformer Models with Attention
Implementing a Neural Machine Translator from Scratch in Keras
If you have been around long enough, you should notice that your search engine can understand human language much better than in previous years. The game changer was the attention mechanism. It is not an easy topic to explain, and it is sad to see someone consider that as secret magic. If we know more about attention and understand the problem it solves, we can decide if it fits into our project and be more comfortable using it.
If you are interested in natural language processing and want to tap into the most advanced technique in deep learning for NLP, this new Ebook—in the friendly Machine Learning Mastery style that you’re used to—is all you need.
Using clear explanations and step-by-step tutorial lessons, you will learn how attention can get the job done and why we build transformer models to tackle the sequence data. You will also create your own transformer model that translates sentences from one language to another.
About this Ebook:
- Read on all devices: PDF format Ebook, no DRM
- Tons of tutorials: 23 step-by-step lessons, 225 pages
- Foundations: Start from the theoretical background of attention mechanisms, and it will guide you to finish a transformer model
- Hands-on: Instead of using an off-the-shelf model, you implement every nut and bolt so you fully understand what you’re doing
- Working code: More than 50 Python (.py) code files included, in addition to the data file you need
Shows You the Detail of Attention and Transformers.
Designed for Developers. Nothing Hidden.
…another NLP book?
This one is different!
Handling text and human language is a tedious job. Not only is a lot of data cleansing needed, but multiple levels of preprocessing are also required depending on the algorithm you apply. But unarguably, the most challenging part of all natural language processing problems is to find the accurate meaning of words and sentences. It is because human languages are ambiguous, and the sentence structure can be very complex.
This is not an introduction to natural language processing techniques. In fact, before you read this book, you should know some terms on language preprocessing, such as tokenization. The goal of this book is to introduce to you the attention mechanisms that can extract key information from a sequence and show you how a transformer model, in which an attention mechanism is applied, is built and used. There is only one main theme in this book: to make a machine that can translate an English sentence into German.
Why must I know attention?
So, why must I know attention if all I want is to apply deep learning to natural language processing tasks?
You don’t. You can just download a model from some repository and copy over the sample code. You can finish your project without knowing why you should know attention.
However, when you find an issue in the code or discover hundreds of different models with similar names, you will want to know what the code or the model is doing behind the scenes. Understanding the transformer models and the attention mechanisms that power them would allow you to tell why something works and why another doesn’t.
3 Key Ideas You Should Know About Attention
There can be a lot to learn about attention and transformers. But there are three basic questions that you should be able to answer.
1. What are Attention Mechanisms?
When we speak of attention, we talk about a sequence processing problem. We want to read a long sequence as input and produce another sequence as output. Humans often find it difficult to recite a long sentence. All writing guides suggest against using long and complex sentence structures for that reason—readers will not comprehend it. Computers have the same difficulties too. Attention is to relate one word in a sentence (or a token in a sequence) to another so the information or meaning they carry can be correlated. This helps finding the context.
2. How Attention improves Recurrent Neural Networks?
Sequence processing is natural to recurrent neural networks. It is recurrent because we reuse the same neural network for each token in a sequence. Inside the neural network, there is a state to remember what it saw so far, but only in a condensed format. As the sequence becomes longer, it is easy to have this state remember the recent tokens but forget about the older ones. That’s the reason we have limited success in recurrent neural networks once the input is longer. Attention adds another layer on top of it and helps to find the correlation between the states when the network reads different tokens in the sequence. This is to refresh the memory at strategic times. Therefore, an RNN with attention can handle a longer sequence.
3. What is a Transformer?
If attention can be applied to the states of a recurrent neural network, it can also be applied to the input sequences. After all, the output of an attention mechanism is also a sequence. Therefore, we can stack up multiple attention layers and build a neural network out of them without recurrent neural networks. It turns out such a network can work well in many problems, and this architecture is called a transformer model.
Introducing Our New Ebook: Building Transformer Models with Attention
Welcome to Building Transformer Models with Attention!
This book is designed to teach machine learning practitioners like you about transformer models from the ground up. This book is for you if you use some off-the-shelf models and see them working but feel clueless about how attention and transformers can solve your problems.
It starts by giving you a high-level overview of what attention mechanisms are and how people use them. You will learn from the fundamental theory and implement a transformer model line by line in Keras. By the time you finish this book, you will have a working transformer model that can translate English sentences into German.
This book will teach you the inner workings of a transformer model in the fastest and most effective way we know how: to learn by doing. We give you executable code that you can run to develop the intuitions required and that you can copy and paste into your project to immediately get a result. You can even reuse the code on a different dataset to obtain a translator of your favorite languages.
Click to jump straight to the packages.
Who Is This Book For?
…so, is this book right for YOU?
This book is for people who know some Deep Learning and NLP.
Perhaps you have already finished our other book Deep Learning with Python. Perhaps you finished a project with LSTM or other recurrent neural networks. Then, the lessons in this book will guide you to the advanced topic of attention and transformers.
The lessons in this book do assume a few things about you, such as:
- You know basic Python for programming.
- You can comfortably work with your IDE (such as Spyder or Visual Studio Code) or run Python on the command line.
- You know how to develop a model with TensorFlow and Keras.
- You are eager to learn the nuts and bolts of transformer models.
This guide was written in the top-down and results-first style that you’re used to from Machine Learning Mastery.
What if I Am Not Interested in Natural Language Processing?
Transformers and attention are about sequence, not only natural language processing.
Researchers have developed transformer models for computer vision. While the data are fundamentally different, the same idea is applied. Even if you are not interested in NLP problems, you will understand why it can work in other domains.
What if I Am Just Beginning to Learn Natural Language Processing?
Perfect. This book is written for you!
The tutorials in the book do not require sophisticated background knowledge. Following this book and building a translator can be your first project in NLP.
What if I Have Never Used Keras Before?
You don’t need to be an expert in TensorFlow or Keras to read this book. If you still don’t feel comfortable, we have pointers for you to learn the basics of Keras modules, either online or from another book.
You can benefit from this book even if you can barely code. You will know how to learn from other people’s code. You will know how to learn from your own mistakes!
About Your Outcomes
…so what will YOU know after reading this book?
After reading and working through this book, you will know:
- What is an attention mechanism
- How to calculate attention mathematically
- How to implement the attention mechanism into a reusable module in Keras
- What is a transformer
- How we build a transformer encoder and decoder using attention
- How to use a transformer to get the result
You should be able to learn a new idea or two from this book to bring your NLP project to the next level.
After reading this book, you will be able to:
- Explain what attention mechanisms help you
- Tell how to apply a transformer to a sequence of data
- Comfortably use a transformer encoder and/or decoder for your project
What Exactly Is in This Book?
This book was designed to be an advanced book on deep learning. Ideally, you are expected to feel comfortable using a Keras API to build a model from scratch.
There is a lot to do to build a transformer model. You are not going to get lost or distracted. We aim to take you from start to finish to develop a working transformer model that you can reuse in your other deep learning projects. Step-by-step with laser-focused tutorials.
Each tutorial is 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 is intended to be read and used, not to sit idle.
I recommend picking a schedule and sticking to it.
The tutorials are divided into five parts:
- Part 1: Foundations of Attention. A high-level, minimally-technical introduction to attention. You will know what it is about, why it works, and the variations in the implementation.
- Part 2: From Recurrent Neural Networks to Transformer. Start from the traditional recurrent neural network and see its limitations and how attention comes to the rescue. Then we show you why once we have attention, a transformer model can replace a recurrent neural network.
- Part 3: Building a Transformer from Scratch. In multiple steps, you will create the building blocks of a transformer model in Keras. Then you will connect the pieces to build a working transformer with training, testing, and inference.
- Part 4: Applications. There are larger transformer models available. They take a much longer time to train and need much larger datasets, but some of them are available off the shelf. We picked one such model and will show you how to use it to do something in only a few lines of code.
Below is an overview of the 23 step-by-step tutorial lessons you will work through:
Each chapter was designed to be completed in about 30 to 60 minutes by the average developer.
Foundations of Attention
- Chapter 01: What Is Attention?
- Chapter 02: A Bird’s Eye View of Research on Attention
- Chapter 03: A Tour of Attention-Based Architectures
- Chapter 04: The Bahdanau Attention Mechanism
- Chapter 05: The Luong Attention Mechanism
From Recurrent Neural Networks to Transformer
- Chapter 06: An Introduction to Recurrent Neural Networks
- Chapter 07: Understanding Simple Recurrent Neural Networks in Keras
- Chapter 08: The Attention Mechanism from Scratch
- Chapter 09: Adding a Custom Attention Layer to Recurrent Neural Network in Keras
- Chapter 10: The Transformer Attention Mechanism
- Chapter 11: The Transformer Model
- Chapter 12: The Vision Transformer Model
Building a Transformer from Scratch
- Chapter 13: Positional Encoding in Transformer Models
- Chapter 14: Transformer Positional Encoding Layer in Keras
- Chapter 15: Implementing Scaled Dot-Product Attention in Keras
- Chapter 16: Implementing Multi-Head Attention in Keras
- Chapter 17: Implementing the Transformer Encoder in Keras
- Chapter 18: Implementing the Transformer Decoder in Keras
- Chapter 19: Joining the Transformer Encoder and Decoder with Masking
- Chapter 20: Training the Transformer Model
- Chapter 21: Plotting the Training and Validation Loss Curves for the Transformer Model
- Chapter 22: Inference with the Transformer Model
- Chapter 23: A Brief Introduction to BERT
- Appendix A: How to Setup a Workstation for Python
- Appendix C: How to Setup Amazon EC2 for Deep Learning on GPUs
You can see that each part targets a specific learning outcome and so does each tutorial within each section. This acts as a filter to ensure you are only focused on what you need to know to get to a specific result and do not get bogged down in unrelated objectives.
The tutorials were not designed to teach you everything, nor to have you know everything about each topic. They were designed to help you understand how to get something done, how to use a tool, and how to see the results 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 with a full overview of the table of contents from the book.
Take a Sneak Peek Inside the Ebook
Click image to Enlarge.
Download Your Sample Chapter
Do you want to take a closer look at the book? Download a free sample chapter PDF.
Enter your email address, and your sample chapter will be sent to your inbox.
BONUS: Python Code to Do Great Things
…you also get 45 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 you can follow along and compare your answers to a known working implementation of each example in the provided Python files.
This helps to speed up your progress when working through the details of a specific task, such as:
- Generating positional encodings
- Computing scaled dot-product attentions
- Building a transformer encoder
- Building a transformer decoder
- Training a complete transformer model
Most of the provided code was developed in a text editor and is intended to be run on the command line. No special IDE or notebooks are required.
All code examples were designed and tested with Python 3.9+.
All code examples will run on modest and modern computer hardware and were executed on a CPU.
Python Technical Details
This section provides some technical details about the code provided in the book.
- Python Version: You can use Python 3.6 or higher, but Python 3.9 is recommended.
- Operating System: You can use Windows, Linux, or Mac OS X.
- Hardware: A standard modern workstation will do.
- 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 set up a Python machine learning environment.
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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 industry is demanding skills in machine learning.
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Business knows what these skills are worth and are paying sky-high starting salaries.
A Data Scientists Salary Begins at:
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I recommend using standalone Keras version 2.4 (or higher) running on top of TensorFlow version 2.2 (or higher).
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There is one case of tutorials that do not support TensorFlow 2 because the tutorials make use of third-party libraries that have not yet been updated to support TensorFlow 2. Specifically tutorials that use Mask-RCNN for object recognition. Once the third party library has been updated, these tutorials too will be updated.
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.
- Short, typically 7 days or 14 days in length.
- 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.
- Longer, typically 25+ complete tutorial lessons, each taking up to an hour to complete.
- Complete, providing a gentle introduction into each lesson and includes full working code and further reading.
- 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.
A code file is provided for each example presented in the book.
Dataset files used in each chapter are also provided with the book.
The code and dataset files are provided as part of your .zip download in a code/ subdirectory. Code and datasets are organized into subdirectories, one for each chapter that has a code example.
If you have misplaced your .zip download, you can contact me and I can send an updated purchase receipt email with a link to download your package.
Ebooks can be purchased from my website directly.
- First, find the book or bundle that you wish to purchase, you can see the full catalog here:
- Click on the book or bundle that you would like to purchase to go to the book’s details page.
- Click the “Buy Now” button for the book or bundle to go to the shopping cart page.
- Fill in the shopping cart with your details and payment details, and click the “Place Order” button.
- After completing the purchase you will be emailed a link to download your book or bundle.
All prices are in US dollars (USD).
Books can be purchased with PayPal or Credit Card.
All prices on Machine Learning Mastery are in US dollars.
Payments can be made by using either PayPal or a Credit Card that supports international payments (e.g. most credit cards).
You do not have to explicitly convert money from your currency to US dollars.
Currency conversion is performed automatically when you make a payment using PayPal or Credit Card.
After filling out and submitting your order form, you will be able to download your purchase immediately.
Your web browser will be redirected to a webpage where you can download your purchase.
You will also receive an email with a link to download your purchase.
If you lose the email or the link in the email expires, contact me and I will resend the purchase receipt email with an updated download link.
After you complete your purchase you will receive an email with a link to download your bundle.
The download will include the book or books and any bonus material.
To use a discount code, also called an offer code, or discount coupon when making a purchase, follow these steps:
1. Enter the discount code text into the field named “Discount Coupon” on the checkout page.
Note, if you don’t see a field called “Discount Coupon” on the checkout page, it means that that product does not support discounts.
2. Click the “Apply” button.
3. You will then see a message that the discount was applied successfully to your order.
Note, if the discount code that you used is no longer valid, you will see a message that the discount was not successfully applied to your order.
There are no physical books, therefore no shipping is required.
All books are EBooks that you can download immediately after you complete your purchase.
I recommend reading one chapter per day.
Momentum is important.
Some readers finish a book in a weekend.
Most readers finish a book in a few weeks by working through it during nights and weekends.
You will get your book immediately.
After you complete and submit the payment form, you will be immediately redirected to a webpage with a link to download your purchase.
You will also immediately be sent an email with a link to download your purchase.
What order should you read the books?
That is a great question, my best suggestions are as follows:
- Consider starting with a book on a topic that you are most excited about.
- Consider starting with a book on a topic that you can apply on a project immediately.
Also, consider that you don’t need to read all of the books, perhaps a subset of the books will get you the skills you need or want.
Nevertheless, one suggested order for reading the books is as follows:
- Probability for Machine Learning
- Statistical Methods for Machine Learning
- Linear Algebra for Machine Learning
- Optimization for Machine Learning
- Calculus for Machine Learning
- Master Machine Learning Algorithms
- Machine Learning Algorithms From Scratch
- Python for Machine Learning
- Machine Learning Mastery With Weka
- Machine Learning Mastery With Python
- Machine Learning Mastery With R
- Data Preparation for Machine Learning
- Imbalanced Classification With Python
- Time Series Forecasting With Python
- Ensemble Learning Algorithms With Python
- XGBoost With Python
- Deep Learning With Python
- Deep Learning with PyTorch
- 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
- Generative Adversarial Networks with Python
- Building Transformer Models with Attention
- Productivity with ChatGPT (this book can be read in any order)
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.
I update the books frequently and you can access the latest version of a book at any time.
In order to get the latest version of a book, contact me directly with your order number or purchase email address and I can resend your purchase receipt email with an updated download link.
I do not maintain a public change log or errata for the changes in the book, sorry.
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 operated out of Puerto Rico.
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 a Company Number. The details are as follows:
- Company Name: Zeus LLC
- Company Number: 421867-1511
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 for Credit Card 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 or debit 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.
When you purchase a book from my website and later review your bank statement, it is possible that you may see an additional small charge of one or two dollars.
The charge does not come from my website or payment processor.
Instead, the charge was added by your bank, credit card company, or financial institution. It may be because your bank adds an additional charge for online or international transactions.
This is rare but I have seen this happen once or twice before, often with credit cards used by enterprise or large corporate institutions.
My advice is to contact your bank or financial institution directly and ask them to explain the cause of the additional charge.
If you would like a copy of the payment transaction from my side (e.g. a screenshot from the payment processor), or a PDF tax invoice, please contact me directly.
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.