Building Transformer Models From Scratch with PyTorch
Attention Mechanisms to Language Models
Transformer models have revolutionized artificial intelligence, powering everything from ChatGPT to video generation. But how do these powerful models actually work? Despite their impact, transformers aren’t as complicated as they seem. They’re built from a few core components, and the architecture remains consistent across both small and large models. By understanding these building blocks, you can see why one model behaves differently from another.
You could dive into the research papers or explore the code, but nothing teaches better than building a transformer from scratch. While training massive commercial-scale systems requires huge resources, you can grasp all the essential concepts by creating smaller yet functional models. Through step-by-step guidance, you’ll learn each component — tokenizers, embeddings, attention mechanisms, normalization layers — and how they fit together. This ebook takes you on a hands-on journey, showing you how to build transformers piece by piece, so you gain real insight into the architecture driving today’s AI revolution. After purchasing, you will receive:
- Read on all devices: English PDF format EBook, no DRM.
- Comprehensive coverage: 17 chapters + 2 appendices, 200 pages.
- Practical implementation: Complete PyTorch code for building transformer models from scratch.
- Step-by-step guidance: Build working translation and text generation models, starting from the most fundamental.
- Modern techniques: Learn recent developments like YaRN, Grouped-Query Attention, Mixture-of-Experts, and more.
You Can Be a Master of Transformer Architectures!
Convinced?
How Can You Understand the Power of Transformer Models?
…by building one, starting from attention to autoregressive generation.
Transformer models have proven to be a powerful tool for natural language processing and have since found their way into other domains, including image, audio, and video applications. It is the cornerstone for generative AIs. To truly understand how these models work, rather than using them as black boxes, you need to comprehend the details of the model architecture.
The thesis of this book is not about practical applications of transformer models. Rather, it is to list out the building blocks and show you how they interact with each other to form the complete transformer model.
Here are some examples of what you will learn:
- The different attention mechanisms, including MHA, MQA, GQA, and MLA.
- The mixture-of-expert module is a drop-in replacement of the feed-forward network module.
- The pros and cons of pre-norm architecture compared to post-norm architecture.
- How transformer models are used autoregressively for generation.
Creating a large language model might seem daunting. So how should you start?
Go Above and Beyond on Transformer Models
…the key is “Learning by Doing”
Go above and beyond with Building Transformer Models from Scratch with PyTorch by learning each and every building block in a modern transformer model. You might wonder if such a powerful model has numerous moving parts. In reality, you only need to know a handful of building blocks and some variations of each. The key lies in understanding how and why such blocks exist within the larger model and the role they play.
Here’s The WRONG WAY To Learn Transformer Models.
Some people might advise you to:
- Read hundreds of academic papers as the history of transformer models is built from them.
- Follow the trend and stay up-to-date with the latest models that are launched every week.
- Understand what those models are proposed for and how people use them.
While these suggestions aren’t entirely wrong, following them could lead to spending excessive time on superficial details without building a solid foundation of knowledge.
This approach is not the BEST WAY
We advocate for a practical, results-oriented approach—focusing on building working models rather than getting lost in theoretical details.
Learning to Think About Transformer Models Effectively
…so here is a simple approach
Transformer models are indeed straightforward to understand. If you read it at the right level, there are only a few building blocks underneath. To build a model is merely designing some key parameters and following a recipe to assemble the building blocks. To understand an open-source model that you found on the Internet is likewise.
Therefore, the key is to grasp the architecture of transformers. Not too many details, nor too many abstractions.
This book guides you through all the necessary details that you need to know about transformer models. By focusing on function blocks and their common variations, you’ll learn how a transformer model works, and you can build your own from scratch on your own computer.
Introducing “Building Transformer Models From Scratch with PyTorch”
…your gateway to building powerful transformer models with minimal effort
This book is designed to help you quickly build knowledge in the inner workings of transformer models, paving the way for you to get involved in more technical projects involving transformers. We take a balanced approach to explaining the theoretical model of each building block using simple equations and its implementation in PyTorch. Through the fully working code, you know how to build a transformer model from the bottom up. Most importantly, you’ll learn how to put your model into practical use.
This book is a step-by-step tutorial to help you create your own mini-LLM, which can deliver amazing results.
Our goal is to provide you with just enough knowledge about transformer models in a short time so that you can understand the design of all other models. By the end, you’ll be able to explain a model from its input to output at the functional level.
Consider this book your guide to understanding the power of modern transformer models. Let’s take a closer look at what you’ll discover inside.
Everything You Need to Know to Start Creating a Transformer Model
You Will Get:
17 Chapters on How a Transformer Model Works
This book is organized into three comprehensive parts, taking you from foundational concepts to building complete transformer models.
These three parts are Overview, Building Blocks of Transformer Models, and Building Complete Models.
- Overview: The first part provides high-level background knowledge about attention mechanisms and transformer architecture, giving you a bird’s eye view of how these models work and the variations in their design.
- Building Blocks of Transformer Models: This comprehensive section breaks down every component of a transformer model in detail, from tokenizers and embeddings to attention mechanisms and normalization layers. You’ll learn both traditional approaches and recent innovations, such as YaRN and Grouped-Query Attention.
- Building Complete Models: The final part brings everything together with hands-on projects. You’ll build complete transformer models for practical applications, including a full transformer for language translation and a decoder-only model for text generation.
Part I: Overview
The first part provides you with the foundational knowledge needed to understand transformer models.
- Chapter 1: Introduction to Attention and Transformer Models
- Chapter 2: Encoders and Decoders in Transformer Models
Part II: Building Blocks of Transformer Models
This extensive section breaks down each component of a transformer model in detail, covering both traditional approaches and recent innovations.
- Chapter 3: Tokenizers in Language Models
- Chapter 4: Word Embeddings in Language Models
- Chapter 5: Positional Encodings in Transformer Models
- Chapter 6: Interpolation in Positional Encodings and Using YaRN for Larger Context Windows
- Chapter 7: Multi-Head Attention and Grouped-Query Attention
- Chapter 8: Multi-Head Latent Attention (MLA)
- Chapter 9: Attention Masking in Transformer Models
- Chapter 10: LayerNorm and RMS Norm in Transformer Models
- Chapter 11: Linear Layers and Activation Functions in Transformer Models
- Chapter 12: Mixture of Experts Architecture in Transformer Models
- Chapter 13: Skip Connections in Transformer Models
Part III: Building Complete Models
The final part brings everything together with practical projects, showing you how to build complete models. It begins with the historical seq2seq architecture, utilizing recurrent neural networks, and transitions into using the transformer architecture.
- Chapter 14: Building a Plain Seq2Seq Model for Language Translation
- Chapter 15: Building a Seq2Seq Model with Attention for Language Translation
- Chapter 16: Building a Transformer Model for Translation
- Chapter 17: Building a Decoder-Only Model for Text Generation
Here’s Everything You’ll Get…
in Building Transformer Models From Scratch with PyTorch
From Attention Mechanisms to Language Models
A digital download that contains everything you need, including:
- Step-by-step tutorials to guide you through installing your Python environment locally or on AWS so you can build your model with the assistance of a GPU.
- Complete source code in Python for all chapters, showing you exactly how to implement various building blocks and transformer models.
- Digital Ebook in PDF format so that you can have the book open side-by-side with your IDE screen while working on your transformer projects.
What More Do You Need?
Take a Sneak Peek Inside The Ebook
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Do you have any Questions?
What Are Skills in Machine Learning Worth?
Your boss asks you:
Hey, can you build a predictive model for this?
Imagine you had the skills and confidence to say:
"YES!"
...and follow through.
I have been there. It feels great!
How much is that worth to you?
The industry is demanding skills in machine learning.
The market wants people that can deliver results, not write academic papers.
Business knows what these skills are worth and are paying sky-high starting salaries.
A Data Scientists Salary Begins at:
$100,000 to $150,000.
A Machine Learning Engineers Salary is Even Higher.
What Are Your Alternatives?
You made it this far.
You're ready to take action.
But, what are your alternatives? What options are there?
(1) A Theoretical Textbook for $100+
...it's boring, math-heavy and you'll probably never finish it.
(2) An On-site Boot Camp for $10,000+
...it's full of young kids, you must travel and it can take months.
(3) A Higher Degree for $100,000+
...it's expensive, takes years, and you'll be an academic.
OR...
For the Hands-On Skills You Get...
And the Speed of Results You See...
And the Low Price You Pay...
Machine Learning Mastery Ebooks are
Amazing Value!
And they work. That's why I offer the money-back guarantee.
You're A Professional
The field moves quickly,
...how long can you wait?
You think you have all the time in the world, but...
- New methods are devised and algorithms change.
- New books get released and prices increase.
- New graduates come along and jobs get filled.
Right Now is the Best Time to make your start.
Bottom-up is Slow and Frustrating,
...don't you want a faster way?
Can you really go on another day, week or month...
- Scraping ideas and code from incomplete posts.
- Skimming theory and insight from short videos.
- Parsing Greek letters from academic textbooks.
Targeted Training is your Shortest Path to a result.
Professionals Stay On Top Of Their Field
Get The Training You Need!
You don't want to fall behind or miss the opportunity.
Frequently Asked Questions
Customer Questions (78)
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This is by design and I put a lot of thought into it. My rationale is as follows:
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My materials are playbooks intended to be open on the computer, next to a text editor and a command line.
They are not textbooks to be read away from the computer.
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I do give away a lot of free material on applied machine learning already.
You can access the best free material here:
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Specifically:
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If you are having trouble finding the table of contents, search the page for the section titled “Table of Contents”.
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If you purchase a book or bundle and later decide that you want to upgrade to the super bundle, I can arrange it for you.
Contact me and let me know that you would like to upgrade and what books or bundles you have already purchased and which email address you used to make the purchases.
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If you use my material to teach, please reference the source, including:
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For example:
- Jason Brownlee, Machine Learning Algorithms in Python, Machine Learning Mastery, Available from https://machinelearningmastery.com/machine-learning-with-python/, accessed April 15th, 2018.
Also, if your work is public, contact me, I’d love to see it out of general interest.
Generally no.
I don’t have exercises or assignments in my books.
I do have end-to-end projects in some of the books, but they are in a tutorial format where I lead you through each step.
The book chapters are written as self-contained tutorials with a specific learning outcome. You will learn how to do something at the end of the tutorial.
Some books have a section titled “Extensions” with ideas for how to modify the code in the tutorial in some advanced ways. They are like self-study exercises.
Sorry, new books are not included in your super bundle.
I release new books every few months and develop a new super bundle at those times.
All existing customers will get early access to new books at a discount price.
Note, that you do get free updates to all of the books in your super bundle. This includes bug fixes, changes to APIs and even new chapters sometimes. I send out an email to customers for major book updates or you can contact me any time and ask for the latest version of a book.
No.
I have books that do not require any skill in programming, for example:
Other books do have code examples in a given programming language.
You must know the basics of the programming language, such as how to install the environment and how to write simple programs. I do not teach programming, I teach machine learning for developers.
You do not need to be a good programmer.
That being said, I do offer tutorials on how to setup your environment efficiently and even crash courses on programming languages for developers that may not be familiar with the given language.
No.
My books do not cover the theory or derivations of machine learning methods.
This is by design.
My books are focused on the practical concern of applied machine learning. Specifically, how algorithms work and how to use them effectively with modern open source tools.
If you are interested in the theory and derivations of equations, I recommend a machine learning textbook. Some good examples of machine learning textbooks that cover theory include:
I generally don’t run sales.
If I do have a special, such as around the launch of a new book, I only offer it to past customers and subscribers on my email list.
I do offer book bundles that offer a discount for a collection of related books.
I do offer a discount to students, teachers, and retirees. Contact me to find out about discounts.
Sorry, I don’t have videos.
I only have tutorial lessons and projects in text format.
This is by design. I used to have video content and I found the completion rate much lower.
I want you to put the material into practice. I have found that text-based tutorials are the best way of achieving this. With text-based tutorials you must read, implement and run the code.
With videos, you are passively watching and not required to take any action. Videos are entertainment or infotainment instead of productive learning and work.
After reading and working through the tutorials you are far more likely to use what you have learned.
Yes, I offer a 90-day no questions asked money-back guarantee.
I stand behind my books. They contain my best knowledge on a specific machine learning topic, and each book as been read, tested and used by tens of thousands of readers.
Nevertheless, if you find that one of my Ebooks is a bad fit for you, I will issue a full refund.
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 support purchases from any country via PayPal or Credit Card.
Yes.
I recommend using standalone Keras version 2.4 (or higher) running on top of TensorFlow version 2.2 (or higher).
All tutorials on the blog have been updated to use standalone Keras running on top of Tensorflow 2.
All books have been updated to use this same combination.
I do not recommend using Keras as part of TensorFlow 2 yet (e.g. tf.keras). It is too new, new things have issues, and I am waiting for the dust to settle. Standalone Keras has been working for years and continues to work extremely well.
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.
Mini-courses are:
- 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.
Ebooks are:
- 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
- The Beginner’s Guide to Data Science
- 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
- Next-Level Data Science
- 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
- NLP with Hugging Face Transformers
- Deep Learning for Computer Vision
- Machine Learning in Open CV
- Deep Learning for Time Series Forecasting
- Better Deep Learning
- Generative Adversarial Networks with Python
- Building Transformer Models with Attention
- Building Transformer Models from Scratch with PyTorch
- Productivity with ChatGPT (this book can be read in any order)
- Mastering Digital Art with Stable Diffusion (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.
Generally, no.
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).
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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
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.
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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.
Discounts
I do offer discounts to students, teachers and retirees.
Please contact me to find out more.
Free Material
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.
Yes.
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).
Yes.
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.