Better Deep Learning
Train Faster, Reduce Overfitting, and Make Better Predictions
Deep learning neural networks have become easy to define and fit, but are still hard to configure.
In this new eBook written in the friendly Machine Learning Mastery style that you’re used to, discover exactly how to improve the performance of deep learning neural network models on your predictive modeling projects.
With clear explanations, standard Python libraries, and step-by-step tutorial lessons, you’ll discover how to better train your models, reduce overfitting, and make more accurate predictions.
About this Ebook:
- Read on all devices: PDF format Ebook, no DRM.
- Tons of tutorials: 26 step-by-step lessons, 575 pages.
- Expert knowledge: quotes from research papers and books.
- Concrete tutorials: step-by-step tutorial projects.
- Working code: 118 Python (.py) code files included.
Clear, Complete End-to-End Examples.
Click to jump straight to the packages.
…the great challenge in using neural networks!
Deep Learning Models are EASY to Define but HARD to Configure.
The Neural Network Renaissance…
Historically, neural network models had to be coded from scratch.
You might spend days or weeks translating poorly described mathematics into code and days or weeks more debugging your code just to get a simple neural network model to run.
Those days are in the past. Today, you can define and begin fitting most types of neural networks in minutes with just a few lines of code, thanks to open source libraries such as Keras built on top of sophisticated mathematical libraries such as TensorFlow.
As deep learning practitioners, we live in amazing and productive times.
Nevertheless, even though new neural network models can be defined and evaluated rapidly, there remains little guidance on how to actually configure neural network models in order to get the most out of them.
The Challenge of Configuring Neural Networks…
Configuring neural network models is often referred to as a “dark art.”
This is because there are no hard and fast rules for configuring a network for a given problem. We cannot analytically calculate the optimal model type or model configuration for a given dataset.
Instead, there are decades’ worth of techniques, heuristics, tips, tricks, and other tacit knowledge spread across code, papers, blog posts, and in peoples heads.
Fortunately, there are techniques that are known to address specific issues when configuring and training a neural network that are available in modern deep learning libraries like Keras.
Further, discoveries have been made in the past decade in areas such as activation functions, adaptive learning rates, regularization methods, and ensemble techniques that have been shown to dramatically improve the performance of neural network models regardless of their specific type.
The techniques are available; you just need to know what they are and when to use them.
A Framework for Better Deep Learning Performance
Unfortunately, you cannot simply grid search across the techniques used to improve deep learning performance.
Instead, you must diagnose the type of performance problem you are having with your model, then carefully choose and evaluate a given intervention tailored to that diagnosed problem.
There are three types of problems that are straightforward to diagnose with regard to poor performance of a deep learning neural network model; they are:
- Problems With Learning. (e.g. bad performance or getting stuck)
- Problems With Generalization. (e.g. overfitting or bad test set performance)
- Problems With Predictions. (e.g. high variance in the final model)
This breakdown provides a systematic approach to thinking about the performance of your deep learning model.
We can summarize techniques that assist with each of these problems as follows:
- Better Learning. Techniques that improve or accelerate the adaptation of neural network model weights in response to a training dataset.
- Better Generalization. Techniques that improve the performance of a neural network model on a holdout dataset.
- Better Predictions. Techniques that reduce the variance in the performance of a final model.
So what are the techniques? (…and how do you use them?)
Introducing: “Better Deep Learning“
This book was designed to show you exactly how to improve the performance of your deep learning models.
In writing this book, I imagined that you have developed a deep learning model for a predictive modeling problem and you are encountering a problem with training, overfitting, or predictive performance. The chapters were designed to walk you through the process of diagnosing the issue with your model and showing you a suite of techniques that you can use to address and get better performance. After introducing each method, I hammer them home with step-by-step case studies showing you exactly the API calls to make and the configuration parameters to use.
All examples are demonstrated with simple Multilayer Perceptron models with simple regression and classification predictive modeling problems.
The focus on getting better performance with deep learning small and well understood techniques and datasets means that we do not get distracted with complicated model configurations or data preparation schemes. Instead, we can focus directly on the techniques to improve model performance that you can transplant directly into your project using MLPs, CNNs, RNNs, and other modern deep learning model architectures.
In addition to providing a playbook to show you how to get better performance from your deep learning models, I also designed the book as a type of look-up method for modern techniques. This includes quotes and references to research papers and books that explain the technique, best practices, as well as examples of specific configurations used in published models. It is designed to be a one-stop-shop for the techniques that you must know about in order to get the most out of neural network models on your predictive modeling problems.
Develop practical skills for improving deep learning model performance
that you can apply immediately.
You will discover five areas of focus to ensure your learning algorithm is well configured:
- Model Capacity, that defines the scope of functions that the model can learn.
- Batch Size, that defines the number of samples used to estimate the error gradient.
- Loss Function, that defines how the model error is calculated.
- Learning Rate, that controls how much the model parameters are updated each iteration.
- Data Scaling, that controls the way the problem is perceived by the model.
You will discover seven ensemble learning techniques designed to reduce the variance in the final model and improve predictive performance:
- Model Average Ensemble, that combines the predictions from multiple models.
- Weighted Average Ensemble, that weighs contributions from multiple models by their level of trust.
- Resampling Ensemble, that combines models trained on different subsets of the training dataset (e.g. bootstrap aggregation or bagging).
- Horizontal Ensemble, that combines predictions from multiple models from a contiguous block of epochs a single training run.
- Snapshot Ensemble, that combines predictions from models saved across a training run that uses an aggressive cyclical learning rate schedule.
- Stacking Generalization Ensemble, that trains a new model to learn how to best combine the predictions from multiple models.
- Average Model Weight Ensemble, that combines the model parameters (weights) from multiple models.
You’ll discover two methods to address the main challenges to stable learning:
- Vanishing gradients, the problem that some layers are not updated during training.
- Exploding gradients, the problem that large updates to the model cause a numerical overflow.
You’ll discover three methods to learn a training dataset faster:
- Batch Normalization, that can dramatically accelerate training.
- Greedy Layer-Wise Pretraining, a milestone that facilitated the training of very deep models.
- Transfer Learning, that allows a problem to benefit from training on a related dataset.
You will discover six techniques designed to reduce the overfitting of the training dataset and improve the model’s ability to generalize:
- Weight Regularization, that penalizes a model based on its complexity.
- Activity Regularization, that penalizes model output based on its complexity.
- Weight Constraints, that force a lower complexity model.
- Dropout, that encourages a decoupling of nodes across layers.
- Noise, that encourages the model to be robust to variations in the input.
- Early Stopping, that stops training as soon as performance begins to degrade.
…so is this book right for YOU?
Who Is This Book For?
Let’s make sure you are in the right place.
This book is for developers that know some applied machine learning and some deep learning. This is not a beginner’s book.
Maybe you want or need to start using deep learning 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 of knowledge and experience into a laser-focused course of hands-on tutorials.
This guide was written in the top-down and results-first style that you’re used to from Machine Learning Mastery.
The lessons in this book assume a few things about you.
You need to know:
- You need to know the basics of Python programming.
- You need to know the basics of NumPy array manipulation.
- You need to know the basics of Keras deep learning.
You do NOT need to know:
- You do not need to be a math wiz!
- You do not need to be a deep learning expert!
- You do not need to be an academic researcher!
…so what will YOU know after reading it?
About Your Learning Outcomes
This book will teach you how to get results.
After reading and working through this book,
you will know:
- A checklist of techniques that you can use to improve the performance of deep learning neural network models on your own predictive modeling problems.
- How to accelerate learning through better configured stochastic gradient descent batch size, loss functions, learning rates, and to avoid exploding gradients via gradient clipping.
- How to accelerate learning through correct data scaling, batch normalization, and use of modern activation functions such as the rectified linear activation function.
- How to accelerate learning through choosing better initial weights with greedy layer-wise pretraining and transfer learning.
- A gentle introduction to the problem of overfitting and a tour of regularization techniques.
- How to reduce overfitting by updating the loss function using techniques such as weight regularization, weight constraints, and activation regularization.
- How to reduce overfitting using techniques such as dropout, the addition of noise, and early stopping.
- A gentle introduction to how to combine the predictions from multiple models and a tour of ensemble learning techniques.
- How to combine the predictions from multiple different models using techniques such as weighted averaging ensembles and stacked generalization ensembles, also known as blending.
- How to combine the predictions from multiple models saved during a single training run with techniques such as horizontal ensembles and snapshot ensembles.
This book will NOT teach you how to be a research scientist and all the theory behind why specific methods work. It will teach you how to get better results and deliver value on your predictive modeling projects.
This new understanding of applied deep learning methods will impact your practice of working through predictive modeling problems in the following ways:
- Confidently diagnose poor model training and problems such as premature convergence and accelerate the model training process using one or a combination of modifications to the learning algorithm.
- Confidently diagnose cases of overfitting the training dataset and reduce generalization error using one or a combination of modifications of the model, loss function, or learning algorithm.
- Confidently diagnose high variance in a final model and improve the average predictive skill by combining the predictions from multiple models trained over a single or multiple training runs.
This book is not a substitute for an undergraduate course in deep learning, nor is it a textbook for such courses, although it could be a useful complement. For a good list of top courses, textbooks, and other resources, see the Further Reading section at the end of each tutorial lesson.
… so what is in the Ebook?
26 Step-by-Step Tutorials to Transform you into a
Formidable Deep Learning Practitioner
This book was designed around three main activities for getting better results with deep learning models: better or faster learning, better generalization to new data, and better predictions when using final models.
There are a lot of things you could learn about getting better results from neural network models, from theory to applications to APIs. My goal is to take you straight to getting results with laser-focused tutorials.
I designed the tutorials to focus on how to get things done. They give you the tools to both rapidly understand and apply each technique to your own predictive modeling prediction problems.
Each of the tutorials 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 is intended to be read and used, not to sit idle. I would recommend picking a schedule and sticking to it.
The tutorials are divided into three parts:
- Part 1: Better Learning. Discover the techniques to improve and accelerate the process used to learn or optimize the weights of a neural network model.
- Part 2: Better Generalization. Discover the techniques to reduce overfitting of the training dataset and improve the generalization of models on new data.
- Part 3: Better Predictions. Discover the techniques to improve the performance of final models when used to make predictions on new data.
Table of Contents
Below is an overview of the 26 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.
- I. Introduction
- II. Better Deep Learning Framework
- II. Diagnostic Learning Curves
Part 1: Better Learning
- Lesson 01: Improve Learning by Understanding Optimization
- Lesson 02: Configure Capacity With Nodes and Layers
- Lesson 03: Configure Gradient Precision With Batch Size
- Lesson 04: Configure What to Optimize With Loss Functions
- Lesson 05: Configure Speed of Learning With Learning Rate
- Lesson 06: Stabilize Learning With Data Scaling
- Lesson 07: Fix Vanishing Gradients With ReLU
- Lesson 08: Fix Exploding Gradients With Gradient Clipping
- Lesson 09: Accelerate Learning With Batch Normalization
- Lesson 10: Deeper Models With Greedy Layer-Wise Pretraining
- Lesson 11: Jump-Start Training With Transfer Learning
Part 2: Better Generalization
- Lesson 12: Fix Overfitting With Regularization
- Lesson 13: Penalize Large Weights With Weight Regularization
- Lesson 14: Penalize Large Activity With Activity Regularization
- Lesson 15: Force Small Weights With Weight Constraints
- Lesson 16: Decouple Layers With Dropout
- Lesson 17: Promote Robustness With Noise
- Lesson 18: End on a High Note With Early Stopping
Part 3: Better Predictions
- Lesson 19: Reduce Model Variance With Ensembles Learning
- Lesson 20: Combine Models From Multiple Runs With Model Averaging Ensemble
- Lesson 21: Contribute Proportional to Trust With Weighted Average Ensemble
- Lesson 22: Fit Models on Different Samples With Resampling Ensembles
- Lesson 23: Models from Contiguous Epochs With Horizontal Voting Ensembles
- Lesson 24: Cyclic Learning Rate and Snapshot Ensembles
- Lesson 25: Learn to Combine Predictions With Stacked Generalization Ensemble
- Lesson 26: Combine Model Parameters With Average Model Weights Ensemble
- Appendix A: Getting Help
- Appendix B: How to Set Up Your Workstation
- I. Conclusions
You can see that each part targets a specific approach to improving model performance, and each tutorial targets a specific learning outcome for a technique. This acts as a filter to ensure you are only focused on the things you need to know to get to a specific result and do not get bogged down in the math or near-infinite number of configuration parameters.
The tutorials were not designed to teach you everything there is to know about each of the techniques. They were designed to give you an understanding of how they work and how to use them on your projects the fastest way I know how: to learn by doing.
Take a Sneak Peek Inside The Ebook
Click image to Enlarge.
…you’ll also get 118 fully working Python scripts
BONUS: Better Deep Learning Code Recipes
Sample Code Recipes
Each recipe presented in the book is standalone, meaning that you can copy and paste it into your project and use it immediately.
- You get one Python script (.py) for each example provided in the book.
This means that you can follow along and compare your answers to a known working implementation of each example in the provided Python files.
This helps a lot to speed up your progress when working through the details of a specific task, such as:
- Configuring the learning rate.
- Adding noise.
- Using batch normalization.
- Using weight regularization.
- Configuring dropout.
- Configuring early stopping.
- Using model average ensembles.
- Using stacking ensembles.
- Using snapsthot ensembles.
The provided code was developed in a text editor and intended to be run on the command line. No special IDE or notebooks are required.
All code examples were tested with Python 3 and Keras 2.
All code examples will run on modest and modern computer hardware and were executed on a CPU. No GPUs are required to run the presented examples, although a GPU would make the code run faster.
Python Technical Details
This section provides some technical details about the code provided with the book.
- Python Version: You can use Python 3.
- SciPy: You will use NumPy, Pandas and scikit-learn.
- Keras: You will need Keras version 2 with either a Theano or TensorFlow backend.
- Operating System: You can use Windows, Linux or Mac OS X.
- Hardware: A standard modern workstation will do, no GPUs required.
- Editor: You can use a text editor and run the example from the command line.
Don’t have a Python environment?
The appendix contains step-by-step tutorials showing you exactly how to setup a Python deep learning environment.
About The Author
Hi, I'm Jason Brownlee. I 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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Are you a Student, Teacher or Retiree?
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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:
...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.
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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...
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Targeted Training is your Shortest Path to a result.
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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.
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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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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.
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 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.
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.
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 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.
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.