Deep Learning for Computer Vision
Image Classification, Object Detection, and Face Recognition in Python
Deep learning methods can achieve state-of-the-art results on challenging computer vision problems such as image classification, object detection, and face recognition.
In this new Ebook written in the friendly Machine Learning Mastery style that you’re used to, skip the math and jump straight to getting results.
With clear explanations, standard Python libraries (Keras and TensorFlow 2), and step-by-step tutorial lessons, you’ll discover how to develop deep learning models for your own computer vision projects.
About this Ebook:
- Read on all devices: PDF format Ebook, no DRM.
- Tons of tutorials: 30 step-by-step lessons, 563 pages.
- Foundations: intuitions behind convolutions, pooling, more.
- Real-world projects: detect objects, recognize faces, more.
- Working code: 158 Python (.py) code files included.
…why deep learning?
Traditionally, Computer Vision is REALLY HARD
We are awash in images: photographs, videos, YouTube, Instagram, and increasingly from live video.
Computer Vision, often shortened to CV, is defined as a field of study that seeks to develop techniques to help computers “see” and understand the content of digital images such as photographs and videos.
The problem of computer vision appears simple because it is trivially solved by people, even children.
Helping computers to see turns out to be very hard.
One reason is that we don’t have a strong grasp of how human vision works.
Another reason why it is such a challenging problem is the complexity inherent in the visual world.
A true vision system must be able to see in any of an infinite number of scenes and still extract something meaningful.
UNLOCK Computer Vision With Deep Learning
The Promise of Deep Learning for Computer Vision
Deep learning methods are popular, primarily because they are delivering on their promise.
Some of the first large demonstrations of the power of deep learning were in computer vision, specifically image recognition. More recently in object detection and face recognition.
The five promises of deep learning for computer vision are as follows:
- The Promise of Automatic Feature Extraction. Features can be automatically learned and extracted from raw image data.
- The Promise of End-to-End Models. Single end-to-end models can replace pipelines of specialized models.
- The Promise of Model Reuse. Learned features and even entire models can be reused across related tasks.
- The Promise of Superior Performance. Techniques demonstrate better skill than classical methods on challenging tasks.
- The Promise of General Method. A single general method (e.g. convolutional neural networks) can be used on a range of related tasks.
Impressive Applications of Deep Learning
Computer vision is not “solved” but deep learning is required to get you to the state-of-the-art on many challenging problems in the field.
Deep learning methods are delivering on their promise in computer vision.
Let’s look at three examples to give you a snapshot of the results that deep learning is capable of achieving in the field of computer vision:
1) Automatic Object Detection.
Object detection is the task where, given a photograph of a scene, the system must locate, draw a bounding box, and classify each object.
2) Automatic Face Recognition.
Face recognition is the task where, given a photograph of one or more people, the system must either identify the people in the photograph based on their face or verify that the person in the photograph is who they claim to be.
3) Automatic Image Classification
Image classification is the task where, given a photograph of an object, the system must classify the photograph into one or more known categories.
- Deep learning models can trivially classify photos of dogs and cats with 99% accuracy, a previously unsolved problem.
- Deep learning object detection tasks are now so good and so fast that they can be used on real-time video.
- Deep learning face recognition models can now outperform humans on the same tasks.
You can see that developing systems capable of these tasks would be valuable in a wide range of domains and industries.
So, how can you get started and get good at using deep learning for computer vision fast?
This is the book I wish I had when I was getting started with deep learning for visual recognition.
This book was born out of one thought:
How can I get you proficient with deep learning for computer vision as fast as possible?
The Machine Learning Mastery method suggests that the best way of learning this material is by doing. This means the focus of the book is hands-on with projects and tutorials. This also means not covering some topics, even topics covered by “everyone else” like DSP theory or modeling math.
This book was designed to teach you step-by-step how to bring modern deep learning methods to your computer vision projects.
You will be led along the critical path from a practitioner interested in computer vision to a practitioner that can confidently apply deep learning methods to computer vision problems.
This is the fastest process that I can devise for getting you proficient with deep learning for computer vision.
Develop Real Practical Skills That You Can Apply Immediately, such as:
Image Data Preparation
- Standard Libraries. Discover how to load and handle image data using PIL/Pillow.
- Keras Image Handling. Discover how to handle image data using the Keras deep learning library.
- Scale Pixels. Discover how to normalize and standardize pixel data.
- Load Large Datasets. Discover how to progressively load large image datasets from file.
- Image Augmentation. Discover how to use image data augmentation to improve model performance.
Convolutions and Pooling
- Channel Ordering. Discover intuitions behind channels-first and last ordering and how to change the ordering.
- Convolutional Layers. Discover intuitions behind convolutional layers and how filters work.
- Padding and Stride. Discover intuitions behind stride, the effect of filter size and how to fix border effects with padding.
- Pooling Layers. Discover intuitions behind pooling and how average, max, and global pooling works.
Convolutional Neural Networks
- ImageNet. Discover the ImageNet dataset and ILSVRC competition and the impressive results it has promoted.
- Architectural Innovations. Discover the key model architectural innovations such as Inception and ResNet.
- Code Architectures. Discover how to code key model architectural innovations from scratch.
- 1×1 Convolutions. Discover the intuitions behind the 1×1 convolution and how to use it to manage model complexity.
- Pre-Trained Models. Discover the benefit behind using pre-trained models and how they can be used for transfer learning.
- From Scratch. Discover how to develop image classification models from scratch for benchmark datasets.
- Model Regularization: Discover how to add regularization methods like dropout and data augmentation to reduce overfitting and lift model performance.
- Pre-Trained Models. Discover how to harness world-class pre-trained models to accelerate learning on new problems.
- Dogs vs Cats. Develop a top-performing model to classify photographs of dogs and cats.
- Amazon Rainforest. Develop a top-performing model to label aerial photographs of the Amazon rainforest.
- Object Recognition. Discover the field of object recognition and the subproblems of localization and detection.
- R-CNN. Discover the region-based convolutional neural network model and how to use a pre-trained model for object detection.
- YOLO. Discover the you-only-look-once convolutional neural network model and how to use a pre-trained model for object detection.
- Kangaroo Detection. Discover how to develop, train, evaluate and use an object detection model to locate and detect kangaroos in photographs.
- Face Detection. Discover the problem of face detection and how to use the MTCNN model to detect faces in photographs.
- VGGFace2. Discover the top-performing VGGFace2 model from Oxford and how to use it for face verification and face identification.
- FaceNet. Discover the top-performing FaceNet model from Google and how to use it for face verification and face identification.
…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.
Maybe you want or need to start using deep learning for visual recognition on your research project or on a project at work. This book was written to help you do that quickly and efficiently by compressing years of knowledge and experience into a laser-focused course of hands-on tutorials.
This guide was written in the top-down and results-first style that you’re used to from Machine Learning Mastery.
The lessons in this book assume a few things about you.
You need to know:
- You need to know your way around basic Python.
- You may know a little of basic modeling with scikit-learn.
- You may know a little of basic modeling with Keras.
You do NOT need to be:
- You do not need to be a math wiz!
- You do not need to be a deep learning expert!
- You do not need to be a master of computer vision!
…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:
- The promise of neural networks and deep learning methods in general for computer vision problems.
- How to load and prepare image data, such as photographs, for modeling using best-of-breed Python libraries.
- How specialized layers in a convolutional layer work, including 1D and 2D convolutions, max and average pooling, and intuitions for the impact that each layer has on input data.
- How to configure convolutional layers, including aspects such as filter size, stride, and pooling.
- How key modeling innovations for convolutional neural networks work and how to implement them from scratch, such as VGG blocks, inception models, and ResNet modules.
- How to develop, tune, evaluate, and make predictions with convolutional neural networks on standard benchmark computer vision datasets for image classification, such as Fashion MNIST and CIFAR-10.
- How to develop, tune, evaluate, and make predictions with convolutional neural networks on entirely new datasets for image classification, such as satellite photographs and photographs of pets.
- How to use techniques such as pre-trained models, transfer learning, and image augmentation to accelerate and improve model development.
- How to use pre-trained models and develop new models for object recognition tasks, such as object localization and object detection in photographs, using techniques like Mask R-CNN and YOLOv3.
- How to use deep learning models for face recognition tasks, such as face identification and face verification in photographs, using techniques like Google’s FaceNet and Oxford’s VGGFace2.
This book will NOT teach you how to be a research scientist nor all the theory behind why specific methods work. For that, I would recommend good research papers and textbooks.
This new understanding of applied deep learning methods will impact your practice of working through computer vision problems in the following ways:
- You will be able to confidently load and prepare image data ready for modeling.
- You will be able to develop effective convolutional neural network models quickly.
- You will be able to effortlessly harness world-class pre-trained models on new problems.
This book is not a substitute for an undergraduate course in deep learning or computer vision, nor is it a textbook for such courses, although it could be a useful complement. For a good list of top textbooks and other resources, see the “Further Reading” section at the end of each tutorial lesson.
…so what is in the Ebook?
30 Step-by-Step Tutorials to Transform You
Into a Deep Learning Computer Vision Practitioner
This book was designed around major deep learning techniques that are directly relevant to computer vision problems.
There are a lot of things you could learn about deep learning and computer vision, from theory to abstract concepts to APIs. My goal is to take you straight to developing an intuition for the elements you must understand with laser-focused tutorials.
The tutorials were designed to focus on how to get results with deep learning methods. As such, they will give you the tools to both rapidly understand and apply each technique or operation. There is a mixture of both tutorial lessons and projects to both introduce the methods and give plenty of examples and opportunity to practice using them.
Each of the tutorials 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 would recommend picking a schedule and sticking to it.
The tutorials are divided into 7 parts; they are:
- Part 1: Foundations. Discover a gentle introduction to computer vision, and the promise of deep learning in the field of computer vision, as well as tutorials on how to get started with Keras.
- Part 2: Data Preparation. Discover tutorials on how to load images, image datasets, and techniques for scaling pixel data in order to make images ready for modeling.
- Part 3: Convolutions and Pooling. Discover insights and intuitions for how convolutional neural networks actually work, including convolutions, filter size, padding, and pooling.
- Part 4: Convolutional Neural Networks. Discover the major model architectural innovations in the development of convolutional neural networks and how to code each from scratch, including VGG, Inception and ResNet
- Part 5: Image Classification. Discover how to develop, tune, and evaluate deep convolutional neural networks for image classification, including problems like Fashion MNIST and CIFAR-10 and entirely new datasets.
- Part 6: Object Detection. Discover deep learning models for object detection such as Mask R-CNN and YOLOv3 and how to both use pre-trained models and train models for new object detection datasets.
- Part 7: Face Recognition. Discover deep learning models for face recognition, including FaceNet and VGGFace2, and how to use pre-trained models for face identification and face verification.
Table of Contents
Below is an overview of the 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
Part 1: Foundations
- Lesson 01: Introduction to Computer Vision
- Lesson 02: Promise of Deep Learning for Computer Vision
- Lesson 03: How to Develop Deep Learning Models With Keras
Part 2: Image Data Preparation
- Lesson 04: How to Load and Manipulate Images with PIL/Pillow
- Lesson 05: How to Manually Scale Image Pixel Data
- Lesson 06: How to Load and Manipulate Images with Keras
- Lesson 07: How to Scale Image Pixel Data with Keras
- Lesson 08: How to Load Large Datasets From Directories with Keras
- Lesson 09: How to Use Image Data Augmentation in Keras
Part 3: Convolutions and Pooling
- Lesson 010: How to Use Different Channel Ordering Formats
- Lesson 011: How Convolutional Layers Work
- Lesson 012: How to Use Filter Size, Padding, and Stride
- Lesson 013: How Pooling Layers Work
Part 4: Convolutional Neural Networks
- Lesson 014: ImageNet, ILSVRC, and Milestone Architectures
- Lesson 015: How Milestone Model Architectural Innovations Work
- Lesson 016: How to Implement Model Architectural Innovations
- Lesson 017: How to Use 1×1 Convolutions to Manage Model Complexity
- Lesson 018: How to Use Pre-Trained Models and Transfer Learning
Part 5: Image Classification
- Lesson 19: How to Classify Black and White Photos of Clothing
- Lesson 20: How to Classify Small Photos of Objects
- Lesson 21: How to Classify Photographs of Dogs and Cats
- Lesson 22: How to Label Satellite Photographs of the Amazon Rainforest
Part 6: Object Detection
- Lesson 23: Deep Learning for Object Recognition
- Lesson 24: How to Perform Object Detection with YOLOv3
- Lesson 25: How to Perform Object Detection with Mask R-CNN
- Lesson 26: How to Develop a New Object Detection Model
Part 7: Face Recognition
- Lesson 27: Deep Learning for Face Recognition
- Lesson 28: How to Detect Faces in Photographs
- Lesson 29: How to Perform Face Identification and Verification with VGGFace2
- Lesson 30: How to Perform Face Classification with FaceNet
- Appendix A: Getting Help
- Appendix B: How to Set Up Your Workstation
- Appendix C: How to Setup Amazon EC2 for Deep Learning on GPUs
- I. Conclusions
Ebook Table of Contents
The screenshot below was taken from the PDF Ebook.
It provides you a full overview of the table of contents from the book.
Each part targets a specific learning outcome, and so does each tutorial within each part.
This acts as a filter to ensure you are only focused on the things you need to know to get to a specific result and do not get bogged down in the math or near-infinite number of digressions.
Learn by Doing
The tutorials were not designed to teach you everything there is to know about each of the methods.
They were designed to give you an understanding of how they work, how to use them, and how to interpret the results the fastest way I know how: to learn by doing.
Take a Sneak Peek Inside The Ebook
Click an image to Enlarge.
…you’ll also get 158 fully working Python scripts
BONUS: Deep Learning Computer Vision 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:
- Preparing Data.
- Transforming Data.
- Defining Models.
- Fitting Models.
- Evaluating Models.
- Making Predictions.
- Image Classification.
- Object Localization.
- Object Detection.
- Face Identification.
- Face Verification.
- Face Classification.
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 and GPU. A GPU is not required, but will accelerate the execution of some of the larger examples.
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 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, although a GPU is recommended but not required for some tutorials.
- Editor: You can use a text editor and run the examples from the command line.
Don’t have a Python Environment Set Up?
The appendix contains step-by-step tutorials showing you exactly how to set up a Python deep learning environment, as well as how to use cheap cloud computing to fit models much faster using GPUs.
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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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.
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.
- 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.
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.