Deep Learning With Python
Tap The Power of TensorFlow and Theano with Keras,
Develop Your First Model, Achieve State-Of-The-Art Results
Deep learning is the most interesting and powerful machine learning technique right now.
Top deep learning libraries are available on the Python ecosystem like Theano and TensorFlow. Tap into their power in a few lines of code using Keras, the best-of-breed applied deep learning library.
In this mega Ebook is written in the friendly Machine Learning Mastery style that you’re used to, learn exactly how to get started and apply deep learning to your own machine learning projects. After purchasing you will get:
- 256 Page PDF Ebook.
- 66 Python Recipes.
- 18 Step-by-Step Lessons.
- 9 End-to-End Projects.
Finally, Bring Deep Learning To Your Projects
Click to jump straight to the packages.
Why Are Deep Learning Models So Powerful?
…the secret is “Representation Learning“
Deep learning techniques are so powerful because they learn the best way to represent the problem while learning how to solve the problem.
This is called representation learning.
Representation learning is perhaps the biggest differentiation between deep learning models and classical machine learning algorithm.
It is the power of representation learning that is spurring such great creativity in the way the techniques are being used. For example:
- Deep learning models are being used for very difficult problems and making progress, like colorizing image and videos based on the context in the scene.
- Deep learning models are being used in bold new ways, such as cutting the head off a network trained on one problem and tuning it for a completely different problem, and getting impressive results.
- Combinations of deep learning models are being used to both identify objects in photographs and then generate textual descriptions of those objects, a complex multi-media problem that was previously thought to require large artificial intelligence systems.
Deep learning is hot, it is delivering results and now is the time to get involved. But where do you start?
So How Do Regular People Get Started?
…don’t do what everyone else does!
Where do you even begin in deep learning?
Deep learning looks like a hard field to get started in.
And in many ways it is hard to get started. Hard enough that many people try and quickly give up.
Because they are told that they must already be masters in a laundry list of academic disciplines.
Here’s The WRONG WAY To Get Started in Deep Learning
For example, a common response to the question “how do I get started in deep learning” might be:
- Develop a strong grounding in statistics, probability, linear algebra, multivariate statistics and calculus.
- Develop a deep knowledge of modern machine learning algorithms and techniques.
- Study and become one with the mathematical theory of each deep learning algorithm and a bunch of related techniques for using them.
- Oh and if there is time find a library and start applying deep learning to your problem.
It could take a decade or more to follow this advice and that would be a decade delay that you cannot afford.
This approach is DEAD WRONG
If I had followed the advice given to beginner developers (study discrete math, start with assembler, etc.) I would never have started developing software as a profession.
Don’t let this same “first principles fallacy” stop you from following your growing interest and passion in deep learning.
There is a much easier path that is just right for you. Flip the script.
Deep Learning For The Rest Of Us
…so here is how to do it
Deep learning is a tool that you can use on your machine learning projects. It does not have to be a theoretical academic pursuit that you study in gritty detail.
You can get started in deep learning by selecting one of the best-of-breed deep learning libraries and start developing models.
You will not understand all of the internals to begin with, but you will very quickly learn how to develop and evaluate deep learning models for a variety of machine learning problems. And Start delivering value. Oh and as you may suspect, you probably don’t ever need to understand all of the internals to get excellent results.
The best kept secret of deep learning (and even broader machine learning) is that the applied side is quite shallow. It does not take you long to be able to start using the tools quite expertly on your own projects.
The caveat is that you need to bring some rigor in terms of process to ensure that you results are robust (e.g. careful test harness design) and that your solutions are suitable for the problems you are solving (e.g. careful framing of the problem).
So what are the best-of-breed libraries for deep learning?
Use Python, Build On Top of Theano and TensorFlow
…and boost your progress 1000% by using Keras
Develop and evaluate deep learning models in Python.
The platform for getting started in applied deep learning is Python.
Python is a fully featured general purpose programming language, unlike R and Matlab. It is also quick and easy to write and understand, unlike C++ and Java.
The SciPy stack in Python is a mature and quickly expanding platform for scientific and numerical computing. The platform hosts libraries such as scikit-learn the general purpose machine learning library that can be used with your deep learning models.
It is because of these benefits of the Python ecosystem that two top numerical libraries for deep learning were developed for Python, Theano and the newer TensorFlow library released by Google (and adopted recently by the Google DeepMind research group).
Theano and TensorFlow are two top numerical libraries for developing deep learning models, but are too technical and complex for the average practitioner. They are intended more for research and development teams and academics interested in developing wholly new deep learning algorithms.
The saving grace is the Keras library for deep learning, that is written in pure Python, wraps and provides a consistent agnostic interface to Theano and TensorFlow and is aimed at machine learning practitioners that are interested in creating and evaluating deep learning models.
It is a little over one year old and is clearly the best-of-breed library for getting started with deep learning because of both the speed at which you can develop models and the numerical power it is built upon.
Learn Fast By Building Deep Learning Models For Well Understood Problems
…and build up a library of scripts you can leverage
The fastest way to get a handle on deep learning and get productive at developing models for your own machine learning problems is to practice.
You can use a tutorial-based approach to learn the basics of different neural network models and feel out the features of the Keras API.
Very quickly you can start to pull together this knowledge and take on larger, fuller and more complicated deep learning projects.
This approach is fast and effective for three reasons:
- You are actually writing code and developing deep learning models rather then reading about it or studying theory.
- Each completed small project provides a working base for further investigation or pivoting into a new problem.
- You amass a catalog of working code for deep learning models and library API that you can dip into and pull together on new projects very quickly.
This is the approach that you can use to rapidly get up-to-speed with applied deep learning in Python with the Keras library and start tackling your own predictive modeling problems with deep learning.
It is also the approach that you can follow in my new ebook Deep Learning With Python.
Introducing “Deep Learning With Python”
…your ticket to applied deep learning
This book was designed using for you as a developer to rapidly get up to speed with applied deep learning in Python using the best-of-breed library Keras.
The ebook is comprised of lessons and projects and uses a step-by-step tutorial approach throughout.
The goal is to get you using Keras to quickly create your first neural networks as quickly as possible, then guide you through the finer points of developing deeper models and models for computer vision and natural language problems.
This ebook is your guide to developing and evaluating deep learning models in your own machine learning projects.
Let’s take a closer look at the breakdown of what you will discover inside this ebook.
Everything You Need To Know to Develop Deep Learning Models in Python
You Will Get:
18 Lessons on Deep Learning, Keras and More
9 Project Tutorials that Tie it All Together
This ebook was written around two themes designed to get you started and using deep learning effectively and quickly.
These two parts are Lessons and Projects:
- Lessons: Learn how the sub-tasks of applied deep learning map onto the Keras Python library and the best practice way of working through each task.
- Projects: Tie together all of the knowledge from the lessons by working through case study predictive modeling problems.
Here is an overview of the step-by-step lessons you will complete:
- Lesson 01: Introduction to the Theano library.
- Lesson 02: Introduction to the TensorFlow library.
- Lesson 03: Introduction to the Keras library.
- Lesson 04: Crash Course in Multi-Layer Perceptrons.
- Lesson 05: Develop Your First Neural Network With Keras.
- Lesson 06: Evaluate the Performance Of Deep Learning Models.
- Lesson 07: Use Keras Models With scikit-learn.
- Lesson 08: Save Your Models For Later With Serialization.
- Lesson 09: Keep The Best Models During Training.
- Lesson 10: Understand Model Behavior During Training.
- Lesson 11: Reduce Overfitting With Dropout Regularization.
- Lesson 12: Lift Performance With Learning Rate Schedules.
- Lesson 13: Crash Course in Convolutional Neural Networks.
- Lesson 14: Improve Model Performance With Image Augmentation.
- Lesson 15: Crash Course in Recurrent Neural Networks.
- Lesson 16: Time Series Prediction with Multilayer Perceptrons.
- Lesson 17: Time Series Prediction with LSTM Networks.
- Lesson 18: Understanding Stateful LSTM Recurrent Neural Networks.
Each lesson was designed to be completed in about 30 minutes by the average developer.
Here is an overview of the 7 end-to-end projects you will complete:
- Project 01: Develop Large Models on GPUs Cheaply in the Cloud.
- Project 02: Multiclass Classification of Flower Species.
- Project 03: Binary Classification of Sonar Returns.
- Project 04: Regression of Boston House Prices.
- Project 05: Handwritten Digit Recognition.
- Project 06: Object Recognition in Photographs.
- Project 07: Predict Sentiment From Movie Reviews.
- Project 08: Sequence Classification with LSTMs for Movie Reviews.
- Project 09: Text Generation With Alice in Wonderland.
Each project was designed to be completed in about 60 minutes by the average developer.
Here’s Everything You’ll Get…
in Deep Learning With Python
Hands-On Tutorials and Projects
A digital download that contains everything you need, including:
- Clear algorithm descriptions that help you to understand the principles that underlie each technique.
- Step-by-step deep learning tutorials to show you exactly how to apply each method.
- End-to-end deep learning projects that show you exactly how to tie the pieces together and get a result.
- Python source code recipes for every example in the book so that you can run the tutorial and project code in seconds.
- Digital Ebook in PDF format so that you can have the book open side-by-side with the code and see exactly how each example works.
Resources you need to go deeper, when you need to, including:
- Top machine learning textbooks to deepen your foundation of artificial neural networks and deep learning, if you crave more.
- The best places online where you can ask your challenging questions and actually get a response.
Foundations and grounding you need for applied deep learning, including:
- The high-performance computing platform that underlies deep learning in Python called Theano.
- The second optional framework that underlies deep learning in Python called Google TensorFlow.
- The the best library for deep learning in python for developers called Keras.
- The development of deep learning models on Amazon cloud services to harness the speed of GPU hardware for less than $1 per hour.
The Multilayer Perceptron network, a foundation of deep learning including:
- The basics of multilayer artificial neural networks needed to use them in practice.
- The 6-step process to develop your first neural network with Keras in minutes.
- The 3 methods that you can use to evaluate the performance of your neural networks, including one that gives the most robust estimates.
- The 2 best features of scikit-learn to leverage when developing neural networks with Keras, and the one that will save you hours.
- The 3 end-to-end projects that show you how to use Multilayer Perceptron networks for predictive modeling problems.
MLPs, CNNs and RNNs
The advanced techniques to when developing Multilayer Perceptrons, including:
- The 2 formats that you can use to save your network structure to file and the HDF5 standard that you can use to save network weights for later use.
- The simple method to ensure that your results are not lost if your multi-day run crashes half-way through.
- The simple visualization technique that you can use to check if your deep learning model is over learning or under learning your problem.
- The simple and clever technique that you can use to reduce overfitting.
- The 2 methods you can use to dynamically change learning rate while training that gives you a lift in performance.
The Convolutional Neural Network, for computer vision tasks, including:
- The basics of convolutional neural networks needed to use them in practice such as their structure and learning method.
- The problem of handwritten digit recognition and how to solve it using convolutional neural networks.
- The clever approach of image augmentation and 6 techniques you can use to improve the generalization of your models.
- The problem of object recognition in photographs and how to solve it using convolutional neural networks of increasing size.
- The application of convolutional neural networks to text data and how to use them to predict the sentiment of movie reviews from the text alone.
The Recurrent Neural Network, to learn complex sequences, including:
- The basics of recurrent neural networks needed to use them in practice including their structure and the most popular type.
- The problem of time series prediction and a clever technique to improve the performance for Multilayer Perceptrons on this problem.
- The LSTM recurrent neural network and the 5 ways it can be used to model time series prediction problems.
- The clever framing of sentiment prediction as the classification of a sequence of words and how to use LSTMs to solve it.
- The example problem of predicting the next letter of the alphabet and its use to give you deeper insight into how LSTMs work.
- The invention of new sentences for Alice In Wonderland by an LSTM network trained on the whole book.
What More Do You Need?
Take a Sneak Peek Inside The Ebook
BONUS: Deep Learning Code Recipes
…you also get 66 fully working deep learning scripts
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.
- You get the datasets used throughout the book.
Your Deep Learning Code Recipe Library covers the following topics:
- Data Augmentation
- Data Preparation
- Learning Rate
- CIFAR-10 dataset
- IMDB dataset
- MLP Projects
- MNIST dataset
- Time Series Prediction
- LSTM Stateful Recurrent Networks
- Text Generation
This means that you can follow along and compare your answers to a known working implementation of each algorithm in the provided Python files.
This helps a lot to speed up your progress when working through the details of a specific task.
About The Author
Hi, I'm Jason Brownlee. I run this site and I wrote and published this book.
I live in Australia with my wife and sons. I love to read books, write tutorials, and develop systems.
I have a computer science and software engineering background as well as Masters and PhD degrees in Artificial Intelligence with a focus on stochastic optimization.
I've written books on algorithms, won and ranked well in competitions, consulted for startups, and spent years in industry. (Yes, I have spend a long time building and maintaining REAL operational systems!)
I get a lot of satisfaction helping developers get started and get really good at applied machine learning.
I teach an unconventional top-down and results-first approach to machine learning where we start by working through tutorials and problems, then later wade into theory as we need it.
I'm here to help if you ever have any questions. I want you to be awesome at machine learning.
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The book “Long Short-Term Memory Networks with Python” is not focused on time series forecasting, instead, it is focused on the LSTM method for a suite of sequence prediction problems.
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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.
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I do put some of the book chapters on the blog as examples, but they are not tied to the surrounding chapters or the narrative that a book offers and do not offer the standalone code files.
With each book, you also get all of the source code files used in the book that you can use as recipes to jump-start your own predictive modeling problems.
My books are playbooks. Not textbooks.
They have no deep explanations of theory, just working examples that are laser-focused on the information that you need to know to bring machine learning to your project.
There is little math, no theory or derivations.
My readers really appreciate the top-down, rather than bottom-up approach used in my material. It is the one aspect I get the most feedback about.
My books are not for everyone, they are carefully designed for practitioners that need to get results, fast.
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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
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.
Linux, MacOS, and Windows.
There are no code examples in “Master Machine Learning Algorithms“, therefore no programming language is used.
Algorithms are described and their working is summarized using basic arithmetic. The algorithm behavior is also demonstrated in excel spreadsheets, that are available with the book.
It is a great book for learning how algorithms work, without getting side-tracked with theory or programming syntax.
If you are interested in learning about machine learning algorithms by coding them from scratch (using the Python programming language), I would recommend a different book:
I write the content for the books (words and code) using a text editor, specifically sublime.
I typeset the books and create a PDF using LaTeX.
All of the books have been tested and work with Python 3 (e.g. 3.5 or 3.6).
Most of the books have also been tested and work with Python 2.7.
Where possible, I recommend using the latest version of Python 3.
After you fill in the order form and submit it, two things will happen:
- You will be redirected to a webpage where you can download your purchase.
- You will be sent an email (to the email address used in the order form) with a link to download your purchase.
The redirect in the browser and the email will happen immediately after you complete the purchase.
You can download your purchase from either the webpage or the email.
If you cannot find the email, perhaps check other email folders, such as the “spam” folder?
If you have any concerns, contact me and I can resend your purchase receipt email with the download link.
I do test my tutorials and projects on the blog first. It’s like the early access to ideas, and many of them do not make it to my training.
Much of the material in the books appeared in some form on my blog first and is later refined, improved and repackaged into a chapter format. I find this helps greatly with quality and bug fixing.
The books provide a more convenient packaging of the material, including source code, datasets and PDF format. They also include updates for new APIs, new chapters, bug and typo fixing, and direct access to me for all the support and help I can provide.
I believe my books offer thousands of dollars of education for tens of dollars each.
They are months if not years of experience distilled into a few hundred pages of carefully crafted and well-tested tutorials.
I think they are a bargain for professional developers looking to rapidly build skills in applied machine learning or use machine learning on a project.
Also, what are skills in machine learning worth to you? to your next project? and you’re current or next employer?
Nevertheless, the price of my books may appear expensive if you are a student or if you are not used to the high salaries for developers in North America, Australia, UK and similar parts of the world. For that, I am sorry.
I do offer discounts to students, teachers and retirees.
Please contact me to find out more.
I offer a ton of free content on my blog, you can get started with my best free material here:
About my Books
My books are playbooks.
They are intended for developers who want to know how to use a specific library to actually solve problems and deliver value at work.
- My books guide you only through the elements you need to know in order to get results.
- My books are in PDF format and come with code and datasets, specifically designed for you to read and work-through on your computer.
- My books give you direct access to me via email (what other books offer that?)
- My books are a tiny business expense for a professional developer that can be charged to the company and is tax deductible in most regions.
Very few training materials on machine learning are focused on how to get results.
The vast majority are about repeating the same math and theory and ignore the one thing you really care about: how to use the methods on a project.
Comparison to Other Options
Let me provide some context for you on the pricing of the books:
There are free videos on youtube and tutorials on blogs.
- Great, I encourage you to use them, including my own free tutorials.
There are very cheap video courses that teach you one or two tricks with an API.
- My books teach you how to use a library to work through a project end-to-end and deliver value, not just a few tricks
A textbook on machine learning can cost $50 to $100.
- All of my books are cheaper than the average machine learning textbook, and I expect you may be more productive, sooner.
A bootcamp or other in-person training can cost $1000+ dollars and last for days to weeks.
- A bundle of all of my books is far cheaper than this, they allow you to work at your own pace, and the bundle covers more content than the average bootcamp.
Sorry, my books are not available on websites like Amazon.com.
I carefully decided to not put my books on Amazon for a number of reasons:
- Amazon takes 65% of the sale price of self-published books, which would put me out of business.
- Amazon offers very little control over the sales page and shopping cart experience.
- Amazon does not allow me to contact my customers via email and offer direct support and updates.
- Amazon does not allow me to deliver my book to customers as a PDF, the preferred format for my customers to read on the screen.
I hope that helps you understand my rationale.
I am sorry to hear that you’re having difficulty purchasing a book or bundle.
I use Stripe and PayPal services to support secure and encrypted payment processing on my website.
Some common problems when customers have a problem include:
- Perhaps you can double check that your details are correct, just in case of a typo?
- Perhaps you could try a different payment method, such as PayPal or Credit Card?
- Perhaps you’re able to talk to your bank, just in case they blocked the transaction?
I often see customers trying to purchase with a domestic credit card that does not allow international purchases. This is easy to overcome by talking to your bank.
If you’re still having difficulty, please contact me and I can help investigate further.
I give away a lot of content for free. Most of it in fact.
It is important to me to help students and practitioners that are not well off, hence the enormous amount of free content that I provide.
You can access the free content:
I have thought very hard about this and I sell machine learning Ebooks for a few important reasons:
- I use the revenue to support the site and all the non-paying customers.
- I use the revenue to support my family so that I can continue to create content.
- Practitioners that pay for tutorials are far more likely to work through them and learn something.
- I target my books towards working professionals that are more likely to afford the materials.
All updates to the book or books in your purchase are free.
Books are usually updated once every few months to fix bugs, typos and keep abreast of API changes.
Contact me anytime and check if there have been updates. Let me know what version of the book you have (version is listed on the copyright page).
Please contact me anytime with questions about machine learning or the books.
One question at a time please.
Also, each book has a final chapter on getting more help and further reading and points to resources that you can use to get more help.
Yes, the books can help you get a job, but indirectly.
Getting a job is up to you.
It is a matching problem between an organization looking for someone to fill a role and you with your skills and background.
That being said, there are companies that are more interested in the value that you can provide to the business than the degrees that you have. Often, these are smaller companies and start-ups.
You can focus on providing value with machine learning by learning and getting very good at working through predictive modeling problems end-to-end. You can show this skill by developing a machine learning portfolio of completed projects.
My books are specifically designed to help you toward these ends. They teach you exactly how to use open source tools and libraries to get results in a predictive modeling project.
Do you have another question?