Introduction to Artificial Neural Networks and Deep Learning
Introduction to Artificial Neural Networks and Deep Learning
Introduction to Artificial Neural Networks and Deep Learning

Last updated on 2018-03-26

About the Book

Accompanying code examples, additional resources, and early drafts are available on GitHub at https://github.com/rasbt/deep-learning-book

Machine learning has become a central part of our life — as consumers, customers, and hopefully as researchers and practitioners! I appreciate all the nice feedback that you sent me about "Python Machine Learning," and I am so happy to hear that you found it so useful as a learning guide, helping you with your business applications and research projects. I have received many emails since its release. Also, in these very emails, you were asking me about a possible prequel or sequel.

Initially, I was inclined to write more about the "math" parts, which can be a real hurdle for almost everyone without (or even with) a math major in college. Initially, I thought that writing a book about "machine learning math" was a cool thing to do. Now, I have ~15 chapters worth of notes about pre-calculus, calculus, linear algebra, statistics, and probability theory. However, I eventually came to a conclusion that there were too many other math books out there, already! Most of them are far better and more comprehensive and accurate than my potential ~500-page introduction to the topics that I had in store. After all, I think that the real motivation for learning and understanding a subject comes from being excited about it in the first place; if you are passionate about machine learning and you stumble upon the chain rule in calculus, you wouldn't have any problems to find a trusted resource via your favorite search engine these days.

So, instead of writing that "prequel," let me write about something that's built upon the concepts that I introduced in the later chapters of "Python Machine Learning" -- algorithms for deep learning. After we coded a multi-layer perceptron (a certain kind of feedforward artificial neural network) from scratch, we took a brief look at some Python libraries for implementing deep learning algorithms, and I introduced convolutional and recurrent neural networks on a conceptual level.

In this book, I want to continue where I left off and want to implement deep neural networks and algorithms for deep learning algorithms from scratch, using Python, NumPy, and SciPy throughout this educational journey. In addition to the vanilla Python science-stack, we will implement these algorithms in TensorFlow, Google's open source and cutting-edge deep learning library for implementing and applying deep learning to real-world problems efficiently.

About the Author

Sebastian Raschka
Sebastian Raschka

Sebastian Raschka is the author of the bestselling book “Python Machine Learning.” As a Ph.D. candidate at Michigan State University, he is developing new computational methods in the field of computational biology. Sebastian has many years of experience with coding in Python and has given several seminars on the practical applications of data science and machine learning. Sebastian loves to write and talk about data science, machine learning, and Python, and he is really motivated to help people developing data-driven solutions without necessarily requiring a machine learning background.

Sebastian is also actively contributing to open source projects, and methods that he implemented are now successfully used in machine learning competitions such as Kaggle. In his free-time, Sebastian is also working on models for sports predictions, and if he is not sitting in front of a computer, he enjoys playing sports in his spare time.

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