Neural networks and deep learning. Neural networks are one of the most beautiful programming paradigms ever invented.
In the conventional approach to programming, we tell the computer what to do, breaking big problems up into many small, precisely defined tasks that the computer can easily perform. By contrast, in a neural network we don't tell the computer how to solve our problem. Instead, it learns from observational data, figuring out its own solution to the problem at hand. Automatically learning from data sounds promising. However, until 2006 we didn't know how to train neural networks to surpass more traditional approaches, except for a few specialized problems.
The purpose of this book is to help you master the core concepts of neural networks, including modern techniques for deep learning. A principle-oriented approach This means the book is emphatically not a tutorial in how to use some particular neural network library. A hands-on approach It's rare for a book to aim to be both principle-oriented and hands-on.
51 Free Data Science Books. A great collection of free data science books covering a wide range of topics from Data Science, Business Analytics, Data Mining and Big Data to Machine Learning, Algorithms and Data Science Tools.
Data Science Overviews Data Scientists Interviews The Data Science Handbook (Carl Shan, Henry Wang, William Chen, & Max Song, 2015)The Data Analytics Handbook (Brian Liou, Tristan Tao, & Declan Shener, 2015) Welcome to the School of Data Handbook. The School of Data Handbook is a companion text to the School of Data.
Its function is something like a traditional textbook – it will provide the detail and background theory to support the School of Data courses and challenges. The Handbook should be accessible to all learners. It comes with a Glossary explaining the important terms and concepts. If you stumble across an unexplained term or a concept that requires more explanation, please do get in touch. The Handbook will guide you through the key stages of a data project. Processing stages for data projects While there are many different types of data, almost all processing can be expressed as a set of incremental stages. An introduction to the data pipeline Acquisition describes gaining access to data, either through any of the methods mentioned above or by generating fresh data, e.g through a survey or observations. Highly Recommended Books for Machine Learning Researchers. Learning more like a human: 18 free eBooks on Machine Learning « Big Data Made Simple.
Statistical foundations of machine learning. Python Machine Learning Books. Python is a very popular language for machine learning.
The machine learning libraries and frameworks in Python (especially around the SciPy stack) are maturing quickly. They may not be as feature rich as R, but they are robust enough for small to medium scale production implementation. If you are a Python programmer looking to get into machine learning or you are generally interested to get into machine learning via Python, then I want to use this post to point out some key books you might find useful on your journey. This is by no means a complete list of books, but I think they are the pick of the books you should look at if you are interested in machine learning in Python. You Can Learn Machine Learning Algorithms.
Discover the step-by-step structured learning technique you can use Over the last decade I have been using and refining an algorithm description template to learn, apply and implement algorithms and I even used it to described 45 different algorithms for a book.
I have described this template and how to use it in a handy 12-page PDF guide called: Algorithm Description Template: How to Describe Machine Learning Algorithms. Convinced? Jump straight to the description. Understanding Algorithms is Hard Algorithms are the core of machine learning, but they can be very difficult to learn. Avid Barber : Brml - Home Page browse. A Course in Machine Learning. The Learning and Intelligent OptimizatioN solver. Introduction to Statistical Learning. An Introduction to Statistical Learning with Applications in R Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani This book provides an introduction to statistical learning methods.
It is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences. The book also contains a number of R labs with detailed explanations on how to implement the various methods in real life settings, and should be a valuable resource for a practicing data scientist. For a more advanced treatment of these topics: The Elements of Statistical Learning. Slides and videos for Statistical Learning MOOC by Hastie and Tibshirani available separately here. "An Introduction to Statistical Learning (ISL)" by James, Witten, Hastie and Tibshirani is the "how to'' manual for statistical learning.