Machine Learning & Deep Learning Tutorials

Spark Python Notebooks This is a collection of  IPython notebook / Jupyter  notebooks intended to train the reader on different  Apache Spark  concepts, from basic to advanced, by using the  Python  language. If Python is not your language, and it is R, you may want to have a look at our  R on Apache Spark (SparkR) notebooks  instead. Additionally, if your are interested in being introduced to some basic Data Science Engineering, you might find  these series of tutorials  interesting. There we explain different concepts and applications using Python and R. Instructions A good way of using these notebooks is by first cloning the repo, and then starting your own  IPython notebook / Jupyter  in pySpark mode . For example, if we have a  standalone  Spark installation running in our  localhost  with a maximum of 6Gb per node assigned to IPython: MASTER="spark://127.0.0.1:7077" SPARK_EXECUTOR_MEMORY="...

A Gentle Introduction to Machine Learning Overview Introduction to the Introduction The purpose of this workshop is to give you a broad, accurate, although somewhat cursory understanding of machine learning. More particularly, it aims to help you understand and program some of the common algorithms used in machine learning. It aims to guide you through what is involved in training these algorithms and verifying this training. And it aims to do all this over the subfields of supervised machine learning, unsupervised machine learning, and reinforcement learning. If none of that made any sense, that's ok. Part of the point of the workshop is to explain these and other terms. I'll introduce them slowly from now on. Machine Learning is generally a very mathematical field, which can make it somewhat intimidating. This introduction tries to lower that mathematical barrier by assuming only that the student has (1) a good understanding of Javascript and a (2) decently good...