new courses: deep learning & financial data!

Build neural nets in Deep Learning in Python and learn how to import financial data with Importing & Managing Financial Data in R! [DataCamp]( New Courses! [Deep Learning in Python]( Deep Learning in Python Taught by Dan Becker, Product Director at DataRobot & Contributor to Keras and TensorFlow Deep learning is a set of of algorithms that use especially powerful neural networks. In this course, you'll gain hands-on knowledge of how to use neural networks and deep learning with the Keras 2.0 library. [Start Learning ›]( Importing and Managing Financial Data in R Taught by Joshua Ulrich, Quantitative Analyst & creator of the TTR package Getting data into R can be stressful and time-consuming, especially when you need to merge data from several different sources into one data set. This course will cover importing data from both local files and the internet. [Start Learning ›]( Deep Learning in Python: What You'll Learn Chapter One: Basics of Deep Learning & Neural Networks Become familiar with the fundamental concepts and terminology used in deep learning and build simple neural networks yourself. Chapter Two: Optimizing a Neural Network with Backward Propagation Learn how to optimize the predictions generated by your neural networks using a method called backward propagation. Chapter Three: Building Deep Learning Models with Keras Use the keras library to build deep learning models for both regression as well as classification. Chapter Four: Fine-tuning keras Models Optimize your deep learning models in keras by validating your models and experimenting with wider and deeper networks. [Play now ›]( Importing and Managing Financial Data in R: What You'll Learn Chapter One: Introduction and Downloading Data Learn how getSymbols() and Quandl() make it easy to access data from a variety of sources. Chapter Two: Extracting & Transforming Data Explore how to import, transform, and extract data from multiple instruments. Chapter Three: Managing Data from Multiple Sources Learn how to simplify and streamline your workflow by taking advantage of the ability to customize default arguments. Chapter Four: Aligning Data with Different Periodicities Learn how to convert sparse, irregular data into a regular series. Chapter Five: Importing Text Data and Adjusting for Corporate Actions Learn how to check data for weirdness, handle missing values, and adjust stock prices for splits and dividends. [Play now ›]( DataCamp Inc. 2067 Massachusetts avenue Cambridge MA 02141 [Unsubscribe](