IN THIS CHAPTER
Analyzing live-streaming data using Cloudera Oryx
Recognizing objects in images using CUDA-Convnet
Adding image recognition to web-based apps using ConvNetJS
Obtaining an R SVM implementation using e1071
Adding R GBM optimization support using gbm
Performing natural language interface tasks using Gensim
Creating a generalized linear model using glmnet
Growing a forest of R decision trees using randomForest
Performing scientific tasks of all sorts in Python using SciPy
Obtaining GBDT, GBRT, and GBM support for a variety of languages with XGBoost
The book provides you with a wealth of information about specific machine learning packages such as caret (R) and NumPy (Python). Of course, these are good, versatile packages you can use to begin your machine learning journey. It’s important to have more than a few tools in your toolbox, which is where the suggestions found in this chapter come into play. These packages provide you with additional machine learning insights and capabilities. Even though there are many other packages available on the market, these packages will give you some ideas of where to go next and make it easier to explore other packages as well.
Cloudera Oryx is a machine learning project for Apache Hadoop (
http://hadoop.apache.org/) that provides you with a basis for performing machine learning tasks. It emphasizes the use of live data streaming. This product ...