Add September 2019 talks

2019-09-24_convolutional_neural_networks/README.md

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+# Convolutional Neural Networks
+
+* 2019-09-24
+* Speaker: Ash Pahwa
+
+## Abstract
+
+A convolutional neural network (CNN) is a class of deep neural networks used primarily for analyzing visual imagery. CNNs are used for object recognition in an image. Autonomous cars use CNN technology to navigate smoothly on the roads. There are many biomedical applications where CNN is used for image analysis.
+
+The motivation for CNN originated from the Hubel & Wiesel (Noble prize winners 1981) study of the human visual system. This research leads to the idea that if we apply the convolution operations to the image and then feed them to neural networks, the accuracy of object identification increases. This idea started the revolution of CNN.
+
+This talk will cover the architecture of CNN. The Convolution and Pooling operations will be covered. Tools for building a CNN are R and TensorFlow. The implementation of CNN in R will be demonstrated.

2019-09-24_speeding_up_R_with_Rcpp/README.md

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+# Speeding up R with Rcpp
+
+* 2019-09-24
+* Speaker: Luke Klein
+
+## Abstract
+
+R is a beautiful and flexible language, and it is improving all the time. However, some processes (e.g. repeated function calls) can add a lot of computational overhead. Rcpp is a package for extending R with C++ functions which is utilized by over 1000 packages on CRAN. Many R data types and objects can be mapped back and forth to C++ equivalents which facilitates both writing new code for people just learning C++, as well as easier integration of third-party libraries. In this talk, I'll demonstrate how easy it is to get started with Rcpp and what kind of performance boosts one can achieve. If you're taking aim at levelling-up your function writing skills then Rcpp is the place to start.

README.md

@@ -6,6 +6,11 @@ Presentation materials from our monthly meetings
 
 ## 2019 ##
 
+
+* 09/24/2019: *Ash Pahwa*, [Convolutional Neural Networks](https://github.com/ocrug/presentations/tree/master/2019-09-24_convolutional_neural_networks/)
+
+* 09/24/2019: *Luke Klein*, [Speeding up R with Rcpp](https://github.com/ocrug/presentations/tree/master/2019-09-24_speeding_up_R_with_Rcpp/)
+
 * 08/27/2019: *George G. Vega Yon*, [A Brief Introduction to Using R for High-Performance Computing](https://github.com/ocrug/presentations/tree/master/2019-08-27_high_performance_computing)
 
 * 08/27/2019: *Olga Korosteleva*, [Regression Models for Count Data](https://github.com/ocrug/presentations/tree/master/2019-08-27_regression_models_for_count_data/)
@@ -24,7 +29,7 @@ Presentation materials from our monthly meetings
 
 * 05/28/2019: *watR inspectR*, [watR inspectR - OCRUG Hackathon](https://github.com/ocrug/presentations/tree/master/2019-05-28_OCRUG_Hackathon_watR_inspectR)
 
-*_brief_intro_to_using_r_for_hpc_brief_intro_to_using_r_for_hpc_brief_intro_to_using_r_for_hpc 05/28/2019: *Save the woRld*, [Save the woRld - OCRUG Hackathon](https://github.com/ocrug/presentations/tree/master/2019-05-28_OCRUG_Hackathon_Save_the_woRld)
+* 05/28/2019: *Save the woRld*, [Save the woRld - OCRUG Hackathon](https://github.com/ocrug/presentations/tree/master/2019-05-28_OCRUG_Hackathon_Save_the_woRld)
 
 * 05/28/2019: *Peyman Mohajerian*, Using R in big data (Spark, databases, EMR, Hadoop, H2O.ai)