Scuba dive master, wildlife photographer, anthropologist, programmer, electronics tinkerer and big data expert.
CUDA
When performing data science on an Ubuntu Linux machine remotely, the fans on NVIDIA GPUs may not spin up in response to increased load. This is due to fact that the NVIDIA controlling software generally requires logging into the GUI Desktop. Simply forwarding the xwindow with SSH has little effect,
TensorFlow
I received a pair of Google's 'Coral' Edge TPU Dev Boards today. They have the exact same dimensions as Raspberry Pi devices, including the location of the mounting post holes. This is convenient if you have project boxes or mounting plates that you normally use for
TensorFlow
Feeling like a kid on Christmas, today I finally received a couple of the Google 'Coral' Edge TPU Accelerators in the mail. First impressions upon unboxing is that they're not as physically robust as the Intel Neural Compute Stick 2. The entire Intel NCS2 case is
python
Data assets used in these examples * Titanic [https://www.kaggle.com/c/titanic] * City population [http://ezlocal.com/blog/post/Top-5000-US-Cities-by-Population.aspx] * USGS ShakeMap Atlas [https://earthquake.usgs.gov/data/shakemap/] -------------------------------------------------------------------------------- Reading data from CSV files Reading CSV files and using one of the columns as the index Basic
spark
Scala [https://www.scala-lang.org/] is a fun language which gives you all the power of Java [https://www.java.com], with the simplicity of Python [https://www.python.org/], and the power of functional programming [https://en.wikipedia.org/wiki/Functional_programming]. Though there are a variety of IDE
python
Install CUDA ToolKit The first step in our process is to install the CUDA ToolKit, which is what gives us the ability to run against the the GPU CUDA cores. Because TensorFlow [https://www.tensorflow.org/] is very version specific, you'll have to go to the CUDA ToolKit
spark
A short user defined function written in Scala which allows you to transpose a dataframe without performing aggregation functions.
spark
Vectors are typically required for Machine Learning tasks, but are otherwise not commonly used. Sometimes you end up with an assembled Vector [https://spark.apache.org/docs/latest/ml-features.html#vectorassembler] that you just want to disassemble into its individual component columns so you can do some Spark SQL work,
spark
One of the common data engineering tasks is taking a deep dataset and turning into a wide dataset with some sort of aggregation function. Let's take a quick look at an example dataset to see why we would want to perform this action. Our goal: To determine if
spark
Here's an easy example of how to rename all columns in an Apache Spark DataFrame. Tehcnically, we're really creating a second DataFrame with the correct names. // IMPORT DEPENDENCIES import org.apache.spark.sql.SparkSession import org.apache.spark.sql.functions._ import org.apache.spark.sql.{SQLContext,
spark
The Titanic: Machine Learning from Disaster [https://www.kaggle.com/c/titanic] competition on Kaggle [https://www.kaggle.com/] is an excellent resource for anyone wanting to dive into Machine Learning [https://en.wikipedia.org/wiki/Machine_learning]. There are forums [https://www.kaggle.com/c/titanic/discussion] where you
africa
Copyright: James Conner Camera: Canon EOS 5D Mark III Lens: Canon 100-400L F4 with 1.4x Internal Extender Stats: 560mm/ƒ/5.6/1/1600s/ISO 640 Taken: March 5 2015