It is one thing to learn data science by reading or watching a video / MOOC and other to apply it on problems. You need to do both the things to learn the subject effectively. Today’s article is meant to help you apply deep learning on an interesting problem. If you are questioning, why learn or apply deep learning – you have most likely come out of a cave just now. Deep learning in already powering face detection in cameras, voice recognition on mobile devices to deep learning cars. Today, we will solve age detection problem using deep learning. If you are new to deep learning, I would recommend you to refer the articles below before going through this tutorial and making a submission.
Neural networks are considered complicated and they are always explained using neurons and a brain function. But we do not need to learn how to brain works to understand Neural networks structure and how they operate.
Convolutional Neural Nets are getting all the press but it’s Recurrent Neural Nets that are the real workhorse of this generation of AI.
In the first part of this series, I introduced the Outbrain Click Prediction machine learning competition. That post described some preliminary and important data science tasks like exploratory data analysis and feature engineering performed for the competition, using a Spark cluster deployed on Google Dataproc. In this post, I describe the competition evaluation, the design of my cross-validation strategy and my baseline models using statistics and trees ensembles.
For R users, there hasn’t been a production grade solution for deep learning (sorry MXNET). This post introduces the Keras interface for R and how it can be used to perform image classification. The post ends by providing some code snippets that show Keras is intuitive and powerful.
I’m very pleased to announce my DataCamp course on Visualizing Time Series Data in R. This course is also part of the Time Series with R skills track. Feel free to have a look, the first chapter is free!
The padr package was designed to prepare datetime data for analysis. That is, to take raw, timestamped data, and quickly convert it into a tidy format that can be analyzed with all the tidyverse tools. Recently, a colleague and I discovered a second use for the package that I had not anticipated: checking data quality. Every analysis should contain checking if data are as expected. In the case of timestamped data, observations are sometimes missing due to technical malfunction of the system that produced them. Here are two examples that show how pad and thicken can be leveraged to detect problems in timestamped data quickly.
Volatility modelling is typically used for high frequency financial data. Asset returns are typically uncorrelated while the variation of asset prices (volatility) tends to be correlated across time. In this exercise set we will use the rugarch package (package description: here) to implement the ARCH (Autoregressive Conditional Heteroskedasticity) model in R.