This is just the beginning. Technology, which promises to bring huge changes to the world in coming years, is nothing but Machine Learning. It is an essential part of Artificial Intelligence research and gained the highest limelight in business. Due to the wide usage of digital devices, Machine Learning has offered a revolutionary way of solving tasks which can be data analysis, classification, forecasting, image recognition, etc.
Understanding how artificial intelligence works may seem to be highly overwhelming, but it all comes down to two concepts, machine learning, and deep learning. These two terms are usually used interchangeably assuming they both mean the same, but they are not. Both the terms are not new to us, but the way they are utilized to describe intelligent machines has always been changing.
First of all, myth busted: the 1080 Ti can run minesweeper effortlessly. The machine did restart itself once for no obvious reasons after the proprietary GPU driver was installed. Back to the topic… Here is some R code for fitting a "wide and deep" classification model with Tensorflow and Tensorflow Estimators API. The model is fundamentally a direct combination of a linear model and a DNN model. The synthetic data has 1 million observations, 100 features (20 being useful) and is generated by my R package msaenet.
For those considering an autodidactic alternative, this is for you. You can't go deeply into every machine learning topic. There's too much to learn, and the field is advancing rapidly. Motivation is far more important than micro-optimizing a learning strategy for some long-term academic or career goal. If you're trying to force yourself forward, you'll slow down.