The idea of cross-validation is to split the data into N subsets, to put one subset aside, to estimate parameters of the model from the remaining N-1 subsets, and to use the retained subset to estimate the error of the model. Such a process is repeated N times - with each of the N subsets being used as the validation set . Then the values of the errors obtained in such N steps are combined to provide the final estimate of the model error. The cross-validation is used in various classification and prediction procedures, such as regression analysis, discriminant analysis, neural networks and classification and regression trees (CART) . The goal is to improve the quality of the decision that is made from the outcome of the study on the basis of statistical methods, and to ensure that maximum information is obtained from scarce experimental data.

While this will serve as an overview of Scott's essay, which you can read for further detail and mathematical insights, we will start by with Fortmann-Roe's verbatim definitions which are central to the piece: Error due to Bias: The error due to bias is taken as the difference between the expected (or average) prediction of our model and the correct value which we are trying to predict. Again, imagine you can repeat the entire model building process multiple times. Fortmann-Roe ends the section on over- and under-fitting by pointing to another of his great essays (Accurately Measuring Model Prediction Error), and then moving on to the highly-agreeable recommendation that "resampling based measures such as cross-validation should be preferred over theoretical measures such as Aikake's Information Criteria." I recommend reading Scott Fortmann-Roe's entire bias-variance tradeoff essay, as well as his piece on measuring model prediction error.

Note that, while there are numerous machine learning ebooks available for free online, including many which are very well-known, I have opted to move past these "regulars" and seek out lesser-known and more niche options for readers. The book has wide coverage of probabilistic machine learning, including discrete graphical models, Markov decision processes, latent variable models, Gaussian process, stochastic and deterministic inference, among others. The material is excellent for advanced undergraduate or introductory graduate course in graphical models, or probabilistic machine learning. One of these target audiences is university students(undergraduate or graduate) learning about machine learning, including those who are beginning a career in deep learning and artificial intelligence research.

K-nearest neighbors, or KNN, is a supervised learning algorithm for either classification or regression. It's super intuitive and has been applied to many types of problems. To make a personalized offer to one customer, you might employ KNN to find similar customers and base your offer on their purchase behaviors. KNN has also been applied to medical diagnosis and credit scoring. This is a post about the K-nearest neighbors algorithm and Python.

The world of high-tech innovation can change the destiny of industries seemingly overnight. Now we are on the cusp of a new grand leap thanks to the decomcratization of machine learning and artificial intelligence already underway, according to this opinion piece by Kartik Hosanagar (@khosanagar), Wharton professor of operations, information and decisions, and a cofounder of Yodle Inc., and, Apoorv Saxena (@apoorvsaxena1), a product manager at Google and co-chair of the recent AI Frontiers conference. Last month, at the CloudNext conference in San Francisco, Google announced its acquisition of Kaggle, an online community for data scientists and machine-learning competitions. Although the move may seem far removed from Google's core businesses, it speaks to the skyrocketing industry interest in machine learning (ML). Kaggle not only gives Google access to a talented community of data scientists, but also one of the largest repository of datasets that will help train the next generation of machine-learning algorithms.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

In the future imagined by science fiction, artificial intelligence will reign supreme and take over pretty much everything humans can do. Frankly this sci-fi vision isn't helpful when it comes to applying technology because it distracts us from thinking about what people do well and what advanced techniques such as deep learning do well. In the world of data science, great strides are being made in the area of deep learning. We've made so much progress that it is easy to think that instead of having to embrace data science as a discipline, we can somehow wait a little big longer and have a Watson-like box to perform all of these tasks for us. If you think this way, you are going to miss the boat.

To help accelerate AI research, Pichai announced that the Tensor Processing Units (TPUs) it uses to train machine-learning models is available in the Google Cloud Platform for anyone to use via the Google Compute Engine today. "We want it to be possible for hundreds of thousands of developers to use machine learning," Pichai said. Our new Cloud TPUs accelerate a wide range of machine learning workloads, including training and inference https://t.co/aWvTVMn54Q The CEO also announced that Google will be using the neural nets it creates to build other neural nets with AutoML. The system takes a set of candidate neural nets (Pichai called them baby neural nets) and iterate them using a reinforcement training approach until the best one is found.

SAP co-founder and chairman of the SAP Supervisory Board professor Hasso Plattner has praised the work Apple has done when it comes to voice recognition, touting Siri as the benchmark his company should strive towards. Speaking with journalists at Sapphire Now in Orlando on Wednesday, Plattner admitted that SAP started a little bit late in diving into artificial intelligence (AI). "We have thrown all of the resources we have into machine learning for the next foreseeable future to get as many projects going in order to have an impact," he said, hoping to make up for lost time. "The Googles have been driving for I don't know how many years in the Bay Area -- and all the other ones. AI was there 25 years ago, but it was not fast enough for our type of applications and was outside the system, but now we can apply AI inside the system."

Japan is one of the advanced countries in the AI and robot industries. At the Ise-Shima G-7 summit meeting in Japan in May, world leaders looked amused and fascinated when they watched a robot performing at the International Media Center. At the same time, because of the technological advances and advent of robots, the leaders are concerned that many people in their countries will lose jobs and cry out for assistance from governments. This kind of downside to innovations has been well recorded in the history of modernization. Innovations bring fundamental changes to many traditional business procedures, jobs and professions. But the impact AI will generate is said to be on a far larger scale than previous innovations.