If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
To kick off a series of Neo4j extensions for machine learning, I implemented a set of user-defined procedures that create a linear regression model in the graph database. In this post, I demonstrate use of linear regression from the Neo4j browser to suggest prices for short term rentals in Austin, Texas. Let's check out the use case: The most popular area in Austin, Texas is identified by the last two digits of its zip code: "04". With the trendiest clubs, restaurants, shops, and parks, "04" is a frequent destination for tourists. Suppose you're an Austin local who's going on vacation.
Random Forest is a flexible, easy to use machine learning algorithm that produces, even without hyper-parameter tuning, a great result most of the time. It is also one of the most used algorithms, because it's simplicity and the fact that it can be used for both classification and regression tasks. In this post, you are going to learn, how the random forest algorithm works and several other important things about it. Random Forest is a supervised learning algorithm. Like you can already see from it's name, it creates a forest and makes it somehow random.
As we all know in today's world of quick results and insights nobody wants to spend time in understanding the core concepts of certain statistical terms while performing analytical routine. One statistical term that is talked a lot but known very less in terms of its mechanics is R Squared statistics a.k.a. This statistics helps to measure the closeness of the data to the fitted line of regression. It is also worth mentioning that by squaring the correlation coefficient statistic one can calculate the R Squared value. However, I want to take few step back to clear the fog with regards to the calculation of this statistics and kill the confusion around it (I know this is quite and extreme statement).
Data science and machine learning are not just buzz words of the moment but have rather transformed how business is conducted in today's world. Incorporation of technology and digitization of almost every feasible aspect of business provides access to abundance of data. This data can provide insights which offer a scientific basis for decision making. To keep up with constantly changing trends and to have ability to self-correct based of most recent information necessitates the machines with analytical capabilities. Machine learning is a data analysis method used to automate analytical model building algorithms.
When approaching any type of Machine Learning (ML) problem there are many different algorithms to choose from. In machine learning, there's something called the "No Free Lunch" theorem which basically states that no one ML algorithm is best for all problems. The performance of different ML algorithms strongly depends on the size and structure of your data. Thus, the correct choice of algorithm often remains unclear unless we test out our algorithms directly through plain old trial and error. But, there are some pros and cons to each ML algorithm that we can use as guidance.
This meme has been all over social media lately, producing appreciative chuckles across the internet as the hype around deep learning begins to subside. The sentiment that machine learning is really nothing to get excited about, or that it's just a redressing of age-old statistical techniques, is growing increasingly ubiquitous; the trouble is it isn't true. I get it -- it's not fashionable to be part of the overly enthusiastic, hype-drunk crowd of deep learning evangelists. ML experts who in 2013 preached deep learning from the rooftops now use the term only with a hint of chagrin, preferring instead to downplay the power of modern neural networks lest they be associated with the scores of people that still seem to think that import keras is the leap for every hurdle, and that they, in knowing it, have some tremendous advantage over their competition. While it's true that deep learning has outlived its usefulness as a buzzword, as Yann LeCun put it, this overcorrection of attitudes has yielded an unhealthy skepticism about the progress, future, and usefulness of artificial intelligence.
Editor's Note: This article originally appeared on tensorflow.org and is being republished under the guidelines of the Creative Commons Attribution 3.0 License (more legal details at the end of the article). This tutorial is intended for readers who are new to both machine learning and TensorFlow. If you already know what MNIST is, and what softmax (multinomial logistic) regression is, you might prefer this faster paced tutorial. Be sure to install TensorFlow before starting either tutorial. When one learns how to program, there's a tradition that the first thing you do is print "Hello World."
In this work, we highlight a connection between the incremental proximal method and stochastic filters. We begin by showing that the proximal operators coincide, and hence can be realized with, Bayes updates. We give the explicit form of the updates for the linear regression problem and show that there is a one-to-one correspondence between the proximal operator of the least-squares regression and the Bayes update when the prior and the likelihood are Gaussian. We then carry out this observation to a general sequential setting: We consider the incremental proximal method, which is an algorithm for large-scale optimization, and show that, for a linear-quadratic cost function, it can naturally be realized by the Kalman filter. We then discuss the implications of this idea for nonlinear optimization problems where proximal operators are in general not realizable. In such settings, we argue that the extended Kalman filter can provide a systematic way for the derivation of practical procedures.
In text classification, the problem of overfitting arises due to the high dimensionality, making regularization essential. Although classic regularizers provide sparsity, they fail to return highly accurate models. On the contrary, state-of-the-art group-lasso regularizers provide better results at the expense of low sparsity. In this paper, we apply a greedy variable selection algorithm, called Orthogonal Matching Pursuit, for the text classification task. We also extend standard group OMP by introducing overlapping group OMP to handle overlapping groups of features. Empirical analysis verifies that both OMP and overlapping GOMP constitute powerful regularizers, able to produce effective and super-sparse models. Code and data are available at: https://www.dropbox.com/sh/7w7hjns71ol0xrz/AAC\string_G0\string_0DlcGkq6tQb2zqAaca\string?dl\string=0 .
RIPE is a novel deterministic and easily understandable prediction algorithm developed for continuous and discrete ordered data. It infers a model, from a sample, to predict and to explain a real variable $Y$ given an input variable $X \in \mathcal X$ (features). The algorithm extracts a sparse set of hyperrectangles $\mathbf r \subset \mathcal X$, which can be thought of as rules of the form If-Then. This set is then turned into a partition of the features space $\mathcal X$ of which each cell is explained as a list of rules with satisfied their If conditions. The process of RIPE is illustrated on simulated datasets and its efficiency compared with that of other usual algorithms.