We introduce a novel multivariate random process producing Bernoulli outputs per dimension, that can possibly formalize binary interactions in various graphical structures and can be used to model opinion dynamics, epidemics, financial and biological time series data, etc. We call this a Bernoulli Autoregressive Process (BAR). A BAR process models a discrete-time vector random sequence of $p$ scalar Bernoulli processes with autoregressive dynamics and corresponds to a particular Markov Chain. The benefit from the autoregressive dynamics is the description of a $2^p\times 2^p$ transition matrix by at most $pd$ effective parameters for some $d\ll p$ or by two sparse matrices of dimensions $p\times p^2$ and $p\times p$, respectively, parameterizing the transitions. Additionally, we show that the BAR process mixes rapidly, by proving that the mixing time is $O(\log p)$. The hidden constant in the previous mixing time bound depends explicitly on the values of the chain parameters and implicitly on the maximum allowed in-degree of a node in the corresponding graph. For a network with $p$ nodes, where each node has in-degree at most $d$ and corresponds to a scalar Bernoulli process generated by a BAR, we provide a greedy algorithm that can efficiently learn the structure of the underlying directed graph with a sample complexity proportional to the mixing time of the BAR process. The sample complexity of the proposed algorithm is nearly order-optimal as it is only a $\log p$ factor away from an information-theoretic lower bound. We present simulation results illustrating the performance of our algorithm in various setups, including a model for a biological signaling network.

Pat Bagley is easily my favorite political cartoonist, period. For the politically aware in Utah, he is almost legendary, enjoying superstar status. I've been aware of him since I was a kid, and I always loved his cartoons. Not only does his artistic style appeal to me, he has a way of illustrating a situation in politics that explains it more clearly than a thousand words. His cartoons are humorous, though darkly so. And with every one, you can't help but feel he's had the last word.

So you report with reasonable accuracies what the sentiment about a particular brand or product is. After publishing this report, your client comes back to you and says "Hey this is good. Now can you tell me ways in which I can convert the negative sentiments into positive sentiments?" – Sentiment Analysis stops there and we enter the realms of Opinion Mining. Opinion Mining is about having a deeper understanding of the review that was written. Typically, a detailed review will not just have a sentiment attached to it. It will have information and valuable feedback that can literally help to build the next strategy.

This is the official code repository for Machine Learning with TensorFlow. Warning: The book will be released in a month or two, so this repo is a pre-release of the entire code. I will be heavily updating this repo in the coming weeks. Stay tuned, and follow along! Get started with machine learning using TensorFlow, Google's latest and greatest machine learning library.

Both Machine Learning and Artificial Intelligence has become poplar topics in the tech world recently. With the uprise of products using either Machine Learning or Artificial Intelligence, or both, there's bound to be more jobs popping up for people with those qualifications. Which makes this bundle pretty important. And right now we are offering up The Machine Learning and Artificial Intelligence Bundle for just $39, that's 91% off of the regular price, making it a great time to pick up this bundle. There are four courses included in this bundle, each are typically $120.

Easy Natural Language Processing (NLP) in Python – Over this course you will build multiple practical systems using natural language processing (NLP), the branch of machine learning and data science that deals with text and speech. You'll start with a background on NLP before diving in, building a spam detector and a model for sentiment analysis in Python. Learning how to build these practical tools will give you an excellent window into the mechanisms that drive machine learning. Unsupervised Machine Learning Hidden Markov Models in Python – Data, in many forms, is presented in sequences: stock prices, language, credit scoring, etc. Being able to analyze them, therefore, is of invaluable importance. In this course you'll learn a machine learning algorithm – the Hidden Markov Model – to model sequences effectively.

This will drastically increase your ability to retain the information.

It emphasizes flexibility through the elegant use of object-oriented design patterns.

How do spam detectors work - and how do you build one? How do you make it easier to find patterns in huge amounts of data? The answer is machine learning and artificial intelligence - and you can be a part of the next great tech frontier with help from this four-course bundle. Plus you can get the Machine Learning and Artificial Intelligence Bundle for 91% off at Escapist Deals. Cluster Analysis and Unsupervised Machine Learning in Python: A 1.5-hour training in cluster analysis, used in data mining and big data.

Weka has a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. Massive Online Analysis (MOA) is a popular open source framework for data stream mining, with a very active growing community. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation.