This DoNut Network contains uses The variational auto-encoder ("Auto-Encoding Variational Bayes",Kingma, D.P. and Welling) which is a deep Bayesian network, with observed variable x and latent variable z. The VAE is generated using TFSnippet (library for writing and testing tensorflow models). The generative process of Auto-Encoder is initiated with parameter z with prior distribution p(z), and a hidden network h(z), then uses observed variable x with distribution p(x h(z)). The posterior inference p(z x), variational inference techniques are adopted, to train a separated distribution q(z h(x)). Here each Sequential function creates a multi-layer perception, with 2 hidden layers of 50 units and RELU activation.

Looking to meet enterprise needs in the machine learning space, Oracle is making its Tribuo Java machine learning library available free under an open source license. With Tribuo, Oracle aims to make it easier to build and deploy machine learning models in Java, similar to what already has happened with Python. Released under an Apache 2.0 license and developed by Oracle Labs, Tribuo is accessible from GitHub and Maven Central. Tribuo provides standard machine learning functionality including algorithms for classification, clustering, anomaly detection, and regression. Tribuo also includes pipelines for loading and transforming data and provides a suite of evaluations for supported prediction tasks. Because Tribuo collects statistics on inputs, Tribuo can describe the range of each input, for example.

Find high moving stocks before they move using anomaly detection and machine learning. Surpriver uses machine learning to look at volume price action and infer unusual patterns which can result in big moves in stocks. You will need to install the following package to train and test the models. You can install all packages using the following command. Please note that the script was written using python3.

Tribuo is a Java ML library for multi-class classification, regression, clustering, anomaly detection and multi-label classification.

Hi, I'm introducing a book I wrote, "Hands-on Machine Learning with C ." It can help you to discover new ways of using C for Machine learning development. It will show you how to use C libraries for different aspects of ML from fundamentals to real-world examples. You will see examples of product recommendations, ensemble learning, anomaly detection, image classification, sentiment analysis, and others. Programming examples are written with modern C libraries such as PyTorch C API, Caffe2, Shogun, Shark-ML, mlpack, and dlib. Also, you'll learn how to handle production and deployment challenges on mobile and cloud platforms.

Anomaly Detection has become one of the most useful machine learning tools of the past five years. It can be used from fraud to quality control. Is it possible to isolate fraudsters in online review websites? Can fraudulent financial transactions be detected as they occur? Can live sensor data inform about power grid failures before they happen?

Anomaly detection is the act of identifying an event or item that doesn't conform to others in an expected pattern. Your bank may call you now and then to ask about a specific charge on your credit card because it was unlike most of your purchases. Therefore, it was flagged so customer service would call (or text) you to ask if you made the purchase or if perhaps it was a fraudulent transaction. In healthcare, medical issues or textual errors can be identified and flagged. It has been able to improve image analysis efficiency by flagging specific anomalies in an image so that a radiologist can take a closer look.

Artificial intelligence applied to healthcare wasn't on the radar ten years ago. Outside of academic circles, relatively few people were even aware of the potential of the technology. It seemed like a distant fantasy. Today, though, that's all changed. Artificial intelligence is the new trend in innovation, and everyone in the sector is talking about it.

ARIMA model means Autoregressive Integrated Moving Average. This model provides a family of functions which are a very powerful and flexible to perform any task related to Time Series Forecasting. In Machine Learning ARIMA model is generally a class of statistical models that give outputs which are linearly dependent on their previous values in the combination of stochastic factors. While choosing an appropriate time series forecasting model, we need to visualize the data to analyse the trends, seasonalities, and cycles. When seasonality is a very strong feature of the time series we need to consider a model such as seasonal ARIMA (SARIMA).

As Artificial Intelligence is becoming a mainstream and easily available commercial technology, both organizations and criminals are trying to take full advantage of it. In particular, there are predictions by cyber security experts that going forward, the world will witness many AI-powered cyber attacks1. This mandates the development of more sophisticated cyber defense systems using autonomous agents which are capable of generating and executing effective policies against such attacks, without human feedback in the loop. In this series of blog posts, we plan to write about such next generation cyber defense systems. One effective approach of detecting many types of cyber threats is to treat it as an anomaly detection problem and use machine learning or signature-based approaches to build detection systems.