Linear classifiers perform classification based on the linear combinition of the component features. Some examples of Linear Classifiers include: Naive Bayes Classifier, Linear Discriminant Analysis, Logistic Regression and Perceptrons. ArrayFire's easy to use API enables users to write such classifiers from scratch fairly easily. In this post, we show how you can map mathematical equations to ArrayFire code and implement them from scratch. Naive Bayes Classifier Perceptron Naive Bayes Classifier Naive bayes classifier is a probabilistic classifier that assumes all the features in a feature vector are independent of each other. This assumption simplifies the bayes rule to a simple multiplication of probabilities as show below. First we start with the simple Baye's rule. Where: Since p(x) is the same ...

## ArrayFire Examples (Part 5 of 8) - Machine Learning

This is the fifth in a series of posts looking at our current ArrayFire examples. The code can be compiled and run from arrayfire/examples/ when you download and install the ArrayFire library. Today we will discuss the examples found in the machine_learning/ directory. In these examples, my machine has the following configuration:

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ArrayFire v1.9 (build XXXXXXX) by AccelerEyes (64-bit Mac OSX) License: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX CUDA toolkit 5.0, driver 304.54 GPU0 GeForce GT 560M, 1024 MB, Compute 3.0 (single,double) Display Device: GPU0 GeForce GT 650M Memory Usage: 245 MB free (1024 MB total)... |

1. K-Means Clustering - kmeans.cpp Figure 1 This is an example of K-Means Clustering Algorithm. K-Means Clustering Algorithm is a data mining technique that partitions the given data into groups by their similarities. All you need to know from this example is that this algorithm will repeatedly run through many loops to stabilize the clusters. Again, ArrayFire has this beautiful tool called GFOR loop that handles any matrix array arithmetic and transformation in parallel. This increases the speed of both ...

## Webinar - Machine Learning with ArrayFire

In case you missed it, we recently held a webinar on the ArrayFire GPU Computing Library and its applications to Machine Learning on June 15. This webinar was part of a free series of webinars that help you learn about ArrayFire and Jacket (our MATLAB® product). Anyone can attend these webinars, for they are absolutely free and open for anyone to attend and interact with AccelerEyes engineers. Learn more at http://www.accelereyes.com/webinars. Chris, a Software Engineer at AccelerEyes, explained ArrayFire's position in the GPU computing world, and presented benchmarks where ArrayFire beats GPU libraries such as Thrust in many critical applications. He also mentioned that ArrayFire could be used either standalone, or in combination with other options for GPU computing such ...

## Optimization methods for deep learning

Researchers at SAIL (Stanford Artificial Intelligence Laboratory), have done it again. They have successfully used Jacket to speed up the training part of Deep Learning algorithms. In their paper titled “On Optimization Methods for Deep Learning”, they experiment with some of the well known training algorithms and demostrate their scalability across parallel architectures (GPUs as well as multi-machine networks). The algorithms include SGDs (Stochastic Gradient Descent) L-BFGS (Limited BFGS used for solving non-linear problems), CG (Conjugate Gradient). While SGDs are easy to implement, they require manual tuning. Add to that their sequential nature, they are hard to tune, scale and parallelize making them difficult to use with Deep Learning algorithms. L-BFGS and CG algorithms can be harder to implement and ...

## Action Recognition with Independent Subspace Analysis

Researchers at the Stanford Artificial Intelligence Laboratory (SAIL) have had more success (building on previous work) using Jacket to speed up their algorithm. In a paper at this year's CVPR 2011, entitled “Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis”, they explain how their unsupervised feature learning algorithm competes with other algorithms that are hand crafted or use learned features. KTH Hollywood2 UCF Youtube Best published Results 92.1% 50.9% 85.6% 71.2% Stanford group Results 93.9% 53.3% 86.5% 75.8% Testing their algorithm on four well-known benchmark datasets, they were able to achieve better performance than existing results that have been published so far. For their training purposes, they used a multi-layered stacked convolutional ISA (Independent subspace analysis) ...