Category Archive Machine Learning

ByEric Muccino

Visualizing How Convolution Neural Networks “See”

Convolution Neural Networks (CNN) learns image regognition the way human visual system does. It scans images by using filters which recognizes a unique feature. A little deeper layers identify low level features such as curves and edges, while the deeper layers idtentifies high level features such as eyes or windows. We use Keras library to visualize what CNN are learning to look when making a certain classfication.

Read the rest of the article at Mindboard’s Medium channel.

ByGuru Natarajan

Policy Assessment Using ML

In the 21st century, every person and organization, both public and private, are somehow connected. So, being able to quickly understand and efficiently analyze whether your third-party policy documents such as NIST 800–171, ISO 27001, ISO 9001, etc., meet the standards you set for them is critical to the success of your business. Current policy assessment tools are manual, inefficient, and don’t adequately reduce risk.

We at Mindboard developed a platform to solve these problems. We are utilizing machine-learning, semantic technology, a repository of standard-meeting model documents we provide the most advanced and efficient methodology for automating and evaluating policy documents.

Read the rest of the article at Mindboard’s Medium channel.

ByEric Muccino

Generating Adversarial Samples in Keras (Tutorial)

As deep learning technologies power increasingly more services, associated security risks become more critical to address. Adversarial Machine Learning is a branch of machine learning that exploits the mathematics underlying deep learning systems in order to evade, explore, and/or poison machine learning models. Evasion attacks are the most common adversarial attack method due to their ease of implementation and potential for being highly disruptive. During an evasion attack, the adversary tries to evade a fully trained model by engineering samples to be misclassified by the model. This attack does not assume any influence over the training data.

Evasion attacks have been demonstrated in the context of autonomous vehicles where the adversary manipulates traffic signs to confuse the learning model. Research suggests that deep neural networks are susceptible to adversarial based evasion attacks due to their high degree of non-linearity as well as insufficient model averaging and regularization.

Read the rest of the article at Mindboard’s Medium channel.

ByEric Muccino

Active Learning for Fast Data Set Labeling

Active learning is a special case of machine learning where a model can query a user for input. In this post, we will see how we can use active learning to label large data sets. For most machine learning tasks, large amounts of labeled data is needed is need for model training. However, the process of labeling data can be extremely time consuming and/or expensive. Using active learning, we can leverage a classification model to do most of the labeling for us, so that we only need to label samples when it is most needed.

Read the rest of the article at Mindboard’s Medium channel.

ByGuru Natarajan

Serving Machine Learning Models Using TensorFlow Serving

Exploring how TensorFlow models can be served using TensorFlow Serving…

Read the article at Mindboard’s Medium channel.

ByGuru Natarajan

Deploying Machine Learning Models Using Docker

Productionize the Flask API for deployment using Docker via nginx, gunicorn and Docker Compose to create a scalable template for deploying machine learning models.

Read the rest of the article at Mindboard’s Medium channel

ByEric Muccino

Investigating RNN Memory Stability

A Recurrent Neural Networks (RNN) is a class of Artificial Neural Network that contains connections along a temporal axis, producing a functioning memory of prior network inferences that influences the network’s output. Two of the most common types of RNN are the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells. LSTMs and GRUs are designed for long-term memory capability. In both cases, the RNN cell maintains a hidden memory state that undergoes an alteration after every inference call.

Read the rest of the article at Mindboard’s Medium channel.

BySaiGayatri Vadali

Q Matrix Update to train Deep Recurrent Q Networks More Effectively

Deep Recurrent Q network, as discussed in previous article, can be very helpful in building smart agents that remember their learning from distant past. This feature makes a Deep Recurrent Q network a valuable function approximator in building AI agents for Deep Reinforcement Learning.

Read the rest of the article at Mindboard’s Medium channel.

BySaiGayatri Vadali

Getting started with Data Science in 30 days using R programming

This series of posts is an attempt to help those who are in this pursuit and introduce them to the world of Data using the most widely popular language – R. Here is a collection of the first 12 articles.

ByGuru Natarajan

Blocking Inappropriate Images in Search Results

Most Internet porn filters apply the all-or-nothing approach to blocking websites and typically don’t block popular search engines, rightly so.  These search engines are the key to the internet’s potential.  While they are generally well protected using features like safe search, there are also glaring misses, especially when one looks at the images served up by the search. Seemingly innocent searches can throw up inappropriate images and several search engines optimize their performance by embedding images into the html as a data.  This breaks most internet filters since filters operate by blocking entire websites using a static list.  

Enter the vRate Chrome extension.  By running within the browser, vRate has the ability to analyze content within the page, even if the image is embedded as data instead of a URL.  vRate overcomes what traditional internet filters have failed at, by applying a combination of approaches to block inappropriate content.  In addition to using static blacklists, vRate analyzes dynamic content on the page, specifically images and video thumbnails to filter inappropriate content.   Seen below is a sample search result with vRate enabled.

Google search result
Google search result

We tried searching an “innocent” key word.  In this case vRate replaced inappropriate images inline, this is because the search results for the most part were benign, except for the odd one or two. However, vRate can also automatically re-direct to a block page if the number of inappropriate images exceeds thresholds and this can be controlled through our sensitivity settings.

The Chrome extension is currently available for free preview, so do download and test drive it, especially check out how search results are handled.