Earlier than Jun-18-2017

Machine Learning


The path to becoming a Data Scientist is a lot of learning. That's why they're making the big bucks. After being dubbed "sexiest job of the 21st Century" by Harvard Business Review, data scientists... To know more log on to www.extentia.com

Deep Learning for Semantic Segmentation of Aerial Imagery - Azavea - Beyond Dots on a Map


As part of the challenge, ISPRS released a benchmark dataset containing 5cm resolution imagery having five channels including red, green, blue, IR and elevation. Part of the dataset had been labeled by hand with six labels including impervious, buildings, low vegetation, trees, cars, and clutter, which accounts for anything not in the previous categories. The Fully Convolutional Network (FCN) approach to semantic segmentation works by adapting and repurposing recognition models so that they are suitable for segmentation. The input to the network consisted of red, green, blue, elevation, infrared, and NDVI channels.

MAGAZINE: Artificial Intelligence in Banking Finance Digest Magazine


The Tokyo Stock Exchange has confirmed that it is deploying machine learning AI tools as the market surveillance solution to investigate potential illegal trading practices. Despite significant investments in back-end processing and compliance, many banking systems – specifically the areas of payments processing, repair, routing and investigations – remain highly inefficient. The context learning and natural language processing capabilities of AI-based payments systems have over the past two decades been proven to dramatically increase straight through processing rates, enable intelligent least cost routing, and result in the removal of inefficient manual interventions and repairs – though these AI benefits have largely been the preserve of larger transaction banks. Other areas also benefiting from AI-based systems include automation of exceptions, investigations and customer retention.Whether utilising the power of natural language processing to fundamentally transform the way customers interact with the Bank, or leveraging the data insights, speed of deployment and increased sales opportunities provided by machine learning technology, AI can enable Banks to achieve lower costs, increase revenue, accelerate processing time and reduce errors across the board.

Everyone keeps talking about AI--here's what it really is and why it's so hot now


Machine learning generally entails teaching a machine how to do a particular thing, like recognizing a number, by feeding it a bunch of data and then directing it to make predictions on new data. The big deal about machine learning now is that it's getting easier to invent software that can learn over time and get smarter as it accumulates more and more data. Machine learning often requires people to hand-engineer certain features for the machine to look for, which can be complex and time-consuming. Deep learning is one type of machine learning that demands less hand-engineering of features.

Facebook Bot Learns to Lie to Get What It Wants The Deep State


In an experiment by Facebook's AI researchers, chatbots were paired off and set the task of dividing a collection of items among themselves. The AI system learned to negotiate by analyzing each side of almost 6,000 human conversations. Facebook tested the bots against humans – most of whom did not realize they were engaging with AI, according to the researchers. The best negotiation bot equaled the skill of human negotiators and achieved better deals about as often as worse deals.

Tesla mechanics rescue a cute kitten that got stuck inside a Model X bumper


Of all the things that could possibly go wrong with your electric self-driving car, finding a kitten inside your bumper may be the cutest of them all. After narrowing down the meows to his vehicle, then the rear bumper, the man decided to bring his car into a Tesla service center to get a little help rescuing the cat safely. SEE ALSO: Tesla's Model X gets the first perfect SUV crash rating The technicians were able to remove a small panel from underneath the rear bumper, and after a little coaxing, the very upset orange baby kitten was safely removed. Tesla service rescues a kitten stuck in the bumper https://t.co/qZW7QjuceQhttps://t.co/3jyJseVodm Tesla founder and CEO Elon Musk tweeted the two clips on Saturday, which is not an unusual move.



Basically, each X(t n) consists of a full set of connections that are input at that particular timestep of the sequence. Also not shown are the fact that each gate and cell has it's own set of weights and biases for both the input and recurrent connections. Thus, an LSTM actually has four sets of input and recurrent weight and bias parameters. In practice this means that usually the input is represented as a tensor with three dimensions (batch, timestep, input).

Python, Machine Learning, and Language Wars. A Highly Subjective Point of View


Well, I typically run an analysis only once (the testing of different ideas and debugging aside); I don't need to repeatedly run a particular piece of code 24/7, I am not developing software applications or web apps for end users. If you are part of the software engineering team that wants optimize the next game-changing high-frequency trading model from your machine learning and data science division, Python is probably not for you (but maybe it was the language of choice by the data science team, so it may still be useful to learn how to read it). Well, I know C and FORTRAN, and if I implement those algorithms in the respective languages executing the "screening" run may be faster compared to a Python implementation. Since it was built with linear algebra in mind (MATLAB for MATrix LABoratory), MATLAB feels a tad more "natural" when it comes to implementing machine learning algorithms compared to Python/NumPy – okay, to be fair, 1-indexed programming languages may seem a little bit weird to us programmers.

A Tutorial on the Expectation Maximization (EM) Algorithm


The Expectation Maximization (EM) algorithm can be used to generate the best hypothesis for the distributional parameters of some multi-modal data. The best hypothesis for the distributional parameters is the maximum likelihood hypothesis – the one that maximizes the probability that this data we are looking at comes from K distributions, each with a mean mk and variance sigmak2. We then proceed to take each data point and answer the following question – what is the probability that this data point was generated from a normal distribution with mean mk and sigmak2? These two steps of estimating the distributional parameters and updating them after probabilistic data assignments to clusters is repeated until convergences to h*.

Top Machine Learning, Deep Learning, NLP, and Data Mining Libraries


It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming. Scikit-learn (formerly scikits.learn) is a free software machine learning library for the Python programming language. Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. Machine Learning for Language Toolkit (MALLET) is a Java toolkit fro statistical natural language processing, document classification, clustering, topic modeling and information extraction.