Within the Machine Learning for Artists workshop program in Opendot from 21st to 25th of November, we are proud to invite you to the Gene Kogan OpenTalk, on Wednesday 23rd at 7 pm in Opendot lab. A Beautiful and Interesting Game: a lecture by Gene Kogan on creative applications for Machine Learning algorithms This talk examines the rise of machine learning and artificial intelligence through the lens of artistic practice and creative subversion. Recent breakthroughs in scientific research, combined with the proliferation of big data and cheap GPU computing power, have dramatically increased the capacities of machine intelligence in a variety of domains. The tech titans have swiftly integrated them into most of their core services, whilst numerous startups have appeared to capitalize on emerging markets. At the same time, artists, boosted by independent open source implementations, have attempted to subvert and illuminate those same technologies, shedding light on the sometimes beautiful and sometimes dangerous new faculties of these powerful algorithms.

Online learning is becoming the norm in universities across the globe, bringing sweeping changes to the way we learn. But earlier this year on online graduate class at Georgia Tech took things a stage further. "Our Teaching Assistants are getting bogged down answering routine questions," said Ashok Goel, who teaches a graduate science course. Students in the class typically post 10,000 messages a semester on the Piazza forum for the course, many of which are either variations on a theme or simple logistical questions. To address this problem, Ashok turned to IBM Watson to create a virtual TA called Jill Watson who was trained on 40,000 posts and released to the wild on the live forum in March as an addition to the other eight TAs.

According to the Financial Times, Pablo Picasso once said, "Computers are useless. They can only give you answers." Unfortunately for us, computers may now be asking more questions than they answer. As a result, the possibilities are rather overwhelming, with answers more ambiguous and uncertain than straightforward. Similarly, we might ask ourselves where we draw the line when it comes to what we find ethically acceptable in terms of artificial intelligence (AI) as it relates to composition/creation in the worlds of art, writing, performing arts and music--as well as liberal arts education. Most of us are aware of music streaming services that select songs for us based on data about users' listening preferences.

This screencast shows how to use Skymind's DataVec to ingest Comma Separated Values from a text file, convert the fields to numeric using a DataVec Transform Process in Spark, and save the modified data. Transforming non-numeric data to numeric data is a key preliminary step to using a Neural Network to analyze the data.

This screencast shows how to use Skymind's DataVec to build an image pipeline to prepare data for processing in a Neural Network using DeepLearning4j. Topics covered include, ParentPathLabelGenerator and Image Scaling.

Tom provides an overview of how to build a simple neural net in this introductory tutorial. This screencast shows how to build a Linear Classifier using Deeplearning4j.

R is a statistical programming language that allows you to build probabilistic models, perform data science, and build machine learning algorithms. R has a great package ecosystem that enables developers to conduct data visualization to data analysis.This video covers advanced-level concepts in R programming and demonstrates industry best practices. This is an advanced R course with an intensive focus on machine learning concepts in depth and applying them in the real world with R. We start off with pre-model-building activities such as univariate and bivariate analysis, outlier detection, and missing value treatment featuring the mice package. We then take a look linear and non-linear regression modeling and classification models, and check out the math behind the working of classification algorithms. We then shift our focus to unsupervised learning algorithms, time series analysis and forecasting models, and text analytics.

During the christmas vacation of 2015, I got a motivational boost again and decided try out Kaggle. So I spent quite some time experimenting with various algorithms for their Homesite Quote Conversion, Otto Group Product Classification and Bike Sharing Demand contests. The main takeaway from this was the experience of iteratively improving the results by experimenting with the algorithms and the data. I learned to trust my logic when doing machine learning. If tweaking a parameter or engineering a new feature seems like a good idea logically, it's quite likely that it actually will help.

A few months ago I demonstrated how to install the Keras deep learning library with a Theano backend. In today's blog post I provide detailed, step-by-step instructions to install Keras using a TensorFlow backend, originally developed by the researchers and engineers on the Google Brain Team. I'll also (optionally) demonstrate how you can integrate OpenCV into this setup for a full-fledged computer vision deep learning development environment. The first part of this blog post provides a short discussion of Keras backends and why we should (or should not) care which one we are using. From there I provide detailed instructions that you can use to install Keras with a TensorFlow backend for machine learning on your own system.

Many prediction problems can be phrased as inferences over local neighborhoods of graphs. The graph represents the interaction between entities, and the neighborhood of each entity contains information that allows the inferences or predictions. We present an approach for applying machine learning directly to such graph neighborhoods, yielding predicitons for graph nodes on the basis of the structure of their local neighborhood and the features of the nodes in it. Our approach allows predictions to be learned directly from examples, bypassing the step of creating and tuning an inference model or summarizing the neighborhoods via a fixed set of hand-crafted features. The approach is based on a multi-level architecture built from Long Short-Term Memory neural nets (LSTMs); the LSTMs learn how to summarize the neighborhood from data. We demonstrate the effectiveness of the proposed technique on a synthetic example and on real-world data related to crowdsourced grading, Bitcoin transactions, and Wikipedia edit reversions.