Talking Machines, 12 episodes, iTunes An interview format based podcast with Hosts, Katherine Gorman and Ryan Adams, who bring clear conversations with experts in the machine learning field. Partially derivative, 23 episodes, iTunes A show about data science, interesting new projects, latest data news and all these conversations over a beer which makes it a good entertainer!! [Latest] Episode 23: Political Science Rulez This week Chris overcompensates for his love of political science while Jonathon continues to be unimpressive. The Data Skeptic, 56 episodes, iTunes This podcast features conversations on topics related to data science, statistics, machine learning, artificial intelligence. It alternates between mini episodes which are quick introductions to concepts and long form episodes which are usually interviews with experts in the field. CyArk is a non-profit focused on using technology and data to preserve the world's important historic and cultural locations digitally.

It seems like strange advice: if your phone is full up with rubbish and you want to free some of the storage up, then download even more rubbish. A new trick lets people get extra space on their iPhone just by downloading some new apps, and by tricking the phone into believing that it needs to clear up some space. To do it, all you need to do is to download a big app that takes up more space on your phone than you have left. As soon as you do so, your iPhone will automatically start clearing up much-needed storage space. A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

Since about 100 years ago, to learn the intrinsic structure of data, many representation learning approaches have been proposed, including both linear ones and nonlinear ones, supervised ones and unsupervised ones. Particularly, deep architectures are widely applied for representation learning in recent years, and have delivered top results in many tasks, such as image classification, object detection and speech recognition. In this paper, we review the development of data representation learning methods. Specifically, we investigate both traditional feature learning algorithms and state-of-the-art deep learning models. The history of data representation learning is introduced, while available resources (e.g. online course, tutorial and book information) and toolboxes are provided. Finally, we conclude this paper with remarks and some interesting research directions on data representation learning.

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.