Deeplearning4j's Github repository has many examples to cover its functionality. The Quick Start Guide shows you how to set up Intellij and clone the repository. This page provides an overview of some of those examples. Most of the examples make use of DataVec, a toolkit for preprocessing and clearning data through normalization, standardization, search and replace, column shuffles and vectorization. Reading raw data and transforming it into a DataSet object for your Neural Network is often the first step toward training that network.

Give us your feedback Thank you for your feedback. Artificial intelligence is one of the important technological advances of the early 21st century. Already it has meant that machines can read medical images as well as a radiologist, and enabled the auto industry to develop autonomous cars. The technology is in danger of being overrated, however, and considerably more work is needed before we can reach the long-dreamt-of moment when machine intelligence matches the human variety. When we discuss AI today we are mainly referring to just one facet of it: deep learning.

We already know that algorithms are ruling the world. Consider it kind of God or Ghost from the machine powering them all, for good or evil -- you decide. Soon Machine Learning Algorithms will be able to accurately guide forward-looking business decisions and reveal behaviors never before seen. Gartner says that Automation and Machine Learning will shape the future of governments. Artificial Intelligence and Machine Learning is the Trend no 1 in Garner's Top 10 Strategic Technology Trends for 2017 Can computers really learn mom's art of cucumber sorting?

Major iPhone assembler Hon Hai Precision Industry, also known as Foxconn, on Friday pledged to dedicate a minimum of $342 million to turn itself into a key artificial intelligence player as the Taiwanese tech conglomerate eyes new growth opportunities beyond smartphones. "We will at least invest some 10 billion New Taiwan dollars ($342 million) over five years to recruit top talent and deploy artificial intelligence applications in all the manufacturing sites," said Chairman Terry Gou. "It's likely that we could even pour in some $10 billion or more if we find the deployments are very successful or can really generate results," said Gou. Gou added that his company aimed to recruit up to 100 top AI experts globally and would open up thousands of jobs for young talent should they have good ideas on how to develop applications using machine learning and deep learning techniques. His team is already looking globally for different types of sensors that can be built into production line equipment to better sense, capture and process data, according to Gou. "We will become a global innovative AI platform rather than just a manufacturing company," said Gou. Foxconn's move to invest heavily in AI comes as it faces mounting challenges to grow its core manufacturing business as global smartphone demand slows. Although its largest customer Apple on Feb. 1 reported record revenue for the last holiday season quarter, the US tech group sold fewer units of the iPhone X than expected and gave a weak outlook for the current quarter.

Want to watch this again later? Sign in to add this video to a playlist. Report Need to report the video? Sign in to report inappropriate content. Report Need to report the video?

Artificial Intelligence, Machine Learning, and Deep Learning are revolutionizing the financial technology industry. Machine Learning and Deep Learning are a growing and diverse fields of Artificial Intelligence (AI) which studies algorithms that are capable of automatically learning from data and making predictions based on data. Machine Learning and Deep Learning are two of the most exciting technological areas of AI today. Each week there are new advancements, new technologies, new applications, and new opportunities. That's why we created this guide to help you keep pace with all these exciting developments.

Hey, I've signed up on the site because of this thread. I find this stuff fascinating, but agree with the last 2 posts in that developing a general AI for games like Civ using machine learning isn't presently achievable. But, do you think it'd be possible to train an AI only for combat scenarios, and have the game use its deep learning derived algorithms only when it comes to warfare? I'm talking just moving units, attacking and defending, coming up with tactics to complete military objectives that a general, normal, preprogrammed AI sets. For example, the general AI sets the objective "I don't want to lose X city" and hands control of it's military (or a part of it) to the deep learning AI that's trained for warfare, in order to defend the city.

Large-scale data gathering and quantum leaps in processing power have set the table for major advancement in artificial intelligence. Yet there's a growing body of evidence that the field of AI is poised to move into a whole new dimension, one where AI not only imagines the real world, but can begin to make accurate decisions on what's real and important, what's not -- and thus predict what's coming next. "Computers are really good at memorization," Carl Vondrick, research scientist at Google Inc., said during a presentation at the Re-Work Deep Learning Summit in San Francisco Thursday. "The problem is teaching them how to forget." Vondrick's research has focused on one of the most vexing challenges in today's online world: how to make use of the massive database of unlabeled videos that clog nearly every corner of the web.

The restricted Boltzmann machine (RBM) is one of the fundamental building blocks of deep learning. RBM finds wide applications in dimensional reduction, feature extraction, and recommender systems via modeling the probability distributions of a variety of input data including natural images, speech signals, and customer ratings, etc. We build a bridge between RBM and tensor network states (TNS) widely used in quantum many-body physics research. We devise efficient algorithms to translate an RBM into the commonly used TNS. Conversely, we give sufficient and necessary conditions to determine whether a TNS can be transformed into an RBM of given architectures. Revealing these general and constructive connections can cross-fertilize both deep learning and quantum many-body physics. Notably, by exploiting the entanglement entropy bound of TNS, we can rigorously quantify the expressive power of RBM on complex data sets. Insights into TNS and its entanglement capacity can guide the design of more powerful deep learning architectures. On the other hand, RBM can represent quantum many-body states with fewer parameters compared to TNS, which may allow more efficient classical simulations.

Multi-camera full-body pose capture of humans and animals in outdoor environments is a highly challenging problem. Our approach to it involves a team of cooperating micro aerial vehicles (MAVs) with on-board cameras only. The key enabling-aspect of our approach is the on-board person detection and tracking method. Recent state-of-the-art methods based on deep neural networks (DNN) are highly promising in this context. However, real time DNNs are severely constrained in input data dimensions, in contrast to available camera resolutions. Therefore, DNNs often fail at objects with small scale or far away from the camera, which are typical characteristics of a scenario with aerial robots. Thus, the core problem addressed in this paper is how to achieve on-board, real-time, continuous and accurate vision-based detections using DNNs for visual person tracking through MAVs. Our solution leverages cooperation among multiple MAVs. First, each MAV fuses its own detections with those obtained by other MAVs to perform cooperative visual tracking. This allows for predicting future poses of the tracked person, which are used to selectively process only the relevant regions of future images, even at high resolutions. Consequently, using our DNN-based detector we are able to continuously track even distant humans with high accuracy and speed. We demonstrate the efficiency of our approach through real robot experiments involving two aerial robots tracking a person, while maintaining an active perception-driven formation. Our solution runs fully on-board our MAV's CPU and GPU, with no remote processing. ROS-based source code is provided for the benefit of the community.