New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
Could a robot do my job as a radiologist? If you asked me 10 years ago, I would have said, "No way!" But if you ask me today, my answer would be more hesitant, "Not yet -- but perhaps someday soon." In particular, new "deep learning" artificial intelligence (AI) algorithms are showing promise in performing medical work which until recently was thought only capable of being done by human physicians. For example, deep learning algorithms have been able to diagnose the presence or absence of tuberculosis (TB) in chest x-ray images with astonishing accuracy.
The Deep Learning Summit is returning to Toronto from October 25 – 26, 2018 and will cover the latest advancements in deep learning technology. Global leaders in the field will address how industry leaders and start-ups are applying deep learning techniques across industry and society. The first ever AI for Government Summit, another event stream, will provide a unique opportunity to interact with government bodies, policymakers, strategists and directors of innovation to explore the use of machine learning to increase efficiency, reduce costs and meet the high demands of the public sector. What's more, the Canadian Government have committed over $125 million to AI developments. Headline partners include Accenture, Qualcomm, Graphcore AI and CBC/Radio Canada who will all be sharing their expertise in the field, participating in workshops, discussions, presentations, demonstrations and exhibitions.
In this post, I will try to shed some light on the intuition about Restricted Boltzmann Machines and the way they work. This is supposed to be a simple explanation with a little bit of mathematics without going too deep into each concept or equation. So let's start with the origin of RBMs and delve deeper as we move forward. Boltzmann machines are stochastic and generative neural networks capable of learning internal representations and are able to represent and (given sufficient time) solve difficult combinatoric problems. They are named after the Boltzmann distribution (also known as Gibbs Distribution) which is an integral part of Statistical Mechanics and helps us to understand the impact of parameters like Entropy and Temperature on the Quantum States in Thermodynamics.
The underwater ocean world is an ecosystem with lots of different sounds. So naval forces have traditionally relied on so-called "golden ears," or musicians and other individuals with particularly sharp hearing, to detect the specific signals coming from an enemy submarine. But given the overload of data today, distinguishing between false alarms and actual dangers has become more difficult. That's why "Thales is working on "Deep Learning" algorithms capable of recognizing the particular "song" of a submarine, much as the "Shazam" app helps you identify a song you hear on the radio", says Dominique Thubert, Thales Underwater Systems, which is specialized in sonar systems for submarines, surface warships, and aircraft. These algorithms, attached to submarines, surface ship or drones, will help naval forces sort through and classify information in order to detect attacks early on.
Research Using CheXNet at Stanford: CheXNet is a deep learning Convolutional Neural Network (CNN) model developed at Stanford University to identify thoracic pathologies from the NIH ChestXray14 dataset. CheXNet is a 121-layer CNN that uses chest X-Ray images to predict the output probabilities of a pathology. It correctly detects pneumonia by localizing the areas in the image that are most indicative of the pathology. Stanford researchers have been able to train the ChestX-Ray14 dataset using a pre-trained model of CheXNet-121 with the ImageNet2012-1K dataset. The NIH dataset consists of over one hundred thousand frontal chest X-ray images from over 30,000 unique patients that have been annotated with up to 14 thoracic diseases including pneumonia and emphysema.
And more broadly, why should you use JVM languagues like Java, Scala, Clojure or Kotlin to build AI and machine-learning solutions? Java is the most widely used programming language in the world. Large organizations in the public and private sector have enormous Java code bases, and rely heavily on the JVM as a compute environment. In particular, much of the open-source big data stack is written for the JVM. This includes Apache Hadoop for distributed data management; Apache Spark as a distributed run-time for fast ETL; Apache Kafka as a message queue; ElasticSearch, Apache Lucene and Apache Solr for search; and Apache Cassandra for data storage to name a few.
As world rolling around Artificial Intelligence (AI), demand for the AI-based product seen exponential growth, so the AI research. Deep learning algorithms and techniques are widely used for research and development of these products. Good news is that year by year Deep Learning has seen its glory in the release of many open source frameworks which ease the pain to develop and implement these algorithms. As there are many deep learning frameworks out there and it can lead to confusion as to which one is better for your task. And choosing a deep learning framework for an AI project is as important as choosing a programming language to code product, Data science project coupled with the right deep learning framework has truly amplified the overall productivity.
Graph Nets is DeepMind's library for building graph networks in Tensorflow and Sonnet. A graph network takes a graph as input and returns a graph as output. The input graph has edge- (E), node- (V), and global-level (u) attributes. The output graph has the same structure, but updated attributes. Graph networks are part of the broader family of "graph neural networks" (Scarselli et al., 2009).
Holding certain layers frozen on a network and training is effectively the same as training on a transformed version of the input, the transformed version being the intermediate outputs at the boundary of the frozen layers. This is the process of "feature extraction" from the input data and will be referred to as "featurizing" in this document. The forward pass to "featurize" the input data on large, pertained networks can be time consuming. DL4J also provides a TransferLearningHelper class with the following capabilities. When running multiple epochs users will save on computation time since the expensive forward pass on the frozen layers/vertices will only have to be conducted once.
Last week we wrapped up a highly successful GPU Technology Conference (GTC) Europe in Munich! GTC is NVIDIA's international conference series, bringing together the top minds in deep learning, analytics, and of course GPUs for sessions, workshops, keynotes, and more. This was the place to be for any and all European organizations interested in leveraging the power of the GPU. As the Kinetica engine runs on GPUs, there's no better place for us to share our solutions for advanced analytics and deep learning. This year we noticed a significant increase in the number of organizations that understand the challenges of the Extreme Data Economy.