When we hear about Convolutional Neural Network (CNNs), we typically think of Computer Vision. CNNs were responsible for major breakthroughs in Image Classification and are the core of most Computer Vision systems today, from Facebook's automated photo tagging to self-driving cars. More recently we've also started to apply CNNs to problems in Natural Language Processing and gotten some interesting results. In this post I'll try to summarize what CNNs are, and how they're used in NLP. The intuitions behind CNNs are somewhat easier to understand for the Computer Vision use case, so I'll start there, and then slowly move towards NLP.

We have lot of amazing frameworks for deep learning which allow us easy and fast prototyping and learning complex architectures even not thinking about what happening inside of them. But sometimes you need to deploy your model somewhere… let's say where you can't use your favorite I recently faced this problem, when I had to deploy recurrent neural network for action recognition trained in Keras in Java. My client doesn't want to use some microservices architecture, he wants everything in Java and basta cosi:) Embedding is vector length of 11, hidden units 15. First, let's load our weights from .hdf5 And if we check one of the most popular tutorials in LSTMs… We are just lucky!

BOSTON, June 30, 2016 (GLOBE NEWSWIRE) -- indico, an innovator in the machine learning and artificial intelligence space, will make a presentation on deep learning at the Sentiment Analysis Symposium, which takes place in New York, July 12th. Dr. Daniel Kuster, a researcher at indico, will focus on the differences between deep learning and traditional machine learning approaches, and how the advantages of deep learning can be exploited to quickly gain new insights about what people say online, and how they say it. The presentation will take place at Fordham University's Lincoln Center Campus in New York City. Machine learning is becoming the tool of choice for analyzing text and image data. While traditional text processing solutions rely on the ability of experts to encode domain knowledge, machine learning models learn directly from the data.

Whether exchanging dialogue with our smartphones or scribbling characters on touchscreens, the Human-Machine Interfaces (HMI) we interact with today are intuitive and foster'easy to use' input methods. Driven by speech, handwriting and touch, our technologies are continually progressing towards intuitive communication between humans and machines, and we are continuing to march forward. However, several advancements in artificial intelligence technology, such as machine and deep learning capabilities, have paved the way for the humanistion of our machines and devices. And there's one particular development in the AI space which has pioneered the ability for seamless human-to-machine interaction - cognitive ergonomics. Through cognitive ergonomics, system designs that allows machines to adapt and operate considering mental workloads and other factors, we are able to communicate with our devices as easy as writing a note on paper.

More than 30 private companies working to advance artificial intelligence technologies have been acquired in the last 5 years by corporate giants competing in the space, including Google, IBM, Yahoo, Intel, and, more recently, Apple and Salesforce. There have been 5 major acquisitions already in 2016. Google has been the most prominent global player, with 9 acquisitions in the category under its belt (follow all of Google's M&A activity here through our real-time Google acquisitions tracker). In 2013, the corporate giant picked up deep learning and neural network startup DNNresearch from the computer science department at the University of Toronto. This acquisition reportedly helped Google make major upgrades to its image search feature.

Artificial Intelligence is developing at a rapid pace, with companies examining its potential to propel business growth. According to CNBC, Google has stated that it aims to win the artificial intelligence game, following news that it had purchased UK artificial intelligence firm Deepmind for more than US 537 million at the beginning of 2014. Artificial intelligence is still developing in the container terminal industry, however, intelligent autonomous machines are used at some fully automated terminals globally. These machines will be controlled by a central terminal operating system (TOS), which will coordinate the movement of automated cranes and other such equipment. Simulation and emulation company TBA are currently developing simulated virtual training techniques to allow terminal operator staff to train in a virtual environment.

Global warming and pollution are causing severe stress to coral reefs across the world. Researchers from the University of California Berkeley and University of Queensland developed a deep learning process that automatically analyzes reef photos that will help measure reef health and changes over time. Reefs provide food and shelter for more than a quarter of all marine species, and support fish stocks that feed more than a billion people and provide jobs to millions of people in coastal areas. The new technology "will allow the world's scientists to more quickly assess the health of coral reefs at scales never dreamed of before," said Ove Hoegh-Guldberg, chief scientist of the global reef record and a professor at the University of Queensland. With that information, they can more effectively take steps to protect and save them.

Recurrent neural networks, and in particular long short-term memory networks (LSTMs), are a remarkably effective tool for sequence processing that learn a dense black-box hidden representation of their sequential input. Researchers interested in better understanding these models have studied the changes in hidden state representations over time and noticed some interpretable patterns but also significant noise. We present LSTMVis a visual analysis tool for recurrent neural networks with a focus on understanding these hidden state dynamics. The tool allows a user to select a hypothesis input range to focus on local state changes, to match these states changes to similar patterns in a large data set, and to align these results with structural annotations from their domain. We provide data for the tool to analyze specific hidden state properties on dataset containing nesting, phrase structure, and chord progressions, and demonstrate how the tool can be used to isolate patterns for further statistical analysis.

The Deep Learning in Finance Summit is a multidisciplinary event bringing together data scientists, engineers, CTOs, CEOs & leading financial corporations to explore the impact of deep learning in the financial sector. Applications include identifying and preventing risks, revolutionising financial forecasting & compliance.

Google's DeepMind artificial intelligence (AI) technology has proven to be very smart. DeepMind's AlphaGo system got worldwide attention for beating top-ranked Go player Lee Sedol earlier this year. Previously it has played Breakout and navigated a Doom-like maze. But now the DeepMind software is looking more versatile. Today the Google DeepMind lab unveiled another feat that looks off the wall but is actually evidence of the strength of Google's AI.