Deep Learning for Data Scientists - Data Science ATL Meetup Presentation, 201...Andrew Gardner
Note: these are the slides from a presentation at Lexis Nexis in Alpharetta, GA, on 2014-01-08 as part of the DataScienceATL Meetup. A video of this talk from Dec 2013 is available on vimeo at http://bit.ly/1aJ6xlt
Note: Slideshare mis-converted the images in slides 16-17. Expect a fix in the next couple of days.
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Deep learning is a hot area of machine learning named one of the "Breakthrough Technologies of 2013" by MIT Technology Review. The basic ideas extend neural network research from past decades and incorporate new discoveries in statistical machine learning and neuroscience. The results are new learning architectures and algorithms that promise disruptive advances in automatic feature engineering, pattern discovery, data modeling and artificial intelligence. Empirical results from real world applications and benchmarking routinely demonstrate state-of-the-art performance across diverse problems including: speech recognition, object detection, image understanding and machine translation. The technology is employed commercially today, notably in many popular Google products such as Street View, Google+ Image Search and Android Voice Recognition.
In this talk, we will present an overview of deep learning for data scientists: what it is, how it works, what it can do, and why it is important. We will review several real world applications and discuss some of the key hurdles to mainstream adoption. We will conclude by discussing our experiences implementing and running deep learning experiments on our own hardware data science appliance.
Deep Learning for Data Scientists - Data Science ATL Meetup Presentation, 201...Andrew Gardner
Note: these are the slides from a presentation at Lexis Nexis in Alpharetta, GA, on 2014-01-08 as part of the DataScienceATL Meetup. A video of this talk from Dec 2013 is available on vimeo at http://bit.ly/1aJ6xlt
Note: Slideshare mis-converted the images in slides 16-17. Expect a fix in the next couple of days.
---
Deep learning is a hot area of machine learning named one of the "Breakthrough Technologies of 2013" by MIT Technology Review. The basic ideas extend neural network research from past decades and incorporate new discoveries in statistical machine learning and neuroscience. The results are new learning architectures and algorithms that promise disruptive advances in automatic feature engineering, pattern discovery, data modeling and artificial intelligence. Empirical results from real world applications and benchmarking routinely demonstrate state-of-the-art performance across diverse problems including: speech recognition, object detection, image understanding and machine translation. The technology is employed commercially today, notably in many popular Google products such as Street View, Google+ Image Search and Android Voice Recognition.
In this talk, we will present an overview of deep learning for data scientists: what it is, how it works, what it can do, and why it is important. We will review several real world applications and discuss some of the key hurdles to mainstream adoption. We will conclude by discussing our experiences implementing and running deep learning experiments on our own hardware data science appliance.
Jeff Dean at AI Frontiers: Trends and Developments in Deep Learning ResearchAI Frontiers
In this talk at AI Frontiers conference, Jeff Dean discusses recent trends and developments in deep learning research. Jeff touches on the significant progress that this research has produced in a number of areas, including computer vision, language understanding, translation, healthcare, and robotics. These advances are driven by both new algorithmic approaches to some of these problems, and by the ability to scale computation for training ever large models on larger datasets. Finally, one of the reasons for the rapid spread of the ideas and techniques of deep learning has been the availability of open source libraries such as TensorFlow. He gives an overview of why these software libraries have an important role in making the benefits of machine learning available throughout the world.
Transform your Business with AI, Deep Learning and Machine LearningSri Ambati
Video: https://www.youtube.com/watch?v=R3IXd1iwqjc
Meetup: http://www.meetup.com/SF-Bay-ACM/events/231709894/
In this talk, Arno Candel presents a brief history of AI and how Deep Learning and Machine Learning techniques are transforming our everyday lives. Arno will introduce H2O, a scalable open-source machine learning platform, and show live demos on how to train sophisticated machine learning models on large distributed datasets. He will show how data scientists and application developers can use the Flow GUI, R, Python, Java, Scala, JavaScript and JSON to build smarter applications, and how to take them to production. He will present customer use cases from verticals including insurance, fraud, churn, fintech, and marketing.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
JavaOne 2013: Effective Foreign Function Interfaces: From JNI to JNRRyan Sciampacone
Effective Foreign Function Interfaces: From JNI to JNR
JavaOne 2013 CON4767
Ryan A. Sciampacone, Senior Software Developer, IBM JTC
What do you do when your application needs access to platform features that aren’t available in the Java platform? You need a foreign function interface (FFI). The Java Native Interface (JNI) is the classic power tool for calling native code from your Java program. Using JNI means stepping out of the managed safety of the JVM into the wilds of native code. This session explains the most common JNI performance and correctness pitfalls and explains how to find and avoid them. As the JVM becomes the runtime of choice for more languages, the FFI landscape is also evolving. This session introduces alternative FFI approaches that minimizes effort (SWIG) and native code. It examines JNR in detail and shows how alternatives perform relative to handwritten JNI.
Lukasz Kaiser at AI Frontiers: How Deep Learning Quietly Revolutionized NLPAI Frontiers
While deep learning is very popular, it might not be well know how profoundly it has changed natural language processing (NLP). Lukasz gives an overview of the challenges unique to NLP that made it hard for neural networks, say how they were overcome, and how the new end-to-end deep learning methods managed to significantly improve over state-of-the-art in multiple NLP tasks, such as machine translation, parsing, and summarization.
Nikko Ström at AI Frontiers: Deep Learning in AlexaAI Frontiers
Alexa is the service that understands spoken language in Amazon Echo and other voice enabled devices. Alexa relies heavily on machine learning and deep neural networks for speech recognition, text-to-speech, language understanding, skill selection, and more. In this talk Nikko presents an overview of deep learning in Alexa and gives a few illustrating examples.
