Posts by Category: Artificial Intelligence Resources

Updated> Artificial Intelligence Resources 2017 White Paper Link Dataset Compilation

August 02, 2017

Updated> Artificial Intelligence Resources 2017 White Paper Link Dataset Compilation
http://www.AIResources.info/

The white paper link dataset compilation of the Artificial Intelligence Resources 2017 Subject Tracer™ Information Blog by Marcus P. Zillman, M.S., A.M.H.A. is a freely available 28 page .pdf document (256KB) listing the latest and greatest online resources and sites for artificial intelligence! Completely updated including all links validated and new URLs added on August 2, 2017. Other white papers are available by clicking here.

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The 10 Algorithms Machine Learning Engineers Need to Know by James Le

July 29, 2017

The 10 Algorithms Machine Learning Engineers Need to Know by James Le
http://www.kdnuggets.com/2016/08/10-algorithms-machine-learning-engineers.html

Machine learning algorithms can be divided into 3 broad categories — supervised learning, unsupervised learning, and reinforcement learning.Supervised learning is useful in cases where a property (label) is available for a certain dataset (training set), but is missing and needs to be predicted for other instances. Unsupervised learning is useful in cases where the challenge is to discover implicit relationships in a given unlabeled dataset (items are not pre-assigned). Reinforcement learning falls between these 2 extremes — there is some form of feedback available for each predictive step or action, but no precise label or error message. Since this is an intro class, they didn’t learn about reinforcement learning, but they hope that 10 algorithms on supervised and unsupervised learning will be enough to keep you interested. This will be added to Artificial Intelligence Resources Subject Tracer™.

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Mallet – MAchine Learning for LanguagE Toolkit

July 29, 2017

Mallet – MAchine Learning for LanguagE Toolkit
http://mallet.cs.umass.edu/

MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text. MALLET includes sophisticated tools for document classification: efficient routines for converting text to “features”, a wide variety of algorithms (including Naïve Bayes, Maximum Entropy, and Decision Trees), and code for evaluating classifier performance using several commonly used metrics. In addition to classification, MALLET includes tools for sequence tagging for applications such as named-entity extraction from text. Algorithms include Hidden Markov Models, Maximum Entropy Markov Models, and Conditional Random Fields. These methods are implemented in an extensible system for finite state transducers. This will be added to Data Mining Resources Subject Tracer™. This will be added to Artificial Intelligence Resources Subject Tracer™.

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Deep Learning for Java – Open Source, Distributed, Deep Learning Library for the JVM

July 28, 2017

Deep Learning for Java – Open Source, Distributed, Deep Learning Library for the JVM
https://deeplearning4j.org/

Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments on distributed GPUs and CPUs. Deeplearning4j aims to be cutting-edge plug and play, more convention than configuration, which allows for fast prototyping for non-researchers. DL4J is customizable at scale. Released under the Apache 2.0 license, all derivatives of DL4J belong to their authors. DL4J can import neural net models from most major frameworks via Keras, including TensorFlow, Caffe, Torch and Theano, bridging the gap between the Python ecosystem and the JVM with a cross-team toolkit for data scientists, data engineers and DevOps. This will be added to Data Mining Resources Subject Tracer™. This will be added to Artificial Intelligence Resources Subject Tracer™.

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MOA (Massive Online Analysis)

July 28, 2017

MOA (Massive Online Analysis)
https://moa.cms.waikato.ac.nz/

MOA is the most popular open source framework for data stream mining, with a very active growing community (blog). It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation. Related to the WEKA project, MOA is also written in Java, while scaling to more demanding problems. This will be added to Data Mining Resources Subject Tracer™. This will be added to Artificial Intelligence Resources Subject Tracer™.

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Weka 3: Data Mining Software in Java

July 28, 2017

Weka 3: Data Mining Software in Java
http://www.cs.waikato.ac.nz/ml/weka/index.html

Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. Found only on the islands of New Zealand, the Weka is a flightless bird with an inquisitive nature. The name is pronounced like this, and the bird sounds like this. Weka is open source software issued under the GNU General Public License. They have put together several free online courses that teach machine learning and data mining using Weka. Check out the website for the courses for details on when and how to enroll. The videos for the courses are available on Youtube. Yes, it is possible to apply Weka to big data! This will be added to Data Mining Resources Subject Tracer™. This will be added to Artificial Intelligence Resources Subject Tracer™.

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Microsoft Cognitive Toolkit

July 27, 2017

Microsoft Cognitive Toolkit
https://www.microsoft.com/en-us/cognitive-toolkit/

A free, easy-to-use, open-source, commercial-grade toolkit that trains deep learning algorithms to learn like the human brain. Unlock deeper learning with the new Microsoft Cognitive Toolkit. The Microsoft Cognitive Toolkit—previously known as CNTK—empowers you to harness the intelligence within massive datasets through deep learning by providing uncompromised scaling, speed, and accuracy with commercial-grade quality and compatibility with the programming languages and algorithms you already use. Features include: a) Speed & Scalability – The Microsoft Cognitive Toolkit trains and evaluates deep learning algorithms faster than other available toolkits, scaling efficiently in a range of environments—from a CPU, to GPUs, to multiple machines—while maintaining accuracy; b) Commercial-Grade Quality – The Microsoft Cognitive Toolkit is built with sophisticated algorithms and production readers to work reliably with massive datasets. Skype, Cortana, Bing, Xbox, and industry-leading data scientists already use the Microsoft Cognitive Toolkit to develop commercial-grade AI; and c) Compatibility – The Microsoft Cognitive Toolkit offers the most expressive, easy-to-use architecture available. Working with the languages and networks you know, like C++ and Python, it empowers you to customize any of the built-in training algorithms, or use your own. This will be added to Artificial Intelligence Resources Subject Tracer™.

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Char-RNN

July 27, 2017

Char-RNN
https://github.com/karpathy/char-rnn

This code implements multi-layer Recurrent Neural Network (RNN, LSTM, and GRU) for training/sampling from character-level language models. In other words the model takes one text file as input and trains a Recurrent Neural Network that learns to predict the next character in a sequence. The RNN can then be used to generate text character by character that will look like the original training data. The context of this code base is described in detail in my blog post. If you are new to Torch/Lua/Neural Nets, it might be helpful to know that this code is really just a slightly more fancy version of this 100-line gist that I wrote in Python/numpy. The code in this repo additionally: allows for multiple layers, uses an LSTM instead of a vanilla RNN, has more supporting code for model checkpointing, and is of course much more efficient since it uses mini-batches and can run on a GPU. This will be added to Artificial Intelligence Resources Subject Tracer™.

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Awareness Watch Talk Show for Wednesday July 26, 2017 at 2:00pm EDST – Artificial Intelligence Resources 2017

July 26, 2017

Awareness Watch Talk Show for Wednesday July 26, 2017 at 2:00pm EDST – Artificial Intelligence Resources 2017
http://www.BlogTalkRadio.com/AwarenessWatch/

This program will be featuring my just updated Artificial Intelligence Resources 2017. We will be highlighting the latest and greatest resources and sources for artificial intelligence covering search engines, subject directories, articles, guides and tracers….literally everything on the Internet covering ARTIFICIAL INTELLIGENCE!! We will also discussing my latest freely available Awareness Watch Newsletter V15N7 July 2017 featuring 2017 New Economy as well as my freely available July 2017 Zillman Column highlighting Employment Resources 2017. You may call in to ask your questions at (718)508-9839. The show is live and thirty minutes in length starting at 2:00pm EDST on Wednesday, July 26, 2017 and then archived for easy review and access. Listen, Call and Enjoy!!

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Awareness Watch Newsletter V15N8 August 2017

July 21, 2017

Awareness Watch Newsletter V15N8 August 2017
http://AwarenessWatch.VirtualPrivateLibrary.net/V15N8.pdf
Awareness Watch™ Newsletter Blog and Archives
http://www.AwarenessWatch.com/

The August 2017 V15N8 Awareness Watch Newsletter is a freely available 55 page .pdf document (380KB) from the above URL. This month’s featured report covers the following areas and subjects that are constantly monitored by our Subject Tracer™ Bots from both the world wide web and deep web. The areas and subjects covered in this month’s Awareness Watch Newsletter are as follows: 1) Peer to Peer (P2P), File Sharing, Grid and Matrix Search Engines; 2) Semantic Web Research Resources; 3) Artificial Intelligence Resources 2017; and 4) Bot and Intelligent Agent Research Resources and Sites 2017. These 4 areas are currently the most exciting and happening places on the Internet and need to be placed as a top priority learning event for all readers of this newsletter!! The Awareness Watch Spotters cover many excellent and newly released annotated current awareness research sources and tools as well as the latest identified Internet happenings and resources including a number of neat and must-have tools! The Awareness Watch Article Review covers Fostering Digital and Scientific Literacy: Learning Through Practice by Patricia Dias da Silva, Lorna Heaton.

Subscribe to the monthly free Awareness Watch Newsletter by clicking here.

This research is powered by Subject Tracer Bots™ available from the Virtual Private Library™.

©2017 Marcus P. Zillman, M.S., A.M.H.A.

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