Posts by Category: Artificial Intelligence Resources

Deckard Intelligence – Your AI For Project Management

September 18, 2017

Deckard Intelligence – Your AI For Project Management
https://deckard.ai/

Deckard Intelligence is the AI engine built to automate time estimations, optimize sprint planning and increase team performance. Turn your backlog data into a productive workflow! They are highly qualified experts in the area of machine learning, AI, data science, software engineering and security. Their mission is to shape your idea into product solutions and to optimise your business with core code knowledge and algorithm crafting. Services include: a) Data science and analytics – They extract valuable information from your (un)structured data and create applications to leverage it and tackle your most challenging problems; b) Architecture and Data Infrastructure – Let them help you implement data processing pipelines, streaming analytics, and data warehouses to process, analyze, and protect your data; c) Machine Learning and A.I. – They can help you design and implement custom machine learning solutions to make your products and services smarter and efficient; and d) Medical technology – They work with big corporations and startups to innovate businesses, disrupt markets and design products that will make an impact and change the lives of millions of people. this will be added to Artificial Intelligence Resources Subject Tracer™. This will be added to Entrepreneurial Resources Subject Tracer™. This will be added to the tools section of Research Resources Subject Tracer™.

35 views

Exploratory – The Third Wave of AI

August 28, 2017

Exploratory – The Third Wave of AI
https://exploratory.io/

Data Science is not just for Engineers and Statisticians. It’s for Everyone who is interested in data. With a simple UI experience and the depth of R’s analytics power, you can interact and understand ANY data at the speed of your thought. Exploratory’s Machine Learning framework provides a simple and consistent experience for building, predicting, and evaluating the models from hundreds of the machine learning algorithms available in R. With Exploratory’s open machine learning framework you can bring your own favorite algorithms and run them natively, which means you can build, predict, and evaluate the models with point and clicks, just like any other out-of-the-box machine learning models. This will be added to Artificial Intelligence Resources Subject Tracer™. This will be added to Entrepreneurial Resources Subject Tracer™. This will be added to the tools section of Research Resources Subject Tracer™.

46 views

The Ultimate Artificial Intelligence Resources Guide by Kyle Poyar

August 16, 2017



The Ultimate Artificial Intelligence Resources Guide by Kyle Poyar

http://labs.openviewpartners.com/artificial-intelligence-resources-guide/

The term Artificial Intelligence was originally coined by John McCarthy in 1955 who defined it as “the science and engineering of making intelligent machines.” More than half a century later, AI and machine learning are taking their places among tech’s most talked about trends. Over the past few years, AI has made inroads in data mining, industrial robotics, speech recognition and much more. And its rise is only slated to continue. By 2022, the overall artificial intelligence market is expected to be worth more than $16 billion. That’s an expected compound annual growth rate of over 62% from 2016 to 2022. It’s not wonder then that tech giants and small startups alike are investing heavily in AI and machine learning. Here, they have compiled an in-depth guide to help you brush up on your AI knowledge. This will be added to Artificial Intelligence Resources Subject Tracer™. This will be added to Entrepreneurial Resources Subject Tracer™.

52 views

Kaggle – Home of Data Science and Machine Learning

August 07, 2017

Kaggle – Home of Data Science and Machine Learning
https://www.kaggle.com/

Kaggle helps you learn, work and play. Features include: a) Competitions – Climb the world’s most elite machine learning leader boards, b) Datasets – Explore and analyze a collection of high quality public datasets, and c) Kernels – Run code in the cloud and receive community feedback on your work. This will be added to Artificial Intelligence Resources Subject Tracer™. This will be added to Script Resources Subject Tracer™. This will be added to the tools section of Research Resources Subject Tracer™. This will be added to Statistics Resources and Big Data Subject Tracer™.

64 views

Digital Operating Systems Using Artificial Intelligence, Deep Learning, and Bots for the Small and Medium Size Business – Tools and Resources

August 05, 2017

Digital Operating Systems Using Artificial Intelligence, Deep Learning, and Bots for the Small and Medium Size Business – Tools and Resources
http://www.DigitalOperatingSystems.com/

Digital Operating Systems by Marcus P. Zillman, M.S., A.M.H.A. will be a 500+ page manual with 15 chapters from Artificial Intelligence Resources to Future Predictions and Conclusions. This is designed giving the tools and resources necessary for the small and medium size business who now want to create or improve their digital operating system for the New Economy. This publication is in process and should be available in September 2017. For additional information click here or contact the author.

93 views

Amazon AI – Bringing Powerful Artificial Intelligence To All Developers

August 04, 2017

Amazon AI – Bringing Powerful Artificial Intelligence To All Developers
https://aws.amazon.com/amazon-ai/

Amazon AI services bring natural language understanding (NLU), automatic speech recognition (ASR), visual search and image recognition, text-to-speech (TTS), and machine learning (ML) technologies within the reach of every developer. Based on the same proven, highly scalable products and services built by the thousands of machine learning experts across Amazon, Amazon AI services provide high-quality, high-accuracy AI capabilities that are scalable and cost-effective. In addition, the AWS Deep Learning AMI provides a way for AI developers and researchers to quickly and easily begin using any of the major deep learning frameworks to train sophisticated, custom AI models; experiment with new algorithms; and learn new deep learning skills and techniques on AWS’ massive compute infrastructure. This will be added to Artificial Intelligence Resources Subject Tracer™. This will be added to Script Resources Subject Tracer™. This will be added to Entrepreneurial Resources Subject Tracer™.

130 views

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.

64 views

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™.

83 views

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™.

72 views

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™.

78 views