ML / AI

to sort

• http://www.humanbrainproject.eu/
• http://www.nih.gov/science/brain/

• http://wiki.opencog.org/w/The_Open_Cognition_Project

NLP

• https://en.wikipedia.org/wiki/Natural_language_processing

• http://thetokenizer.com/2013/04/28/build-your-own-summary-tool/

• https://textblob.readthedocs.org/en/latest/

• https://github.com/Tabule/Sherlock

• http://eacl2014.org/tutorial-metaphor

• http://languagengine.co/blog/what-happened-to-old-school-nlp/ [1]

• https://news.ycombinator.com/item?id=16713964

Bots

• https://dev.botframework.com/ [2]

• https://github.com/itsabot/abot

• https://news.ycombinator.com/item?id=11503998

• https://home-assistant.io/

• https://pagenodes.com/
  • https://github.com/monteslu/pagenodes

Machine learning

• https://en.wikipedia.org/wiki/Machine_learning

• https://news.ycombinator.com/item?id=12713056

• https://news.ycombinator.com/item?id=12936891

• A List of Data Science and Machine Learning Resources
• https://www.cs.utexas.edu/~klivans/395t.html

• Tom Mitchell: Never Ending Language Learning

• https://en.wikipedia.org/wiki/Artificial_neuron

• https://en.wikipedia.org/wiki/Perceptron

• https://news.ycombinator.com/item?id=12751585

• https://github.com/rasbt/python-machine-learning-book/blob/master/faq/difference-deep-and-normal-learning.md [3]

"In applications of "usual" machine learning, there is typically a strong focus on the feature engineering part; the model learned by an algorithm can only be so good as its input data. Of course, there must be sufficient discriminatory information in our dataset, however, the performance of machine learning algorithms can suffer substantially when the information is buried in meaningless features. The goal behind deep learning is to automatically learn the features from (somewhat) noisy data; it's about algorithms that do the feature engineering for us to provide deep neural network structures with meaningful information so that it can learn more effectively. We can think of deep learning as algorithms for automatic "feature engineering," or we could simply call them "feature detectors," which help us to overcome the vanishing gradient challenge and facilitate the learning in neural networks with many layers."

• http://deeplearning4j.org/

• http://www.damninteresting.com/on-the-origin-of-circuits/ [4]

• https://probmods.org/

• http://karpathy.github.io/2015/05/21/rnn-effectiveness/

• https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding
  • https://news.ycombinator.com/item?id=12713388

• https://news.ycombinator.com/item?id=12942169

• http://www.cs.cmu.edu/~tom7/mario/
  • https://www.youtube.com/watch?v=xOCurBYI_gY

• http://neuralnetworksanddeeplearning.com/ [5]

• https://news.ycombinator.com/item?id=8258652

• http://www.technologyreview.com/view/530561/the-revolutionary-technique-that-quietly-changed-machine-vision-forever/

• https://news.ycombinator.com/item?id=8553307

• http://openssi.net/
  • http://records.sigmm.ndlab.net/2014/10/ssi-an-open-source-platform-for-social-signal-interpretation/ [6]

• YouTube: Artificial Intelligence: Neural Networks in Machine Learning

• https://en.wikipedia.org/wiki/Convolutional_neural_network

• https://news.ycombinator.com/item?id=9109157

• https://news.ycombinator.com/item?id=11530304

• http://quantombone.blogspot.co.uk/2015/04/deep-learning-vs-probabilistic.html [7]

• http://googleresearch.blogspot.be/2015/06/inceptionism-going-deeper-into-neural.html
• http://www.theguardian.com/technology/2015/jun/18/google-image-recognition-neural-network-androids-dream-electric-sheep
• https://317070.github.io/LSD/
• https://github.com/google/deepdream [8]
• http://ryankennedy.io/running-the-deep-dream/
• https://github.com/graphific/DeepDreamVideo [9]

• https://www.gnu.org/software/gneuralnetwork/ [10]

• https://open_nsfw.gitlab.io/ [11]

• Caffe - a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license.
  • Caffe Model Zoo

• Torch - a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.
  • https://github.com/torch/torch7

• https://github.com/karpathy/char-rnn - Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN) for character-level language models in Torch

• YouTube: Connections between physics and deep learning [https://news.ycombinator.com/item?id=13062139

• DeepRhyme (D-Prime) – generating dope rhymes with deep learning [12]

• Composing Music With Recurrent Neural Networks [13]

• Data Science Machine - an end-to-end software system that is able to automatically develop predictive models from relational data. The Machine was created by Max Kanter and Kalyan Verramachaneni at the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. The system automates two of the most human-intensive components of a data science endeavor: feature engineering, and selection and tuning of the machine learning methods that build predictive models from those features. First, an algorithm called Deep Feature Synthesis automatically engineers features. Next, through an approach called Deep Mining, the Machine composes a generalized machine learning pipeline that includes dimensionality reduction methods, feature selection methods, clustering, and classifier design. Finally, it tunes the parameters through a Gaussian Copula Process.
  • System that replaces human intuition with algorithms outperforms human teams

• What's Next in Deep Learning [14]

• Deep Residual Learning for Image Recognition https://news.ycombinator.com/item?id=10715628

• http://www.wildml.com/2016/01/attention-and-memory-in-deep-learning-and-nlp/ [15]

• TensorFlow - an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.

• http://tflearn.org
  • https://github.com/tflearn/tflearn [16]

• http://playground.tensorflow.org/ [17]

• https://www.oreilly.com/learning/hello-tensorflow [18]

• https://github.com/sherjilozair/char-rnn-tensorflow

• https://nucl.ai/blog/enhance-pixel-art/

• http://www-personal.umich.edu/~reedscot/iclr_project.html [19]

• https://deepmind.com/blog/wavenet-generative-model-raw-audio/ [20]

• http://www.wildml.com/2016/10/learning-reinforcement-learning [21]

• https://blog.acolyer.org/2016/10/12/towards-deep-symbolic-reinforcement-learning/ [22]

• http://www.wildml.com/2016/10/learning-reinforcement-learning/ [23]

• https://research.googleblog.com/2016/12/open-sourcing-embedding-projector-tool.html [24]

• https://tryolabs.com/blog/2016/12/06/major-advancements-deep-learning-2016/ [25]

• https://nips.cc/Conferences/2016/SpotlightVideos

• https://arxiv.org/abs/1612.03770 [26]

• https://jalammar.github.io/visual-interactive-guide-basics-neural-networks/ [27]

• Operational calculus on programming spaces - https://news.ycombinator.com/item?id=13280818

• https://www.darpa.mil/news-events/2016-06-17 [28]

• http://distill.pub/about/ [29]

• https://vectordash.com

Artificial intelligence

• https://en.wikipedia.org/wiki/Artificial_intelligence

• http://theai.wiki/Home [30]

• https://medium.com/backchannel/how-elon-musk-and-y-combinator-plan-to-stop-computers-from-taking-over-17e0e27dd02a#.m7ju9koio

• https://www.nature.com/news/there-is-a-blind-spot-in-ai-research-1.20805 [31]

• OpenAI is a non-profit artificial intelligence research company. Our goal is to advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return. Since our research is free from financial obligations, we can better focus on a positive human impact. We believe AI should be an extension of individual human wills and, in the spirit of liberty, as broadly and evenly distributed as is possible safely. The outcome of this venture is uncertain and the work is difficult, but we believe the goal and the structure are right. We hope this is what matters most to the best in the field. [32]

• BotLibre.org - an open source platform based on an advanced artificial intelligence engine developed in Java. The Bot Libre AI engine can be used in any Java platform, such as a Java webserver, Java client, or on Android. The Bot Libre SDK supports access to Bot Libre's web API from JavaScript, Android, Java, iOS, and objective C. [33]

• Mycroft Mycroft - the world’s first open source assistant. Mycroft runs anywhere – on a desktop computer, inside an automobile, or on a Raspberry Pi. This is open source software which can be freely remixed, extended, and improved. Mycroft may be used in anything from a science project to an enterprise software application.
  • https://github.com/MycroftAI/mycroft-core
  • https://github.com/MycroftAI/mycroft-skills

Home automation

• https://en.wikipedia.org/wiki/Home_automation

• https://news.ycombinator.com/item?id=8152714

Home Assistant

• https://home-assistant.io
  • https://home-assistant.io/addons/google_assistant/

• http://creutz.spdns.de/lib/exe/fetch.php?media=edv:homeautomation_linux.pdf

Node-RED

• Node-RED - a programming tool for wiring together hardware devices, APIs and online services in new and interesting ways. It provides a browser-based editor that makes it easy to wire together flows using the wide range of nodes in the palette that can be deployed to its runtime in a single-click.

• YouTube: Intro to Node-RED: Part 1 Fundamentals

openHAB

• openHAB - a vendor and technology agnostic open source automation software for your home
  • https://github.com/openhab