Decebal on programming

Day 8 of Machine Learning in Review

• • ☕️ 4 min read

Resources explored

  • Smaller Collaborative Robots Are Disrupting The Robotics Industry

    I love the reports from www.cbinsights.com, their research does more than scratch the surface and often offers myself a way to link up ideas with the state of the market. As it stands I have no partnership with them, I just consider them an awesome resource.

    Warning, exponentially difficult business idea: Personally after reading the report I started dreaming of a personal cook robot with an AI that can adjust recipes based on my feedback on food and tailor it differently for my wife or my future children.

  • Lenia - Mathematical Life Forms

    I have no clue how I can use this, but I am a sucker for learning Maths concepts through visualizations, this could come in handy later in my research. So far I have only got to look at https://chakazul.github.io/Lenia/JavaScript/Lenia.html and play with the presents without any understanding of what is actually going on :). The fact that I couldn’t find anything by googling the animals name didn’t help a great lot either.

  • jupyter-repo2docker

    I just used this on CoinAi on my way to try and explore crypto currency auto trading and statistics.

    Really useful tool as long as it works on your targeted repository.

  • Two Minute Papers

    I wouldn’t say this is great for learning, but an amazing resource for news in AI. The format of 2–4 minutes per video is enough for delivering the news wanted to make you curious about it, while keeping them close to the subject.

    A few examples:

    *If you know of other similar sources please do share, along with your opinion if you’d like.

  • Data BlockChain

    Their subtitle looks like this: “Merging Big Data, Artificial Intelligence and Blockchain Technology to Bring Critical Information to the World” Which made me curious, but so far as I couldn’t find anything else to explain their use cases I find these as rather marketing stunts in order to attract attention to their IPO.

    However a Data Marketplace alone is an amazing proposition, my worry looking through it is that I don’t understand the source. A data marketplace is any public blockchain, or maybe I need a better understanding to unclad my ignorance.

  • Neural Networks on Scribd

    Forgive my ignorance if you already knew about this, but I find this collection of resources amazing to start with.

    • I have a knack for picking up old books, like Neural Networks and Pattern Recognition, might be old in terms of publishing (1997), but it would make for a good introduction to the methods of Pattern Recognition as theory.

    • Deep Learning Fundamentals in Python is an introduction to the basics of Neural Networks. Looks great for settling some fundamentals if you’re into reading. The introduction alone is enough for me.

    • Artificial Neural Networks Architecture Applications is amazing for the stage I am at as it exposes use cases like “Use of Artificial Neural Networks to Predict TheBusiness Success or Failure of Start-Up Firms” or “Robust Design of Artificial Neural NetworksMethodology in Neutron Spectrometry”. This type of book is something I can easily get behind and search for more similar through scribd’s features.

  • KMeans in less than 5 minutes

    K-means clustering is a classification algorithm used to automatically divide a large group into smaller groups.

  • Best Programming Languages for Machine Learning

    Suraj goes through Python and Javascript as programming languages for Machine Learning. There are plenty more to consider, my favourites are Julia and Go-Lang.

  • Event Registry

    Real time news content published by over 30,000 news publishers worldwide.

    This type of resource is a great start on fake news break down effort as well as a source for media publications.

  • Twitter Sentiment Analysis - Learn Python for Data Science #2

    Just went through this sample from Suraj and took the liberty to explore the tools referred in Sentiment Analysis Tools Overview with his code and must say the packages for R helped me a great deal.