Machine Learning in Bookmaking – FansUnite – Medium

Let's talk about machine learning in bookmaking for a minute. #fansunitetoken

  • Smart bettors quickly take advantage and the bookmaker shifts the line to equivalate betting volume on either side of a matchup.Similarly, high variance in opinion when the data between two teams is very similar can often lead to poor lines.
  • By polling the crowd with low limits to start, Pinnacle can often limit exposure on early lines and avoid getting picked off on markets by sharp bettors.This novel method of polling the crowd drives lines globally, and it’s no surprise that the default action for almost every sportsbook is to…
  • To produce lines we will use an ensemble of best in class Deep Learning networks, alongside other more common approaches to shape a line up to 24 hours before current markets take shape.At, the world’s preeminent social token betting platform, we have been actively shaping our risk management strategy…
  • We offer an industry leading 1% margin and will maintain a winners welcome philosophy.The Value to the Betting CustomerOur automated machine approach to setting lines offers the following core value to our customers.Savings we can pass on to our bettors.
  • By using Machine Learning, we can offer real time In-Play betting markets to our amazing customers.Stable Currency: Solid lines offer big rewards to currency and token holders by ensuring that the coin base is not drained by sophisticated traders and demand remains strong for our low margin lines.

Machine Learning is becoming a standard tool of the sports betting industry. At we are keenly aware of this technology and actively incorporating it into our risk management strategy…
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A.I. Researchers Leave Elon Musk Lab to Begin Robotics Start-Up

  • Mr. Abbeel and the other founders of Embodied Intelligence, including the former OpenAI researchers Peter Chen and Rocky Duan and the former Microsoft researcher Tianhao Zhang, specialize in an algorithmic method called reinforcement learning — a way for machines to learn tasks by extreme trial and error.
  • Researchers at DeepMind, the London-based A.I. lab owned by Google, used this method to build a machine that could play the ancient game of Go better than any human.
  • Much like Google and labs at Brown and Northeastern University, Embodied Intelligence is also augmenting these methods with a wide range of other machine learning techniques.
  • Some researchers question how much these machine learning techniques will ultimately improve robotics, believing they are overhyped among both researchers and the news media.
  • But Mr. Abbeel is among the world’s top researchers in his field, and his decision to start a own company is an indication that machine learning will continue to push robotics forward.

Pieter Abbeel, a Berkeley professor, is part of the team that has started Embodied Intelligence to make it possible for robots to learn on their own.
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Book: Machine Learning Algorithms From Scratch

Book: Machine Learning Algorithms From Scratch

  • From First Principles With Pure Python and – – Use them on Real-World Datasets – – You must understand algorithms to get good at machine learning.
  • In this mega Ebook written in the friendly Machine Learning Mastery style that you’re used to, finally cut through the math and learn exactly how machine learning algorithms work.
  • I’ve written books on algorithms, won and ranked in the top 10% in machine learning competitions, consulted for startups and spent a long time working on systems for forecasting tropical cyclones.
  • (yes I have written tons of code that runs operationally) – – I get a lot of satisfaction helping developers get started and get really good at machine learning.
  • I teach an unconventional top-down and results-first approach to machine learning where we start by working through tutorials and problems, then later wade into theory as we need it.

Discover How to Code Machine Algorithms
From First Principles With Pure Python and
Use them on Real-World Datasets

$37 USD
You must understand algorithms t…
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SPSS Modeler available for free for educational usage

SPSS Modeler is now free for educational usage:  #opensource #machinelearning #datascience

  • A powerful visual tool like IBM SPSS Modeler is actually a great way to learn about data science and machine learning.
  • IBM is supporting this goal by providing IBM SPSS Modeler for free for educational usage.
  • This distribution is part of the IBM Academic Initiative program which provides many types of IBM software and cloud services for free or reduced cost.
  • If you are a student or professor, please go to and search for IBM SPSS Modeler.
  • If you are not a student or professor but know of some students/professors looking for free data science software, let them know about IBM SPSS Modeler.


Search SPSS Predictive Analytics

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Get started with machine learning using Python

Get started with machine learning using #Python:

  • Also, getting started with Python and machine learning is easy as there are plenty of online resources and lots of Python machine learning libraries available.
  • With basic Python programming skills under your belt, you’re ready to pick up basic machine learning skills.
  • Other online training worth checking out include:

    After getting a good feel for Python and machine learning, consider learning the open source Python libraries.

  • A few Python libraries to check out include:

    With an understanding of basic Python, machine learning skills, and Python libraries, you are all set.

  • Machine learning with Python is a great addition to your technical skillset, and there are lots of free and low-cost online resources available to help.

Machine learning is an in-demand skill to add to your resume. We walk through steps for wading into machine learning with the help of Python.
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Artificial Intelligence must be for all

#AI must be for all, including developing countries. Our CEO @ollybuston's latest blog:

  • Around the world individual researchers and small teams are working on AI solutions to deliver services where currently they are absent or inadequate.
  • We’ve all read the stories about machines taking over our jobs, and the World Bank says that jobs in developing countries are even more at risk of automation.
  • As algorithms become cheaper than people, manufacturing and low-skilled service jobs that are carried out by workers in low and middle income countries may be ‘reshored’ and replaced by work done by machines in rich countries.
  • The answer to the question “Will AI be more of an opportunity or more of a risk for developing countries?”
  • It is time to start laying the foundations for a truly global approach to AI development that maximises the opportunities and minimises the risks for everyone.

The list of tech failures in development is long.  Whether it be drone
pilots getting in the way of emergency relief in Nepal, or computers
gathering dust in Indian schools because teachers don’t know how to use
them, the outcome is rarely positive when techies fall in love with their
preferred solutions rather than taking  time to understand real problems
faced by real people.
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The Present and Future of Quantum Computing for AI – Towards Data Science – Medium

The Present and Future of #QuantumComputing for #AI

  • For AI researchers optimization and sampling is particularly important, because it allows to train Machine Learning models much faster with higher accuracy.At the present time, Canadian D-Wave is the leading company in quantum computing.
  • Task description is encoded as the energy function in connections between qubits, and through annealing they are moving towards some optimal configuration.If the transition is carried out slowly enough the algorithm will find a ground state (i.e., an optimal solution) with high probability:During the annealing process, probability of qubits ending up in the minimum energy state increasesQuantum Coupling allows qubits to explore all potential solutions simultaneously, and at the same time Quantum Tunneling allows them to move through high energy barriers towards the “better” states.
  • This video by D-Wave explains QA in more details:IBM QAnother major player is IBM Q. Big Blue is working with Gate-model quantum computing and their machines are Universal Quantum Computers.
  • State-of-the-art processors from IBM have 16 and 17 qubits, and it’s really hard to scale further.More general architecture of IBM’s processors allows them to run any quantum algorithms.
  • Only theoretical research and simulations on toy problems.Overall, quantum computing looks like a promising direction for stochastic models in Machine Learning.

Quantum computing is still in it’s infancy, and no universal architecture for quantum computers exists right now. However, their prototypes are already here and showing promising results in…
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