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 Fansunite.io, 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 fansunite.io we are keenly aware of this technology and actively incorporating it into our risk management strategy…
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Introducing Gluon — An Easy-to-Use Programming Interface for Flexible Deep Learning

Deep learning just got simpler & faster with the new Gluon API.

  • The first result of this collaboration is the new Gluon interface, an open source library in Apache MXNet that allows developers of all skill levels to prototype, build, and train deep learning models.
  • It brings together the training algorithm and neural network model, thus providing flexibility in the development process without sacrificing performance.
  • Then, when speed becomes more important than flexibility (e.g., when you’re ready to feed in all of your training data), the Gluon interface enables you to easily cache the neural network model to achieve high performance and a reduced memory footprint.
  • For each iteration, there are four steps: (1) pass in a batch of data; (2) calculate the difference between the output generated by the neural network model and the actual truth (i.e., the loss); (3) use to calculate the derivatives of the model’s parameters with respect to their impact on…
  • To learn more about the Gluon interface and deep learning, you can reference this comprehensive set of tutorials, which covers everything from an introduction to deep learning to how to implement cutting-edge neural network models.

Today, AWS and Microsoft announced a new specification that focuses on improving the speed, flexibility, and accessibility of machine learning technology for all developers, regardless of their deep learning framework of choice. The first result of this collaboration is the new Gluon interface, an open source library in Apache MXNet that allows developers of all skill levels to prototype, build, and train deep learning models. This interface greatly simplifies the process of creating deep learning models without sacrificing training speed.
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Free Learning

Free #Java #DeepLearning eBook 

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  • Time is running out to claim this free ebook – – Dive into the future of data science and learn how to build the sophisticated algorithms that are fundamental to deep learning and AI with Java.
  • Starting with an introduction to basic machine learning algorithms, to give you a solid foundation, Deep Learning with Java takes you further into this vital world of stunning predictive insights and remarkable machine intelligence.
  • By the end of the book, you’ll be ready to tackle Deep Learning with Java.
  • Wherever you’ve come from – whether you’re a data scientist or Java developer – you will become a part of the Deep Learning revolution!

A new free programming tutorial book every day! Develop new tech skills and knowledge with Packt Publishing’s daily free learning giveaway.
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Generating Photorealistic Images of Fake Celebrities with Artificial Intelligence – NVIDIA Developer News Center

Researchers from @NVIDIA used #GANs to generate photorealistic images of fake celebrities.

  • Researchers from NVIDIA recently published a paper detailing their new methodology for generative adversarial networks (GANs) that generated photorealistic pictures of fake celebrities.
  • Rather than train a single neural network to recognize pictures, researchers train two competing networks.
  • “The key idea is to grow both the generator and discriminator progressively:  starting from a low resolution, we add new layers that model increasingly fine details as training progresses,” explained the researchers in their paper Progressive Growing of GANs for Improved Quality, Stability and Variation.
  • Since the publicly available CelebFaces Attributes (CelebA) training dataset varied in resolution and visual quality — and not sufficient enough for high output resolution — the researchers generated a higher-quality version of the dataset consisting of 30,000 images at 1024 x 1024 resolution.
  • Generating convincing realistic images with GANs are within reach and the researchers plan to use TensorFlow and multi-GPUs for the next part of the work.

Researchers from NVIDIA recently published a paper detailing their new methodology for generative adversarial networks (GANs) that generated photorealistic pictures of fake celebrities.
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Vertex.AI

#PlaidML now has preliminary support for for Mac and Python 3:

#Keras #OpenCL #DeepLearning

  • Last week we announced the release of PlaidML, an open source software framework designed to enable deep learning on every device.
  • We received immediate requests for Mac and Python 3, today we’re pleased to announce preliminary support for both.
  • Installing PlaidML with Keras on a Mac is as simple as , but we’ve added something extra: – – We’ve updated plaidvision with support for macOS and Mac built-in webcams.
  • The actual installation only takes a moment: – – PlaidML on Mac is a preview and we are very interested in hearing about user experiences.
  • We’d especially like to thank GitHub user Juanlu001, our first open source contributor, for taking the lead on Python 3 support.

Last week we announced the release of PlaidML, an open source software framework designed to enable deep learning on every device. Our goal with PlaidML is to make deep learning accessible by supporting the most popular hardware and software already in the hands of developers, researchers, and students. Last week’s release supported Python 2.7 on Linux. We received immediate requests for Mac and Python 3, today we’re pleased to announce preliminary support for both.
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Vertex.AI

Vertex.AI - Announcing PlaidML: Open Source #DeepLearning for Every Platform

  • Our company uses PlaidML at the core of our deep learning vision systems for embedded devices, and to date we’ve focused on support for image processing neural networks like ResNet-50, Xception, and MobileNet.
  • We wrote about this in a previous post comparing PlaidML inference throughput to TensorFlow on cuDNN.
  • After updating to Keras 2.0.8, cuDNN 6, and Tensorflow 1.3, it’s within about 4% of PlaidML’s throughput: – – It’s a great improvement and we continue to use TensorFlow as our benchmark for other areas where PlaidML is less mature.
  • Briefly, the system requirements are: – – To get PlaidML installed and do a quick benchmark all you need to do is: – – By default, plaidbench will benchmark 1024 inferences at batch size 1 using Keras on PlaidML and print a result similar to the following: – – In…
  • Then run plaidbench with the “no-plaid” option: – – The output should look like the following: – – PlaidML can take longer to execute on the first run, but tends to outperform TensorFlow + cuDNN, even on the latest NVIDIA hardware (in this case by about 14%).

We’re pleased to announce the next step towards deep learning for every device and platform. Today Vertex.AI is releasing PlaidML, our open source portable deep learning engine. Our mission is make deep learning accessible to every person on every device, and we’re building PlaidML to help make that a reality. We’re starting by supporting the most popular hardware and software already in the hands of developers, researchers, and students. The initial version of PlaidML runs on most existing PC hardware with OpenCL-capable GPUs from NVIDIA, AMD, or Intel. Additionally, we’re including support for running the widely popular Keras framework on top of Plaid to allow existing code and tutorials to run unchanged.
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TensorFlow or Keras? Which one should I learn? – Imploding Gradients – Medium

#TensorFlow or #Keras? Which one should I learn?

  • With plenty of libraries out there for deep learning, one thing that confuses a beginner in this field the most is which library to choose.Deep Learning libraries/frameworks as per popularity(Source : Google)In this blog post, I am only going to focus on Tensorflow and Keras.
  • And if Keras is more user-friendly, why should I ever use TF for building deep learning models?
  • You can tweak TF much more as compared to Keras.FunctionalityAlthough Keras provides all the general purpose functionalities for building Deep learning models, it doesn’t provide as much as TF.
  • Absolutely, check the example below:Playing with gradients in TensorFlow (Credits : CS 20SI: TensorFlow for Deep Learning Research)Conclusion (TL;DR)if you are not doing some research purpose work or developing some special kind of neural network, then go for Keras (trust me, I am a Keras fan!!)
  • But as we all know that Keras is going to be integrated in TF, it is wiser to build your network using tf.contrib.Keras and insert anything you want in the network using pure TensorFlow.

Deep learning is everywhere. 2016 was the year where we saw some huge advancements in the field of Deep Learning and 2017 is all set to see many more advanced use cases. With plenty of libraries out…
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Five Hot AI Startups Step into Spotlight at GTC Europe Inception Awards

Five hot #AI startups step into the spotlight at the #GTC17EU Inception Awards:

  • Then we gave one of them — Gamaya, a 20-person startup harnessing deep learning to help farms improve their productivity and sustainability — a new DGX Station in front of a room packed with more than 160 investors, entrepreneurs and industry observers.
  • The event’s contenders were selected from among the 700 European startups participating in our Inception program, which accelerates the development of startups involved in AI and deep learning.
  • After looking at an initial round of 25 startups, our judges chose companies we believe to be the five hottest in Europe to tell their stories.
  • Besides our winner Gamaya, the startups included presentations from: – – The Inception Awards continue the series of events we’ve held in Silicon Valley and China in conjunction with our GPU Technology Conference world tour.
  • Our Inception virtual accelerator program supports more than 1,900 AI startups with GPUs, deep learning expertise and other resources to help them be successful.

We brought five of the hottest startups in Europe and put them in front of a panel of some of tech’s savviest players at GTC Europe in Munich Tuesday.
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Setting up your Visual Studio Code Tools for AI – Towards Data Science – Medium

Setting up your #VisualStudio Code Tools for #AI

#MachineLearning

  • It seamlessly integrates with Azure Machine Learning for robust experimentation capabilities, including but not limited to submitting data preparation and model training jobs transparently to different compute targets.This is actually a useful tool for developers who needs to work on a AI Solution, while still using the code editor of…
  • “Number of seats” is basically the total number of Azure users you can add to your Experimentation account.A subscription can have only one plan with a “DevTest” pricing tier.Currently supported location is Australia East, East US 2, and West Central US.Install Azure Machine Learning WorkbenchOnce we have our Azure Machine…
  • It allows you to manage machine learning solutions through the entire data science life cycle.Currently the Azure Machine Learning Workbench desktop app can be installed on the following operating systems only:Windows 10Windows Server 2016macOS Sierra (macOS High Sierra is not supported yet)Note: Azure Machine Learning Workbench will also download and…
  • $ brew install openssl$ mkdir -p /usr/local/lib$ ln -s /usr/local/lib/$ ln -s /usr/local/lib/Install and explore project samples in Visual Studio Code Tools for AINow that we have our Azure Machine Learning accounts and Azure Machine Learning Workbench setup, we’re now ready to use Visual Studio Code Tools for AIDownload the Visual…
  • To fix this, just restart the VS Code and you should be able to see the command again.Create a new project in Azure Machine Learning Sample ExplorerWe’ll now then try to create a simple project using sample explorer and test it in our local machine.Click “install” to the Simple Linear Regression…

Microsoft just launched a new set of tools related to Artificial Intelligence last September at Microsoft Ignite 2017, and one of those tools is Visual Studio Code Tools for AI. This is actually a…
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Deep Learning for Object Detection: A Comprehensive Review

#DeepLearning for Object Detection: A Comprehensive Review  #NeuralNetworks

  • By the end of this post, we will hopefully have gained an understanding of how deep learning is applied to object detection, and how these object detection models both inspire and diverge from one another.
  • This time around, I want to do the same for Tensorflow’s object detection models: Faster R-CNN, R-FCN, and SSD.
  • Faster R-CNN is now a canonical model for deep learning-based object detection.
  • Fast R-CNN resembled the original in many ways, but improved on its detection speed through two main augmentations: – – The new model looked something like this: – – As we can see from the image, we are now generating region proposals based on the last feature map of the…
  • In other words, Faster R-CNN may not be the simplest or fastest method for object detection, but it is still one of the best performing.


By the end of this post, we will hopefully have gained an understanding of how deep learning is applied to object detection, and how these object detection models both inspire and diverge from one another.

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