BigData News Monday, April 2 Data, Secure computation, Learn data analytics & more…
BigData News TLDR / Table of Contents
- Blockchain-based Machine Learning Marketplaces – Fred Ehrsam –
- Machine learning models trained on data from blockchain-based marketplaces have the potential to create the world’s most powerful artificial intelligences. They combine two potent primitives: private…
- data, secure computation, secure computation methods, data providers, best data
- Weekly Digest, April 2
- Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture o…
- Learn data analytics, online Business Analytics, Wharton Customer Analytics, Learn data analysis, high level rigor
- 7 Steps to Mastering Deep Learning with Keras
- neural network, Keras, neural networks, ,
- Blockchain-based Machine Learning MarketplacesMachine learning models trained on data from blockchain-based marketplaces have the potential to create the worlds most powerful artificial intelligences.
- They combine two potent primitives: private machine learning, which allows for training to be done on sensitive private data without revealing it, and blockchain-based incentives, which allow these systems to attract the best data and models to make them smarter.
- Transparency in rewards Data and model providers can see they are getting the fair value of what theyve submitted since all computation is verifiable, making them far more likely to participate.
- Similar to the prior cryptocurrency trading system example, it would work by allowing a marketplace of models focused on different areas (ex: web site recommendations, music) to compete for access to your encrypted data and recommend things to you, and perhaps even pay you for contributing your data or your…
- A simple construction from Algorithmia Research places a bounty on a model that is accurate above a certain backtesting threshold: – Simple construction creating a bounty on a machine learning model by Algorithmia ResearchNumerai currently takes things three steps further: it uses encrypted data (although not fully homomorphically), it combines…
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- Keras is a high-level neural network API, helping lead the way to the commoditization of deep learning and artificial intelligence.
- Keras code is portable, meaning that you can implement a neural network in Keras using Theano as a backened and then specify the backend to subsequently run on TensorFlow, and no further changes would be required to your code.
- However, I will make the assertion that, perhaps more than any other established, mainstream neural network library, Keras is ideally suited to the practice of data science.
- To implement a convolutional neural network (CNN) in Keras, start by reading the documentation on its convolutional layers: – – After this, look at both of the following tutorials on CNNs in Keras.
- This isn’t necessarily an either/or approach; you may find valuable nuggets in both write-ups: – – To learn more about convolution neural networks in general, try Brandon Rohrer’s video: – – – – To implement a recurrent neural network (RNN) in Keras, start by reading the documentation on its recurrent…
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