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Machine Learning and AI
Just for Fun
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Predictive Coding Approximates Backprop along Arbitrary Computation...
The backpropagation of error (backprop) is a powerful algorithm for training machine learning architectures through end-to-end differentiation. Recently it has been shown that backprop in...
Machine Learning and AI shared a link
The Pandemic is Replacing Lawyers with Robots Faster Than Ever - Best of Legal Tech
Covid-19 isn´t just causing trials to be remote, now it is accelerating the replacement of lawyers too.
Machine Learning and AI shared a link
30 Largest TensorFlow Datasets for Machine Learning | Lionbridge AI
To help you find the training data you need, this article will briefly introduce some of the largest TensorFlow datasets for machine learning.
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AI learns to trace neuronal pathways
Cold Spring Harbor Laboratory (CSHL) scientists have taught computers to recognize a neuron in microscope images of the brain more efficiently than any previous approach. The researchers improved the ...
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50 million artificial neurons to facilitate machine-learning research
Fifty million artificial neurons—a number roughly equivalent to the brain of a small mammal—were delivered from Portland, Oregon-based Intel Corp. to Sandia National Laboratories last month, said Sand...
Memristor Breakthrough: First Single Device To Act Like a Neuron
Analog computing with neuron-like devices could efficiently solve problems traditional computers struggle with
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Back to Machine Learning Basics - Decision Tree & Random Forest
In this article, we explore Decision Tree and Random Forest algorithms, implement them from scratch with Python and learn how to use from Sci-Kit Learn.
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Transformers in NLP: Creating a Translator Model from Scratch | Lionbridge AI
In this post, we look at transformer architecture for NLP by creating an English to German translation network almost from scratch.
Machine Learning and AI shared a link
Google AI recently released the new trimmed-down pQRNN, an extension to the projection attention neural network PRADO that Google AI created last year and which has achieved SOTA performance on many t... View More
Google Approaches BERT-Level Performance Using 300x fewer Parameters with Extension of Its New NLP model PRADO
The trimmed-down pQRNN extension to Google AI’s projection attention neural network PRADO compares to BERT on text classification tasks for on-device use.
Machine Learning and AI shared a link
In this work, researchers train a learned optimizer using a larger and more diverse set of training tasks, better optimization techniques, an improved optimizer architecture, and more compute than pre... View More
