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Python Programming for a RNN

Recurrent Neural Networks Tutorial, Part 1 – Introduction to RNNs – [] Recurrent Neural Networks (RNNs) are popular models that have shown great promise in many NLP tasks. But despite their recent popularity I’ve only found a limited number of resources that throughly explain how RNNs work, and how to implement them. That’s what this […]

Recurrent Neural Networks

Neural network software uses layers of computational cells that supply feedback across the layers to adjust coefficient values and “train” the network to provide a desired result. Once the network has been trained, it can be supplied input data that is not predetermined and it will output a result that offers some form of analysis […]

Low Power Voice Interface Chip

Voice interaction with computing devices has been growing more sophisticated and more common. Power requirements have been a major obstacle. A new low power speech recognition chip design is about to change that. If the power savings suggested for this chip are actually realized, it could open up the potential for voice interfaces in everything […]

Visual Cortex in USB Form Factor

Housed in a USB key device, the Fathom contains an embedded neural network that can accelerate deep learning with low power requirements. It uses the dedicated Myriad 2 Vision Processing Unit (VPU) chip to provide visual cortex functions in stand alone environments without requiring cloud connectivity. Movidius Announces Deep Learning Accelerator and Fathom Software Framework […]

Fixing Brains With Engineered Neural Tissue

Micro Tissue Engineered Neural Networks (micro-TENNs) are preformed neural network structures which can serve as a foundation for reconstruction of damaged brain pathways. These engineered tissues are being injected into the brains of lab rats for testing. If the neural network tissue is “trained” with a specific knowledge set before being injected, it might become […]

AI Chess Machine Learns by Itself

Deep learning researchers are combining neural networks with the computing power of GPUs to reduce the training time required to produce good results. But this just accelerates the speed of “brute force” learning that tries everything and tests for success or failure. Human intelligence uses filters to eliminate choices that make no sense. Narrowing the […]