learning a neural network from the inside out
In this 5-min read I’ll try to explain how convergence can be slower for LSTM neural networks where memory state is maintained from one mini-batch to the next. An example of this problem can be found in my project about a microsleep detector
Binary classification using EEG readings (part 3 of 3)
It’s much more costly to the end user for the autonomous vehcile to not take over the steering when eyes are closed, than to take over steering when eyes are open.
Binary classification using EEG readings (part 2 of 3)
Autonomous vehicles are no longer reserved for the realm of science fiction, but some AI experts say the complexities of AI-driven cars in public make the possibility of never needing to drive a far off future. Perhaps to bridge the gap are technologies that greatly reduce the labour involved in driving, while still reducing the risk.
Customising an LSTM model to better understand Attention in Sequence to Sequence text prediction
Explaining the concept of ‘Attention’ in Natural Language Processing Models by removing part of the memory function of a Recurrent Neural Network Encoder-Decoder
learning a neural network from the inside out
