Validation datasets for model selection
There is no real utility in taking the position that you won’t do model selection via validation splits, although, know there is a meta-purpose to bringing a blowtorch to this concept…
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.
Binary classification using EEG readings (part 1 of 3)
Produce a detector that distinguisihes when a car’s driver has eyes closed. This detector will connect to an autopilot system for the car temporarily until driver regains control, otherwise safely brings the car to a halt on the side of the road. Detector will have access to EEG signals connected to drivers head.
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
