we'll dig down and try to understand what's making our deep networks hard to train. When we look closely, we'll discover that the different layers in our deep network are learning at vastly different speeds. In particular, when later layers in the network are learning...
. At this rate, the change blindness effect will have disappeared entirely in a decade, like so much of the rest of psychology, wherein the size of effects notoriously decline over time (likely due to a bias against common-sense for publication reasons). In comparison, modern research ...
Ensemble models are produced by training several similar models and combining their results to improve accuracy, reduce bias, reduce variance and identify the best model to use with new data. Gradient boosting. This is a boosting approach that resamples your data set several times to generate ...
shirts to facebook users in the run-up to the us presidential election. meriem mahdhi algorithms policed welfare systems for years. now they're under fire for bias human rights groups have launched a new legal challenge against the use of algorithms to detect error and fraud in france's ...
Data bias:To get accurate results from an AI model, training requires quality data. To mitigate data bias, data scientists must vet data sources thoroughly before curating training data sets. The right data:Training data sets requires heavy volumes of data that represent appropriate diversity and ...
If a feature (e.g. another stock or a technical indicator) has no explanatory power to the stock we want to predict, then there is no need for us to use it in the training of the neural nets. We will using XGBoost (eXtreme Gradient Boosting), a type of boosted tree regression ...
After the Axial Age, the West moved toward continuous disunity, but China had successfully maintained a persistent unity pattern. Conventional case (history event) studies are subject to selection bias and theoretical frameworks, which is not objective n
Neural networksare sophisticated techniques capable of modeling extremely complex relationships. They’re popular because they’re powerful and flexible. The power comes in their ability to handle nonlinear relationships in data, which is increasingly common as we collect more data. They are often used...
It can work well in continuous action spaces, which is suitable in our use case and can learn (through mean and standard deviation) the distribution probabilities (if softmax is added as an output). The problem of policy gradient methods is that they are extremely sensitive to the step size...
We choose to go to the moon in this decade and do the other things, not because they are easy, but because they are hard, because that goal will serve to organize and measure the best of our energies and skills, because that challenge is one that we are