why one model isn’t enough ?when you’re working in ml or dl, you quickly develop an obsession: boosting performance. you refine your features…1d ago1d ago
is your data lying to you? how to detect hidden non-linearityhey data folks! are you sure your data is linear? think again…3d ago23d ago2
merge your datasets like a bossheeey data folks ! if you’ve ever worked with data tables, you’ve probably encountered…4d ago4d ago
data leakage in AI : understanding, detecting and fixingdata leakage occurs when a model accesses information it should not see during training. this skews its performance, making it overly…Feb 15Feb 15
loss functions in ml and dl, part 1 : huber loss, quantile loss, tweedie loss, log-cosh lossin regression models, the choice of the loss function directly influences the stability and accuracy of predictions. a poor selection can…Feb 111Feb 111
activation functions, part 3 : linear and softplus for regression output layer in dlin deep learning, choosing the right activation function for regression is essential. the linear activation is the standard choice as itFeb 10Feb 10
reinforcement learning, part 1: introductionreinforcement learning (rl) is the third type of machine learning, allowing an agent to learn how to make optimal decisions through…Feb 9Feb 9
what is white noise in stationary time series ? part 1: introductionwhite noise is unique among time series because it is completely random and lacks any structure. unlike other time series that may exhibit…Feb 9Feb 9
stationary and non-stationary time series, part 1 : introductiontime series are at the heart of quantitative finance. whether it is for asset pricing, risk modeling, market anomaly detection, or the…Feb 8Feb 8
