The first time I tried to debug a misbehaving neural network, I spent hours staring at accuracy numbers that looked reasonable but hid a fundamentally broken model. What I needed was a way to actually measure how confident the model…
Optimization is one of the critical processes in deep learning that helps in tuning the parameters of a model to minimize the loss function. Adam optimizer is one of the widely used optimization algorithms in deep learning that combines the…
Autoregressive models (AR models) are a class of statistical models that can be used to analyze time-series data, where the current value of a variable is predicted based on its past values. These models are commonly used in a variety…
Adaboost, short for Adaptive Boosting, is a machine learning algorithm that has gained widespread popularity due to its high accuracy and efficiency. It is a type of ensemble learning method that combines multiple weak classifiers to create a strong classifier.…
Before delving into the topic, let’s know more about what “normalization” is. When the features in the data have different ranges, normalization is an approach used during data processing, to adjust the values of numeric columns in a dataset to…