Early Stopping is to stop the Training of Neural Networks at the Right Time or Stop training when a monitored quantity has stopped improving. A major concern with training neural networks is in the choice of the number of training epochs to use. Too many epochs can lead to overfitting of the training dataset, whereas … Continue reading Early Stopping in Neural Networks
I am listing down some of the very basic things that will help you to progress in your machine learning journey.. 1. Get a hold over the language to be used for implementation – practice the basics till you are comfortable. 2. Practice and become comfortable with data cleaning and processing – this is essential. … Continue reading How to learn Data Science
" The two key ideas of deep learning for computer vision — convolutional neural networks and backpropagation were already well understood in 1989. The Long Short Term Memory (LSTM) algorithm, which is fundamental to deep learning for timeseries, was developed in 1997 and has barely changed since. So why did deep learning only take off … Continue reading Why deep learning? Why now?
We know that machine learning is about mapping inputs (such as images) to targets (such as the label “cat”), which is done by observing many examples of input and targets. We also know that deep neural networks do this input-to-target mapping via a deep sequence of simple data transformations (layers) and that these data transformations … Continue reading Understanding how deep learning works: