Pytorch Image Augmentation using Transforms.
The quality of the images will not be the same from each source. Some images might be of very high quality while others might be just plain bad. In such scenarios, we can blur the image. This helps make our deep learning model more robust. Transforms provide a class for randomly change the brightness, contrast, and saturation of an image.
Convert PASCAL dataset to TFRecord for object detection in TensorFlow
Once you are done annotating your image dataset in the Pascal VOC format, you must convert your data into the TFRecord format. Because to use your own dataset in TensorFlow Object Detection API, you must convert it into the TFRecord file format.
Change the Learning Rate using Schedules API in Keras
The intuition behind slowly reducing the learning rate is that maybe during the initial steps of learning you can afford to take much bigger steps but then as learning approaches convergence then having a slower learning rate allows you to take smaller steps.
Convolutional Neural Network using Sequential model in PyTorch.
How easy this looks compared to when we had to construct a model through the class way of doing it. Sequential class lives in the neural network package and this is a class that we are building by or we’re building an instance of this class by passing in other modules in a sequential.
Detect and Remove Outliers from Pandas DataFrame
Z-score re-scale and center(Normalize) the data and look for data points which are too far from zero(center). Data points far from zero will be treated as the outliers. In most of the cases, a threshold of 3 or -3 is used i.e if the Z-score value is greater than or less than 3 or -3 respectively, that data point will be identified as outliers.