Category Archives: PyTorch

we need to create a matrix of one embedding for each word in the training dataset. We can do that by enumerating all unique words in the Tokenizer.word_index and locating the embedding weight vector from the loaded GloVe embedding.

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.

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.