Attention: Sequence to Sequence Model: Input sequence is provided and output sequence is derived from that input. Encoder and Decoder: The model encodes a particular input provided by us into something that we call as context vector that is passed to the decoder after the encoding which is then decoded by the help of the decoder. Now we can always use a big decoder i.e., the output from all the hidden states but then we have performance issues and the chances of overfitting.
Deep Convolutional GAN Implementing a Deep Convolutional GAN where we are trying to generate house numbers which are supposed to look as realistic as possible.The DCGAN architecture was first explored in 2016 and has seen impressive results in generating new images; you can read the original paper, here import matplotlib.pyplot as plt import numpy as np import pickle as pkl import torch from torchvision import datasets from torchvision import transforms import torch.
Catch up with RNNs and key differences Now if we recall, then we could generalize that RNNs generate one word at a time similarly they also generate one pixel at a time for images. Whereas GANs help to generate a whole image in parallel. It uses a generator-discriminator Network model. The generator model takes random noise and runs it through a differentiable function to transform/reshape it to a more realistic image.
Protocol Based Data Model: - So, we have Protocol oriented data model functions in Python. When we look at object orientation in python, we have 3 core features to look into: The protocol model of python The built-in inheritance protocols Some caviars around how object orientation works Few protocols that comes in real handy when we use object orientation (aka Magic Methods/Dunder(double underscored methods): -