In this blog, we are going to see Generative adversarial networks (GAN). A generative adversarial network is a class of machine learning frameworks used for training generative models. Generative models create new data instances that resemble the training data. Given a training set, a GAN learns to generate new data with the same statistics as the training set. GANs much depend on the training loss of the model, the model tries to minimize loss to generate as real images as possible. Table of content 1) What is GAN and How it works? 2) What is Conditional GAN? 3) Advantages of cGAN 4) Pictorial explanation 5) Use-cases 1) What is GAN and How it works? GAN is a generative model which achieves a high level of realism by pairing a generator with a discriminator. The generator learns to produce the target output, while the discriminator learns to distinguish true data from the output o
PyTorch is the fastest growing deep learning framework and it is also used by many top fortune companies like Tesla, Apple, Qualcomm, Facebook, and many more. It integrates many algorithms, methods, and classes into a single line of code to ease your day. PyTorch has many optimizer classes such as AdaDelta, Adam, and SGD to name a few. The optimizer takes the parameters we want to update, the learning rate we want to use and optimizers update weights through its step() method. In this blog, we are going to see 13 such optimizers which are supported by the PyTorch framework. Table of contents TORCH.OPTIM AdaDelta Class AdaGrad Class Adam Class AdamW Class SparseAdam Class Adamax Class LBFGS Class RMSprop Class Rprop Class SGD Class ASGD Class NAadam Class RAdam Class Conclusion TORCH.OPTIM torch.optim is a PyTorch package containing various optimization algorithms. Most commonly used methods for optimizers are already supported