Transformer architecture explained with minimal PyTorch implementation line-by-line.
The Variational Autoencoder (VAE) is a probabilistic approach that extends the conventional autoencoder framework by introducing stochasticity in encoding and decoding processes, thus enabling the modeling of complex distributions of data. Originally derived from the principles of Variational Bayesian methods, the VAE framework was associated with autoencoders primarily because certain terms in its objective function could be interpreted analogously to the functions of an encoder and a decoder.
Generative Adversarial Networks(GAN) has become one of the most powerful techniques in machine learning since its emergence in 2014. The interesting idea of training methods and the flexible design of the objective function make GAN have numerous variants.
In this blog post, we will explore the concept of distributed training and delve into the details of PyTorch’s DistributedDataParallel training approach. Some Prerequisite Definitions Process: A process is the basic unit of work in an operating system. In the usual context of DDP, you can put that one process control one GPU.
