Book Image

Deep Learning with PyTorch

By : Vishnu Subramanian
Book Image

Deep Learning with PyTorch

By: Vishnu Subramanian

Overview of this book

Deep learning powers the most intelligent systems in the world, such as Google Voice, Siri, and Alexa. Advancements in powerful hardware, such as GPUs, software frameworks such as PyTorch, Keras, TensorFlow, and CNTK along with the availability of big data have made it easier to implement solutions to problems in the areas of text, vision, and advanced analytics. This book will get you up and running with one of the most cutting-edge deep learning libraries—PyTorch. PyTorch is grabbing the attention of deep learning researchers and data science professionals due to its accessibility, efficiency and being more native to Python way of development. You'll start off by installing PyTorch, then quickly move on to learn various fundamental blocks that power modern deep learning. You will also learn how to use CNN, RNN, LSTM and other networks to solve real-world problems. This book explains the concepts of various state-of-the-art deep learning architectures, such as ResNet, DenseNet, Inception, and Seq2Seq, without diving deep into the math behind them. You will also learn about GPU computing during the course of the book. You will see how to train a model with PyTorch and dive into complex neural networks such as generative networks for producing text and images. By the end of the book, you'll be able to implement deep learning applications in PyTorch with ease.
Table of Contents (11 chapters)

LSTM

RNNs are quite popular in building real-world applications like language translation, text classification and many more sequential problems, but in reality, we rarely would use a vanilla version of RNN which we saw in the previous section. The vanilla version of RNN has problems like vanishing gradients and gradient explosion when dealing with large sequences. In most of the real-world problems, variants of RNN such as LSTM or GRU are used, which solve the limitations of plain RNN and also have the ability to handle sequential data better. We will try to understand what happens in LSTM, and build a network based on LSTM to solve the text classification problem on the IMDB datasets.

Long-term dependency

RNNs, in theory...