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)

Installing PyTorch

PyTorch is available as a Python package and you can either use pip, or conda, to build it or you can build it from source. The recommended approach for this book is to use the Anaconda Python 3 distribution. To install Anaconda, please refer to the Anaconda official documentation at https://conda.io/docs/user-guide/install/index.html. All the examples will be available as Jupyter Notebooks in the book's GitHub repository. I would strongly recommend you use Jupyter Notebook, since it allows you to experiment interactively. If you already have Anaconda Python installed, then you can proceed with the following steps for PyTorch installation.

For GPU-based installation with Cuda 8:

conda install pytorch torchvision cuda80 -c soumith

For GPU-based installation with Cuda 7.5:

conda install pytorch torchvision -c soumith

For non-GPU-based installation:

conda...