Deep Learning with PyTorch: A practical approach to building neural network models using PyTorch

Deep Learning with PyTorch: A practical approach to building neural network models using PyTorchReviews
Author: Vishnu Subramanian
Pub Date: 2018
ISBN: 978-1788624336
Pages: 262
Language: English
Format: PDF/EPUB
Size: 44 Mb


Build neural network models in text, vision and advanced analytics using PyTorch
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.
What You Will Learn

  • Use PyTorch for GPU-accelerated tensor computations
  • Build custom datasets and data loaders for images and test the models using torchvision and torchtext
  • Build an image classifier by implementing CNN architectures using PyTorch
  • Build systems that do text classification and language modeling using RNN, LSTM, and GRU
  • Learn advanced CNN architectures such as ResNet, Inception, Densenet, and learn how to use them for transfer learning
  • Learn how to mix multiple models for a powerful ensemble model
  • Generate new images using GAN’s and generate artistic images using style transfer