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

• Learn PyTorch for implementing cutting-edge deep learning algorithms.
• Train your neural networks for higher speed and flexibility and learn how to implement them in various scenarios;
• Cover various advanced neural network architecture such as ResNet, Inception, DenseNet and more with practical examples;

• 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

This book is for machine learning engineers, data analysts, data scientists interested in deep learning and are looking to explore implementing advanced algorithms in PyTorch. Some knowledge of machine learning is helpful but not a mandatory need. Working knowledge of Python programming is expected.