Book Image

Mastering PyTorch

By : Ashish Ranjan Jha
Book Image

Mastering PyTorch

By: Ashish Ranjan Jha

Overview of this book

Deep learning is driving the AI revolution, and PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch book will help you uncover expert techniques to get the most out of your data and build complex neural network models. The book starts with a quick overview of PyTorch and explores using convolutional neural network (CNN) architectures for image classification. You'll then work with recurrent neural network (RNN) architectures and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation using generative models and explore the world of generative adversarial networks (GANs). You'll not only build and train your own deep reinforcement learning models in PyTorch but also deploy PyTorch models to production using expert tips and techniques. Finally, you'll get to grips with training large models efficiently in a distributed manner, searching neural architectures effectively with AutoML, and rapidly prototyping models using PyTorch and fast.ai. By the end of this PyTorch book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.
Table of Contents (20 chapters)
1
Section 1: PyTorch Overview
4
Section 2: Working with Advanced Neural Network Architectures
8
Section 3: Generative Models and Deep Reinforcement Learning
13
Section 4: PyTorch in Production Systems

Why are CNNs so powerful?

CNNs are among the most powerful machine learning models at solving challenging problems such as image classification, object detection, object segmentation, video processing, natural language processing, and speech recognition. Their success is attributed to various factors, such as the following:

  • Weight sharing: This makes CNNs parameter-efficient, that is, different features are extracted using the same set of weights or parameters. Features are the high-level representations of input data that the model generates with its parameters.
  • Automatic feature extraction: Multiple feature extraction stages help a CNN to automatically learn feature representations in a dataset.
  • Hierarchical learning: The multi-layered CNN structure helps CNNs to learn low-, mid-, and high-level features.
  • The ability to explore both spatial and temporal correlations in the data, such as in video processing tasks.

Besides these pre-existing fundamental characteristics...