Ebook: PyTorch Pocket Reference: Building and Deploying Deep Learning Models
Author: Joe Papa
- Genre: Computers // Cybernetics: Artificial Intelligence
- Tags: Neural Networks, Deep Learning, Natural Language Processing, Python, Generative Adversarial Networks, Transfer Learning, Sentiment Analysis, Docker, Optimization, Flask, PyTorch, Image Classification, Loss Functions, Google Colaboratory, TensorBoard, Tensor Calculus, Data Preparation, Model Deployment, TorchServe, Torchtext
- Year: 2021
- Publisher: O'Reilly Media
- City: Sebastopol, CA
- Edition: 1
- Language: English
- pdf
This concise, easy-to-use reference puts one of the most popular frameworks for deep learning research and development at your fingertips. Author Joe Papa provides instant access to syntax, design patterns, and code examples to accelerate your development and reduce the time you spend searching for answers.
Research scientists, machine learning engineers, and software developers will find clear, structured PyTorch code that covers every step of neural network development-from loading data to customizing training loops to model optimization and GPU/TPU acceleration. Quickly learn how to deploy your code to production using AWS, Google Cloud, or Azure and deploy your ML models to mobile and edge devices.
• Learn basic PyTorch syntax and design patterns
• Create custom models and data transforms
• Train and deploy models using a GPU and TPU
• Train and test a deep learning classifier
• Accelerate training using optimization and distributed training
• Access useful PyTorch libraries and the PyTorch ecosystem
Research scientists, machine learning engineers, and software developers will find clear, structured PyTorch code that covers every step of neural network development-from loading data to customizing training loops to model optimization and GPU/TPU acceleration. Quickly learn how to deploy your code to production using AWS, Google Cloud, or Azure and deploy your ML models to mobile and edge devices.
• Learn basic PyTorch syntax and design patterns
• Create custom models and data transforms
• Train and deploy models using a GPU and TPU
• Train and test a deep learning classifier
• Accelerate training using optimization and distributed training
• Access useful PyTorch libraries and the PyTorch ecosystem
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