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- Autors: Pradeepta Mishra
- Izdevējs Apress
- ISBN-10 : 1484289242
- ISBN-13 : 9781484289242
- Formāts: 17.8 x 25.4 x 1.6 cm, minkšti viršeliai
- Valoda: Anglų
- Extra -10% atlaide, ievadot kodu: EXTRA
Atsauksmes
Apraksts
Chapter 1: Introduction to PyTorch, Tensors, and Tensor Operations
Chapter Goal: This chapter is to understand what is PyTorch and its basic building blocks.
Chapter 2: Probability Distributions Using PyTorch
Chapter Goal: This chapter aims at covering different distributions compatible with PyTorch for data analysis.
Chapter 3: Neural Networks Using PyTorch
Chapter Goal: This chapter explains the use of PyTorch to develop a neural network model and optimize the model.
Chapter 4: Deep Learning (CNN and RNN) Using PyTorch
Chapter Goal: This chapter explains the use of PyTorch to train deep neural networks for complex datasets.
Chapter 5: Language Modeling Using PyTorch
Chapter Goal: In this chapter, we are going to use torch text for natural language processing, pre-processing, and feature engineering.
Chapter 6: Supervised Learning Using PyTorch
Goal: This chapter explains how supervised learning algorithms implementation with PyTorch.
Chapter 7: Fine Tuning Deep Learning Models using PyTorch
Goal: This chapter explains how to Fine Tuning Deep Learning Models using the PyTorch framework.
Chapter 8: Distributed PyTorch Modeling
Chapter Goal: This chapter explains the use of parallel processing using the PyTorch framework.
Chapter 9: Model Optimization Using Quantization Methods
Chapter Goal: This chapter explains the use of quantization methods to optimize the PyTorch models and hyperparameter tuning with ray tune.
Chapter 10: Deploying PyTorch Models in Production
Chapter Goal: In this chapter we are going to use torch serve, to deploy the PyTorch models into production.
Chapter 11: PyTorch for Audio
Chapter Goal: In this chapter torch audio will be used for audio resampling, data augmentation, features extractions, model training, and pipeline development.
Chapter 12: PyTorch for Image
Chapter Goal: This chapter aims at using Torchvision for image transformations, pre-processing, feature engineering, and model training.
Chapter 13: Model Explainability using Captum
Chapter Goal: In this chapter, we are going to use the captum library for model interpretability to explain the model as if you are explaining the model to a 5-year-old.
Chapter 14: Scikit Learn Model compatibility using Skorch
Chapter Goal: In this chapter, we are going to use skorch which is a high-level library for PyTorch that provides full sci-kit learn compatibility.
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- Autors: Pradeepta Mishra
- Izdevējs Apress
- ISBN-10: 1484289242
- ISBN-13: 9781484289242
- Formāts 17.8 x 25.4 x 1.6 cm, minkšti viršeliai
- Valoda: Anglų
Chapter 1: Introduction to PyTorch, Tensors, and Tensor Operations
Chapter Goal: This chapter is to understand what is PyTorch and its basic building blocks.
Chapter 2: Probability Distributions Using PyTorch
Chapter Goal: This chapter aims at covering different distributions compatible with PyTorch for data analysis.
Chapter 3: Neural Networks Using PyTorch
Chapter Goal: This chapter explains the use of PyTorch to develop a neural network model and optimize the model.
Chapter 4: Deep Learning (CNN and RNN) Using PyTorch
Chapter Goal: This chapter explains the use of PyTorch to train deep neural networks for complex datasets.
Chapter 5: Language Modeling Using PyTorch
Chapter Goal: In this chapter, we are going to use torch text for natural language processing, pre-processing, and feature engineering.
Chapter 6: Supervised Learning Using PyTorch
Goal: This chapter explains how supervised learning algorithms implementation with PyTorch.
Chapter 7: Fine Tuning Deep Learning Models using PyTorch
Goal: This chapter explains how to Fine Tuning Deep Learning Models using the PyTorch framework.
Chapter 8: Distributed PyTorch Modeling
Chapter Goal: This chapter explains the use of parallel processing using the PyTorch framework.
Chapter 9: Model Optimization Using Quantization Methods
Chapter Goal: This chapter explains the use of quantization methods to optimize the PyTorch models and hyperparameter tuning with ray tune.
Chapter 10: Deploying PyTorch Models in Production
Chapter Goal: In this chapter we are going to use torch serve, to deploy the PyTorch models into production.
Chapter 11: PyTorch for Audio
Chapter Goal: In this chapter torch audio will be used for audio resampling, data augmentation, features extractions, model training, and pipeline development.
Chapter 12: PyTorch for Image
Chapter Goal: This chapter aims at using Torchvision for image transformations, pre-processing, feature engineering, and model training.
Chapter 13: Model Explainability using Captum
Chapter Goal: In this chapter, we are going to use the captum library for model interpretability to explain the model as if you are explaining the model to a 5-year-old.
Chapter 14: Scikit Learn Model compatibility using Skorch
Chapter Goal: In this chapter, we are going to use skorch which is a high-level library for PyTorch that provides full sci-kit learn compatibility.
Atsauksmes