DeepLearningwithPyTorch_GuideforBeginner.epub - (EPUB全文下载)

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Deep Learning with PyTorch
Guide for Beginners and Intermediate
 
 
 
 
 
By Jerry N. P.
 
 
 
How to contact us
If you find in this book any editing issues, damage or other issues, please immediately let me know by email at:
gloria.kemer@gmail.com
 
Our goal is to provide high-quality books for your learning in the computer science subjects.
 
Thank you so much for purchasing this book.
 
Copyright © 2019 by Jerry N. P.
The information provided in this book is for educational and entertainment purposes only. The reader is responsible for his or her own actions and the author does not accept any responsibilities for any liabilities or damages, real or perceived, resulting from the use of this information.
Tags: pytorch deep learning, python programming, python, python data science handbook, neural network python, tensorflow python, tensorflow for deep learning, python code programming.
 
Table of content
Introduction
Chapter 1
- Why PyTorch for Deep Learning?
Chapter 2
- Getting Started with PyTorch
Computational Graphs
Tensors
Autograd in PyTorch
Chapter 3
- Building a Neural Network
The Neural Network Class
Training
Testing
Chapter 4
- Loading and Processing Data
Dataset Class
Transforms
Composing the Transforms
Looping through the Dataset
Using torchvision
Chapter 5
- Convolutional Neural Networks
Loading the Dataset
Building the Model
Training the Model
Model Testing
Chapter 6
- Transfer Learning
Loading the Data
Visualizing some Images
Training the Model
Visualizing Model Predictions
Fine Tune the ConvNet
Training and Evaluation
Feature Extraction
Training and Evaluation
Chapter 7
- Developing Distributed Applications
Point-Point Communication
Collective Communication
Distributed Training
Chapter 8
- Word Embeddings
N-Gram Language Modeling
Computing Word Embeddings
Chapter 9
- Moving a Model from PyTorch to Caffe2
Using the Model on Mobile Devices
Chapter 10
- Custom C Extensions
Create C Functions
Add it to Python Code
Chapter 11
- Neural Transfer with PyTorch
Cuda
Loading Images
Displaying Images
Content Loss
Style Loss
Loading the Neural Network
Input Image
Gradient Descent
Conclusion
Introduction
A lot of data is generated by businesses every day. This data is rich and when analyzed properly, we can gain insights that are of great importance. Deep learning is a branch of machine learning through which we can extract such insights from data. Deep learning involves the creation of neural networks to process data.
These norma ............

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