- Deep Learning with PyTorch
- Vishnu Subramanian
- 159字
- 2021-06-24 19:16:26
Summary
In this chapter, we explored various data structures and operations provided by PyTorch. We implemented several components, using the fundamental blocks of PyTorch. For our data preparation, we created the tensors used by our algorithm. Our network architecture was a model for learning to predict average hours spent by users on our Wondermovies platform. We used the loss function to check the standard of our model and used the optimize function to adjust the learnable parameters of our model to make it perform better.
We also looked at how PyTorch makes it easier to create data pipelines by abstracting away several complexities that would require us to parallelize and augment data.
In the next chapter, we will dive deep into how neural networks and deep learning algorithms work. We will explore various PyTorch built-in modules for building network architectures, loss functions, and optimizations. We will also show how to use them on real-world datasets.
- 24小時(shí)學(xué)會(huì)電腦組裝與維護(hù)
- 深入理解Spring Cloud與實(shí)戰(zhàn)
- 基于Proteus和Keil的C51程序設(shè)計(jì)項(xiàng)目教程(第2版):理論、仿真、實(shí)踐相融合
- INSTANT Wijmo Widgets How-to
- 嵌入式系統(tǒng)設(shè)計(jì)教程
- INSTANT ForgedUI Starter
- 微軟互聯(lián)網(wǎng)信息服務(wù)(IIS)最佳實(shí)踐 (微軟技術(shù)開發(fā)者叢書)
- Spring Cloud微服務(wù)架構(gòu)實(shí)戰(zhàn)
- RISC-V處理器與片上系統(tǒng)設(shè)計(jì):基于FPGA與云平臺(tái)的實(shí)驗(yàn)教程
- Java Deep Learning Cookbook
- 微服務(wù)實(shí)戰(zhàn)
- 計(jì)算機(jī)組裝、維護(hù)與維修項(xiàng)目教程
- Learning Less.js
- 筆記本電腦現(xiàn)場(chǎng)維修實(shí)錄
- 從企業(yè)級(jí)開發(fā)到云原生微服務(wù):Spring Boot實(shí)戰(zhàn)