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PyTorch Essential Training: Deep Learning

Offered By: LinkedIn Learning

Tags

PyTorch Courses Deep Learning Courses Neural Networks Courses Linear Algebra Courses Data Preparation Courses GPU Computing Courses Tensors Courses Model Training Courses Autograd Courses

Course Description

Overview

Explore the basics of deep learning using PyTorch and test your knowledge with hands-on challenges.

Syllabus

Introduction
  • Deep learning with PyTorch
  • What you should know
  • Tour of CoderPad
1. PyTorch Overview and Introduction to Google Colaboratory
  • Introduction to deep learning
  • Why should you use PyTorch
  • Google Colaboratory basics
2. Tensors
  • Introduction to tensors
  • Creating a tensor CPU example
  • Creating a tensor GPU example
  • Moving tensors between CPUs and GPUs
3. Creating Tensors
  • Different ways to create tensors
  • Tensor attributes
  • Tensor data types
  • Creating tensors from random samples
  • Creating tensors like other tensors
  • Solution: Create tensors
4. Manipulate Tensors
  • Tensor operations
  • Mathematical functions
  • Linear algebra operations
  • Automatic differentiation (Autograd)
  • Solution: Split tensors to form new tensors
5. Developing a Deep Learning Model
  • Introduction to the DL training process
  • Data preparation
  • Data loading
  • Data transforms
  • Data batching
  • Model development and training
  • Validation and testing
Conclusion
  • Next steps

Taught by

Jonathan Fernandes

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