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[FreeCoursesOnline.Me] Linkedin - PyTorch Essential Training Deep Learning

[FreeCoursesOnline.Me] Linkedin - PyTorch Essential Training Deep Learning

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Description
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Author : Jonathan Fernandes
Language : English
Released : October 3, 2019
Duration : 56m 3s
Skill Level : Intermediate
Course Source : https://www.linkedin.com/learning/pytorch-essential-training-deep-learning

Description :

PyTorch is quickly becoming one of the most popular deep learning frameworks around, as well as a must-have skill in your artificial intelligence tool kit. It's gained admiration from industry leaders due to its deep integration with Python; its integration with top cloud platforms, including Amazon SageMaker and Google Cloud Platform; and its computational graphs that can be defined on the fly. In this course, join Jonathan Fernandes as he dives into the basics of deep learning using PyTorch. Starting with a working image recognition model, he shows how the different components fit and work in tandem—from tensors, loss functions, and autograd all the way to troubleshooting a PyTorch network.

Instructor :

Jonathan Fernandes

Data Scientist and Machine Learning/AI consultant at DXC Technology

What they do

• Software Developer
• Research Fellow
• Technology Manager

Where they work

• IBM
• Accenture
• US Army
• Tata Consultancy Services
• Infosys

Skills covered in this course

• Machine Learning
• Python (Programming Language)




File list
  • [FreeCoursesOnline.Me] Linkedin - PyTorch Essential Training Deep Learning
  • 0. Websites you may like/How you can help Team-FTU.txt 237 B
  • 01 - Introduction/01 - Welcome.mp4 9.4 MB
  • 01 - Introduction/01 - Welcome.srt 1.6 KB
  • 01 - Introduction/02 - What you should know before watching this course.mp4 3.8 MB
  • 01 - Introduction/02 - What you should know before watching this course.srt 1.4 KB
  • 02 - Fashion MNIST and Neural Networks/01 - Working with the Fashion MNIST dataset.mp4 33.2 MB
  • 02 - Fashion MNIST and Neural Networks/01 - Working with the Fashion MNIST dataset.srt 5.9 KB
  • 02 - Fashion MNIST and Neural Networks/02 - Neural network intuition.mp4 10.6 MB
  • 02 - Fashion MNIST and Neural Networks/02 - Neural network intuition.srt 8.9 KB
  • 03 - Working with Classes and Tensors/01 - Classes.mp4 48.9 MB
  • 03 - Working with Classes and Tensors/01 - Classes.srt 6.2 KB
  • 03 - Working with Classes and Tensors/02 - Tensors.mp4 7.2 MB
  • 03 - Working with Classes and Tensors/02 - Tensors.srt 5.8 KB
  • 03 - Working with Classes and Tensors/03 - Training the network.mp4 5.7 MB
  • 03 - Working with Classes and Tensors/03 - Training the network.srt 5.5 KB
  • 04 - Working with Loss, Autograd, and Optimizers/01 - Loss.mp4 41.4 MB
  • 04 - Working with Loss, Autograd, and Optimizers/01 - Loss.srt 8.3 KB
  • 04 - Working with Loss, Autograd, and Optimizers/02 - Autograd.mp4 10.8 MB
  • 04 - Working with Loss, Autograd, and Optimizers/02 - Autograd.srt 7.1 KB
  • 04 - Working with Loss, Autograd, and Optimizers/03 - Autograd with tensors.mp4 8.9 MB
  • 04 - Working with Loss, Autograd, and Optimizers/03 - Autograd with tensors.srt 5.9 KB
  • 04 - Working with Loss, Autograd, and Optimizers/04 - Optimizers.mp4 2.9 MB
  • 04 - Working with Loss, Autograd, and Optimizers/04 - Optimizers.srt 3.5 KB
  • 04 - Working with Loss, Autograd, and Optimizers/05 - Using optimizers.mp4 10 MB
  • 04 - Working with Loss, Autograd, and Optimizers/05 - Using optimizers.srt 5.4 KB
  • 05 - Troubleshooting and CPUGPU Usage/01 - Troubleshooting.mp4 11.5 MB
  • 05 - Troubleshooting and CPUGPU Usage/01 - Troubleshooting.srt 7.6 KB
  • 05 - Troubleshooting and CPUGPU Usage/02 - CPU to GPU.mp4 14.8 MB
  • 05 - Troubleshooting and CPUGPU Usage/02 - CPU to GPU.srt 9.1 KB
  • 05 - Troubleshooting and CPUGPU Usage/03 - Validation.mp4 7.8 MB
  • 05 - Troubleshooting and CPUGPU Usage/03 - Validation.srt 4.9 KB
  • 06 - Conclusion/01 - Future project ideas.mp4 14.4 MB
  • 06 - Conclusion/01 - Future project ideas.srt 3 KB
  • Exercise Files/Ex_Files_PyTorch_EssT_DL.zip 51.8 KB

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