Linkedin | PyTorch Essential Training: Deep Learning [FCO]

  • Category Other
  • Type Tutorials
  • Language English
  • Total size 241.4 MB
  • Uploaded By SunRiseZone
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  • Last checked 5 days ago
  • Date uploaded 6 years ago
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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)



Files:

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01 - Introduction
  • 01 - Welcome.mp4 (9.4 MB)
  • 01 - Welcome.srt (1.6 KB)
  • 02 - What you should know before watching this course.mp4 (3.8 MB)
  • 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)
  • 01 - Working with the Fashion MNIST dataset.srt (5.9 KB)
  • 02 - Neural network intuition.mp4 (10.6 MB)
  • 02 - Neural network intuition.srt (8.9 KB)
03 - Working with Classes and Tensors
  • 01 - Classes.mp4 (48.9 MB)
  • 01 - Classes.srt (6.2 KB)
  • 02 - Tensors.mp4 (7.2 MB)
  • 02 - Tensors.srt (5.8 KB)
  • 03 - Training the network.mp4 (5.7 MB)
  • 03 - Training the network.srt (5.5 KB)
04 - Working with Loss, Autograd, and Optimizers
  • 01 - Loss.mp4 (41.4 MB)
  • 01 - Loss.srt (8.3 KB)
  • 02 - Autograd.mp4 (10.8 MB)
  • 02 - Autograd.srt (7.1 KB)
  • 03 - Autograd with tensors.mp4 (8.9 MB)
  • 03 - Autograd with tensors.srt (5.9 KB)
  • 04 - Optimizers.mp4 (2.9 MB)
  • 04 - Optimizers.srt (3.5 KB)
  • 05 - Using optimizers.mp4 (10.0 MB)
  • 05 - Using optimizers.srt (5.4 KB)
05 - Troubleshooting and CPUGPU Usage
  • 01 - Troubleshooting.mp4 (11.5 MB)
  • 01 - Troubleshooting.srt (7.6 KB)
  • 02 - CPU to GPU.mp4 (14.8 MB)
  • 02 - CPU to GPU.srt (9.1 KB)
  • 03 - Validation.mp4 (7.8 MB)
  • 03 - Validation.srt (4.9 KB)
06 - Conclusion
  • 01 - Future project ideas.mp4 (14.4 MB)
  • 01 - Future project ideas.srt (3.0 KB)
Exercise Files
  • Ex_Files_PyTorch_EssT_DL.zip (51.8 KB)

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