Image Analysis and Text Classification using CNNs in PyTorch
MP4 | Video: AVC 1920x1080 | Audio: AAC 48KHz 2ch | Duration: 1 Hour | 1.28 GB
Genre: eLearning | Language: English

This video teaches you how to build a powerful image classifier in just minutes using convolutional neural networks and PyTorch. It reviews the fundamental concepts of convolution and image analysis; shows you how to create a simple convolutional neural network (CNN) with PyTorch; and demonstrates how using transfer learning with a deep CNN to train on image datasets can generate state-of-the-art performance. The course is designed for the software engineer looking to get started with deep learning and for the AI researcher with TensorFlow or Theano experience who wants a smooth transition into PyTorch. Prerequisites include an understanding of algebra, basic calculus, and basic Python skills. Learners should download and install PyTorch before starting class.

Learn how to build a powerful image classifier in minutes using PyTorch
Explore the basics of convolution and how to apply them to image recognition tasks
Learn how to do transfer learning in conjunction with powerful pretrained models
Gain experience using powerful deep learning models for image recognition tasks

 

Image_Analysis_and_Text_Classification_using_CNNs_in_PyTorch.part1.rar - 350.0 MB
Image_Analysis_and_Text_Classification_using_CNNs_in_PyTorch.part2.rar - 350.0 MB
Image_Analysis_and_Text_Classification_using_CNNs_in_PyTorch.part3.rar - 350.0 MB
Image_Analysis_and_Text_Classification_using_CNNs_in_PyTorch.part4.rar - 264.0 MB