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You're reading from  Modern Computer Vision with PyTorch

Product typeBook
Published inNov 2020
Reading LevelBeginner
PublisherPackt
ISBN-139781839213472
Edition1st Edition
Languages
Python
Tools
PyTorch
Concepts
Computer Vision
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Authors (2):
V Kishore Ayyadevara
V Kishore Ayyadevara
author image
V Kishore Ayyadevara

V Kishore Ayyadevara leads a team focused on using AI to solve problems in the healthcare space. He has 10 years' experience in data science, solving problems to improve customer experience in leading technology companies. In his current role, he is responsible for developing a variety of cutting edge analytical solutions that have an impact at scale while building strong technical teams. Prior to this, Kishore authored three books — Pro Machine Learning Algorithms, Hands-on Machine Learning with Google Cloud Platform, and SciPy Recipes. Kishore is an active learner with keen interest in identifying problems that can be solved using data, simplifying the complexity and in transferring techniques across domains to achieve quantifiable results.
Read more about V Kishore Ayyadevara

Yeshwanth Reddy
Yeshwanth Reddy
author image
Yeshwanth Reddy

Yeshwanth is a highly accomplished data scientist manager with 9+ years of experience in deep learning and document analysis. He has made significant contributions to the field, including building software for end-to-end document digitization, resulting in substantial cost savings. Yeshwanth's expertise extends to developing modules in OCR, word detection, and synthetic document generation. His groundbreaking work has been recognized through multiple patents. He also created a few Python libraries. With a passion for disrupting unsupervised and self-supervised learning, Yeshwanth is dedicated to reducing reliance on manual annotation and driving innovative solutions in the field of data science.
Read more about Yeshwanth Reddy

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Chapter 3 - Building a Deep Neural Network with PyTorch

  1. What is the issue if the input values are not scaled in the input dataset?
    It takes longer to adjust weights to optimal value because input values vary so widely when they are unscaled
  2. What could be the issue if the background has a white pixel color while the content has a black pixel color when training a neural network?
    The neural network has to learn to ignore a majority of the not so useful content that is white in color
  3. What is the impact of batch size on the model's training time, accuracy over a given number of epochs?
    The larger the batch size more is the time taken to converge and more iterations required to attain a high accuracy
  4. What is the impact of the input value range on weight distribution at the end of the training?
    If the input value is not scaled to a certain range, certain weights can aid in over-fitting
  5. How does batch normalization help in improving accuracy?
    Just like how it is important that we scale...
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Modern Computer Vision with PyTorch
Published in: Nov 2020Publisher: PacktISBN-13: 9781839213472
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Authors (2)

author image
V Kishore Ayyadevara

V Kishore Ayyadevara leads a team focused on using AI to solve problems in the healthcare space. He has 10 years' experience in data science, solving problems to improve customer experience in leading technology companies. In his current role, he is responsible for developing a variety of cutting edge analytical solutions that have an impact at scale while building strong technical teams. Prior to this, Kishore authored three books — Pro Machine Learning Algorithms, Hands-on Machine Learning with Google Cloud Platform, and SciPy Recipes. Kishore is an active learner with keen interest in identifying problems that can be solved using data, simplifying the complexity and in transferring techniques across domains to achieve quantifiable results.
Read more about V Kishore Ayyadevara

author image
Yeshwanth Reddy

Yeshwanth is a highly accomplished data scientist manager with 9+ years of experience in deep learning and document analysis. He has made significant contributions to the field, including building software for end-to-end document digitization, resulting in substantial cost savings. Yeshwanth's expertise extends to developing modules in OCR, word detection, and synthetic document generation. His groundbreaking work has been recognized through multiple patents. He also created a few Python libraries. With a passion for disrupting unsupervised and self-supervised learning, Yeshwanth is dedicated to reducing reliance on manual annotation and driving innovative solutions in the field of data science.
Read more about Yeshwanth Reddy

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